text
stringlengths
4.45k
138k
summary
stringlengths
32
3.35k
You are an expert at summarizing long articles. Proceed to summarize the following text: Development is often strongly regulated by interactions among close relatives , but the underlying molecular mechanisms are largely unknown . In eusocial insects , interactions between caregiving worker nurses and larvae regulate larval development and resultant adult phenotypes . Here , we begin to characterize the social interactome regulating ant larval development by collecting and sequencing the transcriptomes of interacting nurses and larvae across time . We find that the majority of nurse and larval transcriptomes exhibit parallel expression dynamics across larval development . We leverage this widespread nurse-larva gene co-expression to infer putative social gene regulatory networks acting between nurses and larvae . Genes with the strongest inferred social effects tend to be peripheral elements of within-tissue regulatory networks and are often known to encode secreted proteins . This includes interesting candidates such as the nurse-expressed giant-lens , which may influence larval epidermal growth factor signaling , a pathway known to influence various aspects of insect development . Finally , we find that genes with the strongest signatures of social regulation tend to experience relaxed selective constraint and are evolutionarily young . Overall , our study provides a first glimpse into the molecular and evolutionary features of the social mechanisms that regulate all aspects of social life . Social interactions play a prominent role in the lives of nearly all organisms [1] and strongly affect trait expression as well as fitness [2–4] . Social interactions in the context of development ( e . g . parental care ) often strongly regulate developmental trajectories and resultant adult phenotypes , for example via transferred compounds such as milk in mammals [5 , 6] , milk-like secretions in arthropods [7 , 8] , and other forms of nutritional provisioning [9 , 10] . In many taxa including certain birds , mammals , and insects , care for offspring and the regulation of offspring development has shifted at least in part from parents to adult siblings , who perform alloparental care [11] . In eusocial insect societies , sterile nurse workers regulate the development of their larval siblings by modulating the quantity and quality of nourishment larvae receive [12–14] , as well as through the direct transfer of growth-regulating hormones and proteins [15 , 16] . At the same time , larvae influence nurse provisioning behavior via pheromones [17–20] and begging behavior [21 , 22] . In general , traits such as caregiving behavior that are defined or influenced by social interactions are the property of the genomes of multiple interacting social partners [2 , 14] . This has implications for both the mechanistic ( e . g . , molecular ) underpinnings of development and trait expression as well as the genetic basis of trait variation at the population level—i . e . how allelic variation in the genomes of interacting social partners affects trait variation [2 , 14] . Furthermore , because social traits are expressed in one individual but impact the fitness of other individuals , social behavior and socially-influenced traits experience distinct forms of selection , including kin selection and social selection [23 , 24] . Altogether , these distinct genetic features and patterns of selection are often thought to lead to distinct evolutionary features , such as rapid evolutionary dynamics in comparison to other traits [25–27] . In eusocial insects , previous studies show that variation in larval developmental trajectories and ultimate adult phenotypes ( including reproductive caste , body size , etc . ) depends on the combination of larval and nurse genotypes [28–34] . However , the identity of specific genes and molecular pathways that are functionally involved in the expression of social interactions ( e . g . , genes underlying nurse and larval traits affecting nurse-larva interactions ) and the patterns of molecular evolution for these genes have remained less well studied [15 , 16 , 35 , 36] . Transcriptomic studies are often used to identify sets of genes underlying the expression of particular traits by performing RNA-sequencing on individuals that vary in the expression of such traits . For example , in social insects , recent studies have compared the transcriptomes of workers that perform nursing versus foraging tasks [37–39] , or nurses feeding larvae of different stages or castes [35 , 40] . However , given the phenotypic co-regulation known to occur between interacting social partners ( here , nurses and larvae ) , it is likely that genes expressed in one social partner affect the expression of genes in the other social partner , and vice-versa , such that interacting social partners are connected by “social” gene regulatory networks [14 , 32 , 41 , 42] . Thus , identifying the genes important for social interactions such as nurse-larva interactions is only possible by studying the transcriptomic dynamics of both interacting social partners across a time series of interactions . To understand the transcriptomic basis of host-symbiont interactions , recent studies have reconstructed gene regulatory networks acting between hosts and symbionts by collecting and profiling the transcriptomes of each social partner across a time series of interactions [43–47] . Here , we use analogous methodology to study transcriptomic signatures of nurse-larva interactions in the pharaoh ant , Monomorium pharaonis . We sample a developmental time series of larvae as well as the nurses that feed each larval stage in this series , collecting individuals at the moment of interaction in order to identify genes involved in the expression of nurse-larva interactions , as well as genes affected by these interactions ( i . e . the full “social interactome” [14] ) . Pharaoh ant nurses tend to specialize on feeding young versus old larvae , and nurses feeding young versus old larvae show different transcriptomic profiles [40] . Larval transcriptomic profiles also change over development [48 , 49] . Given these results , we predicted that we would observe concerted changes in broad-scale gene expression in larvae and their nurses across larval development ( Fig 1 ) , reflective of the functional importance of nurse-larva interactions . Based on our dual RNA-seq data , we infer social gene regulatory networks acting between nurses and larvae to identify candidate genes predicted to have important social regulatory effects . Finally , we combine our measures of social regulatory effects with available population genomic data [48] to characterize the patterns of molecular evolution of genes underlying nurse-larva interactions . To elucidate transcriptomic signatures of nurse-larva interactions , we performed RNA-sequencing on worker-destined larvae across five developmental stages and nurses that fed larvae of each developmental stage ( termed “stage-specific” nurses; see S1 Fig for sampling scheme , S1 Table for list of samples ) , building upon a previously published dataset focused on caste development in M . pharaonis [48] . We hypothesized that if genes expressed in larvae regulate the expression of genes in nurse and vice versa , we would observe correlated expression profiles across larval development in larvae and nurses ( Fig 1 ) . As a biological control , we collected “random nurses” that we observed feeding any stage of larvae in the colony , and hence would not be expected to show correlated expression dynamics with larvae across the five larval developmental stages . We also collected reproductive-destined larvae , but unless clearly stated otherwise , all analyses were performed on only worker-destined larvae . We collected ten individuals of each sample type to pool into one sample , and we sequenced whole bodies of larvae but separated nurse heads and abdomens prior to sequencing . We grouped genes into co-expression profiles or “modules” using an algorithm designed to characterize gene co-expression dynamics across a short time series [50] , known as Short Time-Series Expression Mining ( STEM ) [51] . Each module represents a standardized pre-defined expression profile , consisting of five values that each represent the log2 fold-change between the given developmental stage and the initial ( L1 ) stage ( see S2 Fig; this results in a total of 81 possible modules ) . We sorted genes into the module that most closely represented their expression profile by Pearson correlation . We identified modules containing a greater than expected number of genes , where we formed null expectations using permutation tests across developmental stages [50] . We identified such significantly-enriched modules separately for larvae , stage-specific nurse heads , stage-specific nurse abdomens , random nurse heads , and random nurse abdomens . We focused on both parallel ( i . e . positive regulation or activation ) and anti-parallel ( i . e . inhibitory ) correlated expression patterns by identifying significantly-enriched modules that were shared in both larvae and nurses ( parallel ) , as well as significantly-enriched modules for which the inverse of the module was identified as significantly-enriched in the social partner ( anti-parallel ) . Larvae and stage-specific nurses shared many significantly-enriched modules ( S2 Table ) . These shared modules contained the majority of genes expressed in nurses ( 65% of genes in stage-specific nurse heads and 76% in abdomens ) . A substantial proportion of the larval transcriptome was also shared with stage-specific nurse heads ( 22% of larval genes ) and abdomens ( 60% of larval genes ) . Overall there was a widespread signature of correlated transcriptional patterns between stage-specific nurses and larvae across larval development ( Fig 2A–2D ) . These coordinated dynamics were dominated by parallel associations in nurse abdomens ( possibly reflecting shared metabolic pathways ) but anti-parallel associations in nurse heads ( possibly reflecting the social regulation of larval growth ) . In contrast to stage-specific nurses , random nurses ( our biological control ) shared few significantly-enriched modules with larvae ( S2 Table ) , and modules shared between random nurses and larvae contained significantly fewer genes than modules shared between stage-specific nurses and larvae ( Fig 2E; Wilcoxon test , P < 0 . 001 for all comparisons ) . Specifically , 2% of genes expressed in random nurse heads and 13% of genes expressed in random nurse abdomens were in modules shared with larvae; 3% of genes expressed in larvae were in modules shared with random nurse heads , and 2% of genes expressed in larvae were in modules shared with random nurse abdomens . Given that we observed transcriptome-wide patterns consistent with nurse-larva transcriptional co-regulation across larval development , we next identified the genes that might be driving these patterns ( see S3 Fig ) . We performed differential expression analysis to identify genes that varied in larval expression according to larval developmental stage , as well as genes that varied in nurse expression according to the developmental stage of larvae they fed . We identified 8125 differentially expressed genes ( DEGs ) in larvae ( 78% of 10446 total genes ) . We identified 2057 and 1408 DEGs in stage-specific nurse heads and abdomens , respectively , compared to 599 and 520 DEGs in random nurse heads and abdomens , respectively . We removed genes differentially expressed in both stage-specific and random nurses ( N = 272 DEGs in heads , N = 140 DEGs in abdomens ) , which might differ among our colony replicates due to random colony-specific effects that were not consistently associated with social regulation of larval development . After this removal , we retained the top 1000 DEGs , sorted by P-value , for each sample type other than random nurses ( larvae , stage-specific nurse heads , stage-specific nurse abdomens ) for social gene regulatory network reconstruction , reasoning that these genes were the most likely to be involved in the regulation of larval development . To infer putative gene-by-gene social regulatory relationships between nurses and larvae , we reconstructed gene regulatory networks acting within and between nurses and larvae ( S3 Fig ) . The output of regulatory network reconstruction is a matrix of connection strengths , which indicate the regulatory effect ( positive or negative ) one gene has on another , separated according to the tissue the gene is expressed in . To identify the most highly connected ( i . e . centrally located , upstream ) genes of regulatory networks , we calculated within-tissue connectivity and social connectivity by averaging the strength of connections across each connection a gene made , differentiating between within-tissue ( nurse-nurse or larva-larva ) and social connections ( nurse-larva ) ( Fig 1B ) . On average , within-tissue connectivity was higher than social connectivity ( Wilcoxon test; P < 0 . 001 in all tissues ) , and within-tissue connectivity was negatively correlated with social connectivity in each tissue ( S4 Fig ) . The top enriched gene ontology terms based on social connectivity in nurses were entirely dominated by metabolism ( S3 and S4 Tables; see also S5 Table for the top 20 genes by nurse social connectivity ) . While based on our data it is not possible to distinguish between genes that code for protein products that are actually exchanged between nurses and larvae versus genes that affect behavior or physiology within organisms ( Fig 1A ) , proteins that are known to be cellularly secreted represent promising candidates for the social regulation of larval development [40] . We downloaded the list of proteins that are known to be cellularly secreted from FlyBase [52] and used a previously-generated orthology map to identify ant orthologs of secreted proteins [40] . Genes coding for proteins with orthologs that are cellularly secreted in Drosophila melanogaster had higher social connectivity than genes coding for non-secreted orthologs in nurse heads ( Fig 3A; Wilcoxon test; P = 0 . 025 ) , though not for nurse abdomens ( P = 0 . 067 ) . For the most part , we have focused on broad patterns of nurse-larva gene coregulation . In this paragraph , we will highlight the potential social role of one of the genes with the highest social connectivity within nurse heads , giant-lens ( S6 Table; giant-lens is the 7th highest gene coding for secreted proteins by social connectivity in nurse heads ) . Giant-lens is an inhibitor of epidermal growth factor receptor ( EGFR ) signaling [53] , and giant-lens expression in nurse heads was negatively associated with the expression of the homolog of eps8 , human EGFR substrate 8 in larvae , most prominently seen in the spike in nurse giant-lens expression accompanied by a drop in larval eps8 expression at the end of larval development ( Fig 3B ) . Giant-lens was also used in regulatory network reconstruction in larvae ( i . e . it was one of the top 1000 DEGs ) , and giant-lens expression in larvae drops steadily throughout development ( S5 Fig; in contrast to the pattern of giant-lens expression in nurse heads ) . Interestingly , eps8 does not exhibit a similar peak and drop in expression level in reproductive-destined larvae in comparison to worker-destined larvae ( S6 Fig ) . It is important to note that these patterns were not seen for all genes in the EGFR pathway , and the results presented here cannot be taken as concrete evidence of EGFR regulation via social processes . Nonetheless , the mechanism illustrated here represents a tangible example of how nurse-larva interactions could function at the molecular level . To investigate the selective pressures shaping social regulatory networks , we used population genomic data from 22 resequenced M . pharaonis workers , using one sequenced M . chinense worker as an outgroup [48] . Using polymorphism and divergence data , we estimated gene-specific values of selective constraint , which represents the intensity of purifying selection that genes experience [54] . To identify genes disproportionately recruited to the core of social regulatory networks , we calculated “sociality index” as the difference between social connectivity and within-tissue connectivity for each gene . Sociality index was negatively correlated to selective constraint due to a positive correlation between within-tissue connectivity and constraint and a negative correlation between social connectivity and constraint ( Fig 4A–4C ) . Additionally , genes differed in sociality index according to their estimated evolutionary age , with ancient genes exhibiting lower sociality indices than genes in younger age categories ( Fig 4D ) . Finally , while evolutionary age and evolutionary rate appear to be somewhat empirically confounded [55] , selective constraint and evolutionary age were each independently associated with sociality index , based on a model including both variables as well as tissue ( GLM; LRT; evolutionary age: χ2 = 21 . 536 , P < 0 . 001; selective constraint: χ2 = 22 . 191 , P < 0 . 001 ) . In organisms with extended offspring care , developmental programs are controlled in part by socially-acting gene regulatory networks that operate between caregivers and developing offspring [14 , 42] . In this study , we sequenced the transcriptomes of ant nurses and larvae as they interacted across larval development to assess the effects of social interactions on gene expression dynamics . We found that large sets of genes ( i . e . modules ) expressed in ant larvae and their caregiving adult nurses show correlated changes in expression across development ( Fig 2 ) . The majority of nurse and larval transcriptomes was represented in these correlated modules , suggesting that the tight phenotypic co-regulation characterizing nurse-larva interactions over the course of larval development is also reflected at the molecular level . To characterize the overall network and evolutionary patterns of genes involved in nurse-larva interactions , we reverse engineered nurse-larva gene regulatory networks and calculated the “social connectivity” for each gene , defined as the sum of inferred social regulatory effects on all genes expressed in social partners . We found that genes with high social connectivity tended to have low within-individual connectivity ( S4 Fig; where within-individual connectivity is defined as the sum of inferred regulatory effects acting within a given tissue ) . Nurse-expressed genes with higher sociality indices ( i . e disproportionately higher social connectivity than within-individual connectivity ) tended to be evolutionarily young and rapidly evolving due to relaxed selective constraint ( Fig 4 ) . Genes with high social connectivity were enriched for a number of Gene Ontology ( GO ) categories associated with metabolism ( S3 and S4 Tables ) , consistent with the idea that molecular pathways associated with metabolism are involved in the expression of social behavior [56 , 57] . Previously , many of the proteins found to be widely present in social insect trophallactic fluid transferred from nurses to larvae were involved in sugar metabolism ( e . g . Glucose Dehydrogenase , several types of sugar processing proteins ) [15] . Along the same lines , many of the genes with with high social connectivity in our study are also annotated with terms associated with sugar metabolism ( S5 Table; e . g . Glycerol-3-phosphate dehydrogenase , Glucose dehydrogenase FAD quinone , Pyruvate dehydrogenase ) . Finally , we found that genes encoding for orthologs of cellularly-secreted proteins in Drosophila melanogaster ( possibly important for intercellular signaling ) tended to exhibit higher levels of social connectivity than their non-secreted counterparts ( Fig 3A ) . One gene that stands out in terms of being cellularly secreted and exhibiting a relatively high social connectivity is giant-lens , which inhibits EGFR signaling [53] . EGFR signaling affects eye and wing development [58] as well as body size in D . melanogaster [59] , caste development in the honey bee Apis mellifera [59 , 60] via the transfer of royalactin from nurses to larvae [59] , and worker body size variation in the ant Camponotus floridanus [61] . Further experimental work is necessary to ascertain whether giant-lens is actually orally secreted by nurses and transferred to larvae , but gene expression dynamics are consistent with the social transfer of giant-lens from nurses to larvae , followed by the inhibition of EGFR signaling at the end of larval development in worker-destined larvae ( Fig 3B ) . Importantly , this inhibition is not seen in reproductive-destined larvae ( S6 Fig ) . While caste in M . pharaonis is socially regulated in the first larval stage [49] , social inhibition of EGFR signaling could play a role in the regulation of worker body size [61] or secondary caste phenotypes such as wings [62 , 63] . In terms of broad evolutionary patterns , our study complements previous results suggesting genes with worker-biased expression tend to be rapidly evolving , evolutionarily young , and loosely connected in regulatory networks in comparison to genes with queen-biased expression [38 , 48 , 64–66] . Because pharaoh ant workers are obligately sterile , their traits are shaped indirectly by kin selection , based on how they affect the reproductive success of fertile relatives ( i . e . queens and males ) [23 , 67] . As a result , all-else-equal , genes associated with worker traits are expected to evolve under relaxed selection relative to genes associated with queen traits [68 , 69] . In general , the suite of genic characteristics commonly associated with worker-biased genes ( rapidly evolving , evolutionarily young , loosely connected ) are all consistent with relaxed selection acting on genes associated with workers [49] . Here , we show that within the worker caste , genes that appear to be functionally involved in the expression of social behavior ( i . e . nursing ) experience relaxed selective constraint relative to genes important for within-worker processes . Therefore , the combination of kin selection as well rapid evolution thought to be characteristic of social traits [25] likely act in concert to shape the labile evolutionary patterns commonly associated with worker-biased genes . Finally , it has also been suggested that plastic phenotypes such as caste recruit genes which were evolving under relaxed selection prior to the evolution of such plastic phenotypes [70–72] . Our results could also be consistent with this hypothesis , though the population genomic patterns we observe show that relaxed selective constraint is ongoing . In this study , we sought to reconstruct regulatory networks acting between nurses and larvae , beginning with the assumption that nurse gene expression changes as a function of the larval stage fed . This is more likely to be the case when nurses are specialized on feeding particular larval stages . According to a previous study , about 50% of feeding events are performed by specialists ( though note specialization is likely a continuous trait , and the 50% figure is the result of a binomial test ) [40] . Therefore , we expect our stage-specific nurse samples to comprise about 50% specialists . We also expect random nurse samples to contain 50% specialist nurses , but , crucially , the specialists should be relatively evenly divided among larval stages since random nurses were collected regardless of which larval stage they were observed feeding . Because our stage-specific nurse samples did not consist of 100% specialists , we expect that the signal of nurse-larva co-expression in our analysis is effectively diluted . In order to maximize the signal of nurse-larval co-expression dynamics , future studies would ideally focus entirely on specialists , as well as on tissues such as brains and the specific exocrine glands [73] known to be important for social behavior and communication . Despite these limitations , we were still able to observe transcriptomic signatures consistent with the social regulation of larval development . In this study , we uncovered putative transcriptomic signatures of social regulation and identified distinct evolutionary features of genes that underlie “social physiology” , the communication between individuals that regulates division of labor within social insect colonies [74 , 75] . Because we simultaneously collected nurses and larvae over a time series of interactions , we were able to elucidate the putative molecular underpinnings of nurse-larval social interactions . This is a promising approach that could be readily extended to study the molecular underpinnings of all forms of social regulation in social insect colonies , including regulation of foraging , regulation of reproduction , etc . . Furthermore , by adapting the methodology presented here ( i . e . simultaneous collection over the course of interactions followed by sequencing ) , the molecular mechanisms and evolutionary features of genes underlying a diverse array of social interactions , including courtship behavior , dominance hierarchy formation , and regulation of biofilm production could all be investigated . Overall , this study provides a foundation upon which future research can build to elucidate the genetic underpinnings and evolution of interacting phenotypes . To construct experimental colonies , we began by creating a homogenous mixture of approximately fifteen large source colonies of the ant Monomorium pharaonis . From this mixture , we created thirty total replicate experimental colonies of approximately equal sizes ( ~300–400 workers , ~300–400 larvae ) . We removed queens from ½ the study colonies to promote the production of reproductive-destined larvae . Reproductive caste is determined in M . pharaonis by the end of the first larval instar , likely in the egg stage [76] , and queen presence promotes culling of reproductive-destined L1 larvae . Removing queens halts this culling , but it is unknown which colony members actually perform such culling [76] . While we initially expected the presence of queens to impact the gene expression profiles of nurses , we detected 0 DEGs ( FDR < 0 . 1 ) between queen-present and queen-absent colonies for every sample type . This could indicate that nurses don’t perform culling and that worker developmental trajectories ( and nutritional needs ) are not appreciably different between queen-present and queen-absent colonies . Because queen presence did not substantially impact gene expression , in this study we pooled samples across queen-present and queen-absent colonies for all analyses . We pre-assigned colonies to one of five larval developmental stages ( labeled L1-L5 , where L1 and L2 refer to 1st-instar and 2nd-instar larvae and L3 , L4 , and L5 refer to small , medium , and large 3rd-instar larvae [77] ) . We identified larval stage through a combination of hair morphology and body size . L1 larvae are nearly hairless , L2 larvae have straight hairs and are twice the length of L1 larvae , and L3-L5 larvae have dense , branched hairs [78] . We separated 3rd-instar larvae into three separate stages based on body size [77] because the vast majority of larval growth occurs during these stages . We sampled individuals ( larvae as well as nurses ) across larval development time: beginning at the L1 stage , we sampled colonies assigned to each subsequent stage at intervals of 3–4 days , by the time the youngest larvae in colonies lacking queens were of the assigned developmental stage ( note that in colonies lacking queens , no new eggs are laid so the age class of the youngest individuals progressively ages ) . We sampled each colony once , according to the developmental stage we had previously assigned the colony ( e . g . for colonies that we labeled ‘L4’ , we waited until it was time to sample L4 larvae and nurses and sampled individuals from that colony at that time ) . From each colony , we sampled stage-specific nurses and worker-destined larvae , as well as random nurses from colonies with queens and reproductive-destined larvae from colonies without queens ( starting at the L2 stage , because at L1 caste cannot be distinguished [76 , 77] . Reproductive-destined larvae include both males and queens ( which cannot be readily distinguished ) , though samples are expected to be largely made up of queen-destined individuals given the typically skewed sex ratio of M . pharaonis [48] . See S1 Table for full sample list . For each time point in each assigned colony , we collected stage-specific nurses , nurses feeding larvae of the specified developmental stage ( L1 , L2 , etc ) . Concurrently , we collected random nurses , nurses we observed feeding a larva of any developmental stage . Rather than paint-marking nurses , we collected them with forceps as soon as we saw them feeding larvae . We collected random nurses as soon as we observed them feeding a larva of any developmental stage in the course of visually scanning the colony . We did not make an attempt to systematically collect nurses from different areas of the nest but did so haphazardly , such that the distribution of larval stages fed resembled overall colony demography . Nurses feed L1 and L2 larvae exclusively via trophallaxis ( i . e . liquid exchange of fluid ) , while nurses feed L3-L5 larvae both via trophallaxis and by placing solid food in larval mouthparts [79] . To get a representative sample of all types of nurses , we did not distinguish between nurses feeding liquid and solid food , though all L3-L5 samples contained a mixture of the two . After collecting nurses , we anaesthetized the colony using carbon dioxide and collected larvae of the specified developmental stage . All samples were flash-frozen in liquid nitrogen immediately upon sample collection . Note that workers in M . pharaonis are monomorphic [80] . We performed mRNA-sequencing on all samples concurrently using Illumina HiSeq 2000 at Okinawa Institute of Science and Technology Sequencing Center . Reads were mapped to the NCBI version 2 . 0 M . pharaonis assembly [38] , and we used RSEM [81] to estimate counts per locus and fragments per kilobase mapped ( FPKM ) for each locus . For further details on RNA extraction and library preparation , see [48] . We used an algorithm that categorizes genes based on their expression dynamics over time into a number of modules represented by pre-defined expression profiles [50]; see S2 Fig for workflow ) . To create modules , we started at 0 and either doubled , halved , or kept the expression level the same at each subsequent stage , resulting in 81 possible modules ( 3*3*3*3 = 81; four stages after L1 ) . To generate gene-specific expression profiles based on real results , we calculated the average log2 fold change in expression ( FPKM ) of the gene at each developmental stage compared to the initial expression level at stage L1 . We then assigned each gene to the closest module by Pearson correlation between gene expression profile and module expression profile [50] . To identify significantly-enriched modules , we generated null distributions of the number of genes present in each module ( based on permutation of expression over time ) , and retained modules with a significantly greater than expected number of genes based on these null distributions ( FDR < 0 . 05 after Bonferroni multiple correction [50] ) . We used the package EdgeR [82] to construct models including larval developmental stage and replicate and performed differential expression analysis for each sample type separately . We retained genes differentially expressed according to a nominal P-value of less than 0 . 05 ( i . e . no false discovery correction ) , as the purpose of this step was simply to identify genes that could be involved in interactions that shape larval development ( rather than spurious interactions arising from replicate-specific effects ) . See S1 Dataset for a list of all stage-specific nurse and larval differentially expressed genes . We normalized expression for each gene using the inverse hyperbolic sine transformation of FPKM . As input to the algorithm , we constructed “meta-samples” by combining expression data within the same replicate and time point from nurses and larvae and labeling genes according to the tissue they were expressed in , along the lines of host-symbiont studies [43 , 45] . We utilized the program GENIE3 [83 , 84] to construct two types of networks: those acting between larvae and nurse heads , and those acting between larvae and nurse abdomens . GENIE3 uses a random forest method to reconstruct regulatory connections between genes , in which a separate random forest model is constructed to predict the expression of each gene , with the expression of all other genes as predictor variables . The output of GENIE3 is a matrix of pairwise directional regulatory effects , where the regulatory effect of gene i on gene j is estimated as the feature importance of the expression of gene i for the random forest model predicting the expression of gene j ( i . e . regulatory effect is how important the expression of gene i is for determining the expression of gene j ) . These regulatory effects ( or strengths ) include both positive and negative as well as non-linear effects , though these different effect types are not distinguished . As a side note , a version of GENIE3 that was developed for time series data , dynGENIE3 [85] , does exist . However , we opted to utilize the original GENIE3 algorithm because we reasoned that the temporal spacing of developmental stages was likely too sparse for regulatory network reconstruction to incorporate time ( note also that the co-expression algorithm we used , STEM , was explicitly designed for short time series such as ours ) . While our method therefore does not explicitly incorporate temporal dynamics , we purposefully biased our results to emphasize larval development over differences between replicates by only utilizing genes differentially expressed across larval development ( or based on larval stage fed in the case of nurses ) . We repeated the entire regulatory reconstruction reconstruction process 1000 times and averaged pairwise connection strengths across runs , as the algorithm is non-deterministic . To capture the total effect of each gene on the transcriptome dynamics within tissues , we averaged the regulatory effects each gene had on all other 999 genes expressed in the same tissue ( “within-individual connectivity” ) . Similarly , to capture the effect each gene had on the transcriptome of social partners , we averaged regulatory effects each gene had on the 1000 genes expressed in social partners ( “social connectivity” ) . Previously , we performed whole-genome resequencing on 22 diploid M . pharaonis workers as well as one diploid M . chinense worker to serve as an outgroup [48] . We estimated selective constraint using MKtest2 . 0 [86] , assuming an equal value of alpha ( an estimate of the proportion of nonsynonymous substitutions fixed by positive selection ) across all genes . Selective constraint is the estimate of the proportion of nonsynonymous mutations that are strongly deleterious and thereby do not contribute to polymorphism or divergence [86] . Selective constraint is estimated using polymorphism data , so it represents the strength of purifying selection genes experience within the study population [54] . Phylostrata are hierarchical taxonomic categories , reflecting the most inclusive taxonomic grouping for which an ortholog of the given gene can be found [87–90] . We focused on distinguishing between genes that were evolutionarily “ancient” , present in non-insect animals , versus genes present in only insects , hymenopterans , or ants [49] . We constructed a database containing 48 hymenopteran available genomes , 10 insect non-hymenopteran genomes , and 10 non-insect animal genomes ( S2 Dataset ) . For outgroup genomes , we focused on well-annotated genomes which spanned as many insect orders and animal phyla as possible . Using this database , we estimated evolutionary age of genes based on the most evolutionarily distant identified BLASTp hit ( E-value 10−10 ) . We performed gene set enrichment analysis based on social connectivity for each gene in each tissue separately using the R package topGO [91] . We identified enriched gene ontology terms using Kolmogorov-Smirnov tests ( P < 0 . 05 ) . We performed all statistical analyses and generated all plots using R version in R version 3 . 4 . 0 [92] , aided by the packages “reshape2” [93] , “plyr” [94] , and “ggplot2” [95] .
Social interactions are fundamental to all forms of life , from single-celled bacteria to complex plants and animals . Despite their obvious importance , little is known about the molecular causes and consequences of social interactions . In this paper , we study the molecular basis of nurse-larva social interactions that regulate larval development in the pharaoh ant Monomorium pharaonis . We infer the effects of social interactions on gene expression from samples of nurses and larvae collected in the act of interaction across a developmental time series . Gene expression appears to be closely tied to these interactions , such that we can identify genes expressed in nurses with putative regulatory effects on larval gene expression . Genes which we infer to have strong social regulatory effects tend to have weak regulatory effects within individuals , and highly social genes tend to experience relatively weaker natural selection in comparison to fewer social genes . This study represents a novel approach and foundation upon which future studies at the intersection of genetics , behavior , and evolution can build .
You are an expert at summarizing long articles. Proceed to summarize the following text: Crimean-Congo hemorrhagic fever virus ( CCHFV ) is a zoonotic agent that causes severe , life-threatening disease , with a case fatality rate of 10–50% . It is the most widespread tick-borne virus in the world , with cases reported in Africa , Asia and Eastern Europe . CCHFV is a genetically diverse virus . Its genetic diversity is often correlated to its geographical origin . Genetic variability of CCHFV was determined within few endemic areas , however limited data is available for Kosovo . Furthermore , there is little information about the spatiotemporal genetic changes of CCHFV in endemic areas . Kosovo is an important endemic area for CCHFV . Cases were reported each year and the case-fatality rate is significantly higher compared to nearby regions . In this study , we wanted to examine the genetic variability of CCHFV obtained directly from CCHF-confirmed patients , hospitalized in Kosovo from 1991 to 2013 . We sequenced partial S segment CCHFV nucleotide sequences from 89 patients . Our results show that several viral variants are present in Kosovo and that the genetic diversity is high in relation to the studied area . We also show that variants are mostly uniformly distributed throughout Kosovo and that limited evolutionary changes have occurred in 22 years . Our results also suggest the presence of a new distinct lineage within the European CCHF phylogenetic clade . Our study provide the largest number of CCHFV nucleotide sequences from patients in 22 year span in one endemic area . Crimean-Congo hemorrhagic fever ( CCHF ) is an acute tick-borne zoonotic disease which is characterized by a fulminant and often hemorrhagic course of disease with the case fatality rate of 10–50% . Causative agent is the Crimean-Congo hemorrhagic fever virus ( CCHFV ) which belongs to the Nairovirus genus in the family Bunyaviridae . CCHF is the most widespread tick-borne disease in the world with cases reported in a number of countries in Africa , Asia , Middle East and southeastern Europe . Geographical distribution is closely linked to the presence of the primary vectors , ticks of the genus Hyalomma [1] . CCHFV genome consists of three single-stranded negative-sense RNA segments: small ( S ) , medium ( M ) and large ( L ) [1] , [2] . Genetic analyses of all three genomic segments have shown that CCHFV exhibits a high level of genetic variability ranging from 20% ( S segment ) , 22% ( L segment ) to 31% ( M segment ) . Genetic variability correlates with the geographical spread of the virus . Namely , phylogenetic analyses of the S segment have shown that geographically separated viral isolates cluster in roughly six clades: two European , three African and one Asian [3] . Genetic variability of CCHFV was also demonstrated within several geographical regions . For example , Ozkaya et al . ( 2010 ) have shown existence of local topotypes of CCHFV in Turkey [4] while Aradaib et al . ( 2011 ) have found the presence of several variants of CCHFV in Sudan [5] . CCHF is endemic in Kosovo . The first reports of CCHF in Kosovo date back to 1957 , when a family outbreak resulting of eight fatal cases , was described [6] . Based on the records of the Institute of Public Health of Kosovo , from 1995 to August 2013 , 228 cases of CCHF have been reported in Kosovo , with the mortality rate of 25 . 5% . There is limited information about CCHFV genetic diversity in Kosovo despite the long presence of CCHFV infections in this area [7] , [8] , [9] . The aim of our study was to investigate the genetic variability of CCHFV from patients in Kosovo in a time span of 22 years in order to determine the spatio-temporal characteristics of CCHFV in this highly endemic area . For the purpose of the study , we included 89 serum samples of Real-Time RT-PCR confirmed CCHF patients from Kosovo , hospitalized from 1991–2013 . Serum samples were periodically received from the National Institute of Public Health of Kosovo , Republic of Kosovo for confirmatory diagnostics and further analyses . Samples were processed as previously described [10] . The study was retrospective therefore we did not obtain additional informed consent from the patients . Instead , the research was approved by the National Medical Ethics Committee of the Republic of Slovenia . We followed the principles of the Helsinki Declaration , the Oviedo Convention on Human Rights and Biomedicine , and the Slovene Code of Medical Deontology . All human samples were anonymized and no additional sample was taken for the purpose of the study . Total RNA from serum samples between years 1991–2009 was extracted using Trizol LS Reagent ( Invitrogen Life Technologies ) according to the manufacturer's instructions . Total RNA from serum samples between years 2010–2013 was extracted using QIAamp Viral RNA Mini Kit ( Qiagen ) according to the manufacturer's instructions . RT-PCR amplification of the complete S segment was performed as described by Deyde et al . [3] . RT-PCR was performed using the SuperScript III One-Step RT-PCR System with Platinum Taq High Fidelity ( Invitrogen Life Technologies ) according to the manufacturer's instructions . Nested PCR was performed using primer pair CCHF SORF-F ( 5′-GCCATGGAAAACAAGATCGAGG-3′ ) and CCHF SORF-R ( 5′-AGTTCTAGATGATGTTGGCAC-3′ ) , yielding a PCR product of 1 , 456 bp which represents the complete coding region of the CCHF N protein . Nested PCR was performed using KOD Xtreme Hot Start DNA Polymerase ( Novagen , EMD4Biosciences ) according to the manufacturer's instructions . Nested PCR cycling conditions were as follows: initial denaturation at 94°C for 2 minutes , followed by 40 cycles of denaturation at 98°C for 10 seconds , primer annealing at 60°C for 30 seconds and elongation at 68°C for 1 minute and 30 seconds . Additionally , a 536 bp fragment ( primers CCHF F2/R3 ) or a 260 bp fragment ( primers CCHF F3/R2 ) of the S segment was amplified as described by Rodriguez et al . [11] if the amplification of the 1 , 456 bp fragment was not successful . Partial M segment nucleotide sequences were obtained as described previously [12] . PCR products were purified with the Wizard SV Gel and PCR Clean-Up System ( Promega ) , sequenced using the BigDye Terminator 3 . 1 Cycle sequencing kit ( Applied Biosystems ) and analyzed with the 3500 Genetic Analyzer ( Applied Biosystems ) . Nucleotide sequences were assembled and edited using CLC Main Workbench software ( CLC bio , Denmark ) . At least two-fold read coverage was obtained for all sequences . Sequences were aligned in MEGA version 5 [13] using Muscle algorithm . Nucleotide sequences were deposited to the GenBank database ( accession numbers KC477779-837 , KF039932-83 , KF595127-49 ) . Nucleotide substitution model was selected based on Akaike's information criterion ( AIC ) in jModelTest , version 0 . 1 . 1 [14] . The general time-reversible model with gamma-distributed rate variation ( GTR+G ) was employed for phylogenetic analyses of the CCHF S segment . Bayesian phylogenetic analyses were performed in MrBayes 3 . 2 [15] and Tracer version 1 . 5 [16] . Four independent Markov Chain Monte Carlo ( MCMC ) runs of four chains each consisting of 10 , 000 , 000 generations were run to ensure effective sample sizes ( ESS ) of at least 1000 . Phylogenetic analysis of the M segment sequences was performed in MEGA5: Molecular Evolutionary Genetics Analysis [17] . The TN92 model with gamma-distributed rate variation was used for the analysis . Maximum clade credibility trees were depicted using FigTree version 1 . 3 . 1 [16] . Evolutionary rates and calculation of the time of the most recent common ancestor ( tMRCA ) were determined for the larger S segment sequences . We estimated the evolutionary rates using a MCMC method implemented in BEAST 1 . 8 . 0 [16] with a relaxed molecular clock ( under the GTR+G+I model of nucleotide substitution ) and a piecewise-constant Bayesian skyline plot as a coalescent prior . Priors were selected according to Zehender et al . [18] . The chains were conducted until reaching ESS>200 and sampled every 10 , 000 steps . Trees were summarized in a maximum clade credibility tree after a 10% burnin using Tree Annotator 1 . 8 . 0 [16] . Mean evolutionary rates and tMRCA were calculated in TreeStat 1 . 8 . 0 [16] . We obtained 37 partial CCHFV S segment sequences ( 1019 bp ) from patients hospitalized in 2002 ( n = 3 ) , 2005 ( n = 1 ) , 2010 ( n = 10 ) , 2012 ( n = 11 ) and 2013 ( n = 12 ) . All sequences clustered in the European CCHF genetic lineage V , along with previously published CCHFV sequences from Kosovo ( Figure 1A ) . Overall identity of the sequences ranged from 98 . 8–100% and we detected three amino acid changes; S272N ( present in samples KS153 and KS149 ) , K316R ( present in samples KS208 , KS213 and KS223 ) and V327I ( present in samples KS172 and KS88 ) ( amino acid positions are numbered relative to the nucleoprotein sequence of CCHFV strain Kosovo Hoti , accession number: AAZ32529 ) . CCHFV sequences clustered in roughly three groups designated A1–A3 ( Figure 1A ) . We estimated a mean evolutionary rate of 2 . 76×10−4 substitutions/site/year and the mean tMRCA for the root of 729 . 4 years ago . We then analyzed a shorter fragment of the S segment ( 389 bp ) , because we had more sequences available . We obtained 79 nucleotide sequences from patients hospitalized in 2001 ( n = 15 ) , 2002 ( n = 8 ) , 2003 ( n = 4 ) , 2004 ( n = 7 ) , 2005 ( n = 3 ) , 2006 ( n = 2 ) , 2010 ( n = 10 ) , 2011 ( n = 6 ) , 2012 ( n = 11 ) and 2013 ( n = 13 ) . Overall identity of the sequences ranged from 98 . 5–100% . All sequences clustered in the European genetic lineage V and were distributed in 5 genetic groups ( A1–A5 ) . The latter phylogenetic analysis was comparable to the previous one , although some resolution was lost . Samples KS-154 and KS-165 , which clustered in group A1 in the previous analysis were miss-assigned to group A3 . The most divergent sequences clustered into group A5 . This cluster was also most divergent compared to other sequences in the European genetic lineage V ( maximum nucleotide distance within the European genetic lineage V was 2 . 9 , that is to the Turkish GQ337053 sequence ) . We additionally obtained 4 partial S segment sequences ( 220 bp ) from patients hospitalized in 1991 ( n = 3 ) and 1992 ( n = 1 ) . These sequences were not included in the previous phylogenetic analysis because they were too short . However , clustering into groups A1–A5 can be distinguished by analysis of mutational profiles of four nucleotide changes: 343T/C , 496C/A , 304C/T or 520A/G and 220T/C or 550T/C ( nucleotide positions are numbered relative to the complete S segment sequence of CCHFV strain Kosovo Hoti , accession number: DQ133507 ) . Thereby we were able to assign two sequences from 1991 to group A2 , while the two other sequences could not be definitely assigned ( sequences could be assigned to either group A3 or A4 ) . In order to further support our findings , we sequenced 431 bp of CCHFV M segment . We obtained 50 partial M segment sequences . Overall identity of the sequences ranged from 95 . 2–100% . In general we observed three distinct phylogenetic groups; A1 , A2 and A5 ( Figure 1C ) . Several sequences could not be assigned to any of the observed groups due to the low resolution of the phylogenetic analysis . Despite several attempts we could not obtain longer M segment sequences from these samples due to low sample volumes and low viral loads . Therefore , we could not obtain a phylogenetic tree with higher resolution . Next , we wanted to determine the geographical distribution of the sequences . Each phylogenetic cluster was plotted on the map of Kosovo with respect to the grouping from the 389 bp S segment phylogenetic analysis . As is seen in Figure 2 sequences are evenly distributed throughout the studied area . The two most abundant phylogenetic groups ( A1 and A2 ) are present in almost all studied municipalities . However , sequences from group A1 are present in southern parts in greater abundance than in the northern parts and vice versa for group A2 . The number of sequences we obtained is comparable to the incidence of CCHF in each municipality . On average we sequenced approximately 50% of total confirmed cases in each municipality . Therefore our results portray a realistic picture of the distribution of viral variants in the endemic area . Sequences from the most divergent phylogenetic group ( A5 ) grouped in two neighboring municipalities in central Kosovo . No obvious ecological or geographical barriers are present in this area which could explain the constrained geographical distribution of the variants . We did not observe any temporal correlation to the phylogenetic clustering . From 2001 to 2010 the two major phylogenetic groups ( A1 and A2 ) occurred in similar abundances . However , significant shifts in abundances of the two groups occurred in the following years . In 2011 , 80% confirmed patients were infected with A1 virus variant ( and 20% with A3 ) . On the contrary , in 2012 we detected the A2 virus variant alone ( we sequenced 92% confirmed CCHF cases ) . In 2013 , again both A1 and A2 variants were present ( 9% and 50% confirmed cases , respectively ) . CCHFV is a genetically diverse virus . It groups into several genetic clades which correlate to the geographic origin to some extent . This correlation is most profoundly seen in the phylogenetic analyses of the viral S segment . The virus groups into seven phylogenetic clades: 2 European , 3 African and 2 Asian [19] . Great genetic diversity of CCHFV has also been shown within each phylogenetic clade in different extents [20] . Several viral variants were detected also within particular endemic areas [4] , [5] , [21] , [22] , [23] , [24] . Furthermore , Ozkaya et al . [4] showed that same viral variants also cluster together geographically . CCHFV is an important causative agent of disease in Kosovo . Due to the high number of CCHF cases in relation to the small size of the endemic area and the long history of CCHF in Kosovo , this area represents an interesting model for studies of viral evolution and genetic variability . The aim of our study was to expand the limited knowledge about the genetic variability of CCHFV in Kosovo . We wanted to obtain partial genome sequences directly from patient serum samples without prior cultivation or cloning in a time span of 22 years . We wanted to determine if there is any geographical clustering of the viral variants and if there were any significant temporal genetic changes . The results of our study revealed that several viral variants are present within the endemic region in Kosovo . Overall nucleotide sequence divergence ( 2% ) is in the scope with previous reports [20] . At least three major phylogenetic groups were formed based on the analysis of a larger portion of the viral S segment . These groups could also be discriminated in the analysis of a smaller S segment fragment . This analysis revealed the presence of 5 distinct phylogenetic clades . Previous report from Turkey described the detection of two genetic variant , or topotypes . Given the fact that the studied area in this report was at least 10 times larger than ours , implies that the overall genetic diversity of CCHFV in Kosovo is very high [4] . This difference can be attributed to several factors . The first is the number of sequenced patients , or rather the proportion of sequenced patients . In our study we sequenced 59% confirmed patients ( a total of 168 confirmed cases from 2001 to 2013 ) , a proportion that is significantly higher than in previous reports . Length of CCHF presence in an endemic area is also important . The first reports of CCHF in Kosovo date back to 1957 , with several sporadic or epidemic years until present . In Turkey however , these reports are scarce and the disease has gained recognition only recently in the last ten years . Our results also suggest that the disease has been present in Kosovo for a long time and that the virus population has been more or less stable during the last 22 years . Variant analysis of nucleotide sequences obtained from patients in years 1991 and 1992 revealed that A2 group has been present throughout the whole period , whilst the existence of A1 group could not be confirmed . We estimated a mean evolutionary rate of 2 . 76×10−4 substitutions/site/year which is in concordance to the estimated evolutionary rate reported in a recent , comprehensive report of whole S segment sequences by Zehender et al . ( 2 . 96×10−4 substitutions/site/year ) [18] . Similarly , we show that the most probable location of the MRCA in Europe was Russia and that the virus was introduced in Kosovo somewhat 50 years ago which coincides with the first reports of the disease in Kosovo in 1957 [25] ( Figure S1 ) . With regard to the temporal changes in virus population we observed changing dynamics of viral variant abundances from 2011 to 2013 . From 2001 to 2011 we steadily detected both major phylogenetic groups ( A1 and A2 ) regardless of the number of cases in each year . However in 2011 we detected only the A1 groups ( out of the two major groups ) and in 2012 we detected only the A2 group . Such a rapid change in relative abundances is somewhat surprising . We could not determine any link with the geographic distribution of the cases nor to any demographic changes in this period . These observations lead us to believe that the underlying cause for the shifts probably lie in the ecology of the disease . There is limited ecological data for Kosovo available , so we could not perform an in-depth analysis . What we have found is that average yearly temperatures in 2010 and 2011 were below average and that average minimum temperatures in 2012 were below average . Data suggest that weather conditions in 2010–2013 changed in relation to previous years . Since climate greatly influences both the vector and the reservoir of the disease , the changing climate patterns could explain the changes in the viral populations . Our results suggest that relative abundances of viral variants are dynamic and are prone to great variations and that ecological factors can play a role in shaping these populations . Of note regarding genetic diversity is also the cluster of three sequences in clade A5 , which is separated from all other sequences present in Kosovo . Furthermore , our results also suggest that this lineage is also significantly different from other sequences in the European CCHFV phylogenetic clade . Spatial analysis of these sequences revealed that all three patients from whom the viral sequences were derived were infected in nearby municipalities , separated no more than 20 km apart . In combination with the temporal analysis it is also evident that the viral variant was present in the area for at least three years . This geographical limitation of the A5 phylogenetic clade is surprising since no obvious ecological and geographical obstacles are present in the area . A greater effort to obtain sequences in this region should be implemented to resolve this issue . Spatial analysis of other phylogenetic clades observed within Kosovo patients did not reveal a clear geographical separation of the major clades . On the other hand , further inspection of the geographical clustering revealed that sequences from the phylogenetic clade A1 clustered more in the southern part of Kosovo , while sequences from clade A2 clustered more in the northern part of Kosovo . Our study provides the first insight into the genetic variability of CCHFV in patients from Kosovo . It provides the largest set of patient derived CCHFV sequences within one geographical area in the span of 22 years . Our results reveal great genetic variability of CCHFV in Kosovo . This diversity is exemplified when we take into account the size of the studied area . Presence of several viral variant and the observed limited evolutionary changes in 22 years suggest that CCHFV has been present in Kosovo for a long time . Our results also suggest that the population of viral variants is prone to significant changes in different endemic years . Further studies are however needed to determine the factors responsible for these changes .
Crimean-Congo hemorrhagic fever ( CCHF ) is an acute , tick-borne disease with a case fatality rate of 10–30% . It is geographically the most widespread tick-borne disease in the world . In recent years there has been an increase of the disease incidence in several countries , mainly in the countries of the Balkan . The disease is also endemic in Kosovo . Since CCHF virus is very genetically diverse we aimed to determine the genetic variability of the virus in Kosovo in the span of 22 years . We obtained the largest number of patient derived nucleotide sequences and found great genetic variability which has been more or less stable during the 22 year period . Our results also suggest that significant changes in viral population occur in different years . We show that ecological factors such as temperature could play a role in the composition of the viral population .
You are an expert at summarizing long articles. Proceed to summarize the following text: Spermatogenesis is a dynamic process that is regulated by adhesive interactions between germ and Sertoli cells . Germ cells express the Junctional Adhesion Molecule-C ( JAM-C , encoded by Jam3 ) , which localizes to germ/Sertoli cell contacts . JAM-C is involved in germ cell polarity and acrosome formation . Using a proteomic approach , we demonstrated that JAM-C interacted with the Golgi reassembly stacking protein of 55 kDa ( GRASP55 , encoded by Gorasp2 ) in developing germ cells . Generation and study of Gorasp2-/- mice revealed that knock-out mice suffered from spermatogenesis defects . Acrosome formation and polarized localization of JAM-C in spermatids were altered in Gorasp2-/- mice . In addition , Golgi morphology of spermatocytes was disturbed in Gorasp2-/- mice . Crystal structures of GRASP55 in complex with JAM-C or JAM-B revealed that GRASP55 interacted via PDZ-mediated interactions with JAMs and induced a conformational change in GRASP55 with respect of its free conformation . An in silico pharmacophore approach identified a chemical compound called Graspin that inhibited PDZ-mediated interactions of GRASP55 with JAMs . Treatment of mice with Graspin hampered the polarized localization of JAM-C in spermatids , induced the premature release of spermatids and affected the Golgi morphology of meiotic spermatocytes . Members of the Junctional Adhesion Molecular family exhibit a similar structure with two extracellular immunoglobulin domains , a single transmembrane region and a C-terminal PSD-95/Discs Large/ZO-1 ( PDZ ) -binding motif . Three of these proteins are highly similar: JAM-A , JAM-B and JAM-C [1] . The latter interacts with JAM-B and the leukocyte integrins αMβ2 and αXβ2 [2 , 3] . Since JAM-B and JAM-C are both expressed by endothelial cells , it has been proposed that their primary function consists in the regulation of inter-endothelial junctional tightness and leukocyte trans-endothelial migration [4] . However , studies of constitutive and conditional knock-out mice for Jam3 ( the gene encoding JAM-C ) revealed an essential function for JAM-C in spermatogenesis [5 , 6] . Spermatogenesis occurs in a stepwise manner , beginning with diploid spermatogonia at the basal surface of seminiferous tubules and ending with mature elongated spermatozoa in tubule lumens which are released at spermiation . Spermatogenesis involves adhesive interactions between developing germ and Sertoli cells [7] and is a continuous process that requires 34 . 5 days in mice . During that time , mitosis , meiosis and maturation occur in spermatogonia , spermatocytes and spermatids , respectively [8 , 9] . Spermatogenesis is a developmental system in which the Golgi apparatus undergoes dramatic rearrangements during the meiotic and post-meiotic phases [10] . Germ cells express JAM-C which participates to spermatogenesis via interaction with JAM-B during post-meiotic maturation of spermatids [6 , 11] . The strong decrease in sperm cells number in Jam3-deficient mice was attributed to the lack of JAM-C recruitment to the junctional plaques at germ/Sertoli cell contacts [6] . Junctional plaques are specialized adhesion structures that anchor germ cells to Sertoli cells and provide spermatids with polarization cues , including JAM-C-mediated polarity signals . The progressive confinement of JAM-C to junctional plaques begins in round spermatids and it is completed in heads of elongated spermatids that remain attached to Sertoli cells via an adhesive structure called apical ectoplasmic specialization [12] . However , little is known about the molecular mechanisms involved in JAM-C polarized localization to spermatids/Sertoli cell contacts . The present study used a combination of proteomic and genetic techniques with structural biochemistry and structure-based drug design approaches to investigate these mechanisms . We demonstrated that GRASP55 interacted with the PDZ-binding motif of JAM-C in testis . GRASP55 is a medial/trans Golgi molecule that is involved in Golgi stacking , Golgi fragmentation during mitosis and the unconventional protein transport triggered by cellular stress [13–18] . The cargo receptor function of GRASP55 was attributed to the interaction of GRASP55 PDZ domains with motifs in the C-terminal part of cargos such as CD8 , TGF-α , or CD83 [19–21] . We solved the 3D structure of GRASP55 in the ligand-free form and in complex with two cargos: JAM-C and JAM-B . The structure revealed a large conformational change between the “open/ligand-free” and “closed/cargo-bound” forms . We used a virtual screening strategy that combined high-throughput docking and pharmacophore filtering to identify protein-protein inhibitors of the GRASP55/JAM interaction [22 , 23] . The best inhibitor , referred to as Graspin for “GRASP55 INhibitor” , exhibited reasonable affinity and selectivity for inhibition of GRASP55/JAMs interaction . The biological relevance of GRASP55/JAM-C interaction in spermatogenesis was validated using genetic ablation of Gorasp2 ( encoding GRASP55 ) and chemical inhibition of GRASP55 PDZ-mediated interactions . We used a proteomic approach to identify molecular mechanisms that regulate the PDZ-dependent functions of JAMs during spermatogenesis . Testes lysates and peptides corresponding to the terminal 19 amino acids ( aa ) of JAMs or mutant sequences that lacked the last three C-terminal aa were used in pulldown assays ( Fig 1A ) . Known PDZ-containing binders of JAMs , such as ZO-1 and ZO-2 and , several new binding partners were identified using mass spectrometry ( MS ) , including the Golgi Reassembly Stacking Protein of 55 kDa ( GRASP55 ) ( Table in S1 Table ) . The MS results indicated that the interaction of GRASP55 with JAMs was likely PDZ-dependent because GRASP55 was not pulled-down with the JAM peptides that lacked PDZ-binding motifs . Yeast two-hybrid interaction assays confirmed that the first PDZ domain of GRASP55 was necessary for interaction with JAM proteins ( Fig 1B ) . Conversely , the PDZ-binding motifs of the JAM sequences were required , as demonstrated in the yeast two-hybrid or peptide pull-down assays that were performed with mutant JAM sequences lacking the three C-terminal aa ( Fig 1B and 1C ) . Measurement of the relative binding of GRASP55 to JAM family members using homogenous time-resolved fluorescence ( HTRF ) or isothermal titration calorimetry ( ITC ) revealed five- to seven-fold higher affinity interactions of GRASP55 with JAM-B and JAM-C ( 4 . 9 μM and 3 . 7 μM , respectively ) as compared to JAM-A ( 27 μM ) ( Fig 1D and 1E; Table in S2 Table ) . Comparable affinities were measured using the full-length GRASP55 protein or isolated tandem PDZ domains ( PDZ12 ) ( Table in S2 Table ) , which supports that the critical residues that contribute to the affinity of GRASP55/JAMs interaction are present within the PDZ tandem domain of GRASP55 . We disrupted the gene encoding GRASP55 , Gorasp2 using homologous recombination to examine the function of GRASP55 in vivo , ( Fig A-C in S1 Fig ) . Gorasp2-deficient mice exhibited growth retardation , similarly to Jam3-deficient mice [24] ( Fig D in S1 Fig ) . Male Gorasp2-/- mice bred normally ( mating behaviors , plug production ) , but these mice were infertile . Therefore , we measured number and size of the litters . We never obtained offspring from Gorasp2-/- males , but Gorasp2-/- females were fertile ( Table 1 ) . Analysis of male reproductive organs isolated from Gorasp2-deficient males revealed no significant differences in the testis/body weight ratio or epididymis and seminal vesicles weights ( Fig E-G in S1 Fig ) . However , we observed a trend toward reduced sperm counts isolated from the epididymis ( Fig H in S1 Fig ) . Several defects such as bent midpiece and abnormal head or reduced motility were also found ( Fig I-K in S1 Fig ) . Microscopic examination confirmed that the epididymis of Gorasp2-/- mice contained rare abnormal cells with large nuclei ( Fig 2A ) , which indicates that spermiogenesis was affected . Spermatid maturation occurs in post-meiotic cells , and it is accompanied by the formation of an acrosome , which is stained with periodic acid-Schiff ( PAS ) reagent . Light microscopic examination of adult testes from Gorasp2-/- mice revealed that PAS staining was affected at all tubule stage differentiation , which suggests abnormal acrosome formation ( S2 Fig ) . This result was confirmed using an antibody against a component of the acrosomal matrix , SP56 , which becomes detectable at the beginning of acrosome assembly [25] . A complete loss of anti-SP56 staining was observed on testes sections from Gorasp2-deficient mice ( Fig 2B ) , and a disorganized and weak residual staining was observed using peanut agglutinin ( Fig 2C ) . These data demonstrated that Gorasp2 deficiency resulted in acrosomal defects that resembled the spermiogenesis defects previously described in Jam3-/- mice [6] . Therefore , we examined the relative localization of GRASP55 and JAM-C by immunofluorescence in tissue sections using Tyramide Signal Amplification ( TSA ) which allows combination of antibodies generated in the same species ( i . e . JAM-C and GRASP55 generated in rabbit ) . This technology is useful , but the enzymatic amplification step hampers comparison of signal intensities between different samples . JAM-C was widely distributed and heavily expressed in spermatogonia and primary spermatocytes . JAM-C expression was reduced in meiotic spermatocytes , with a complete loss in secondary meiotic cells and step 1 spermatids , and weak re-accumulation expression in step 2 spermatids ( Fig A , in S3 Fig and Fig 3A ) . Combination of GRASP55 and PNA staining revealed a co-polarized localization of JAM-C and GRASP55 in step 2 and step 3 round spermatids ( Fig 3A , arrowheads ) . This co-clustering of GRASP55 and JAM-C in the acrosomal region was maintained until stage VIII of seminiferous tubule differentiation , and it was lost in stage X tubules [26] . Co-immunoprecipitation experiments were performed to examine whether a transient interaction between GRASP55 and JAM-C was responsible for the co-polarized localization of these two proteins . Fig 3B shows that the two proteins co-immunoprecipitate . Testes lysates from Gorasp2-deficient mice were used as control . We thus questioned if Gorasp2-deficiency would affect JAM-C localization to acrosomal region of developing spermatids . Staining revealed that JAM-C expression was strongly reduced in round spermatids at all stages of seminiferous tubule differentiation ( Fig 3C ) , but JAM-C remained expressed in spermatogonia and spermatocytes of Gorasp2-/- mice . JAM-B interacts with JAM-C [2] and GRASP55 ( Fig 1D and 1E ) . Therefore , we examined whether JAM-B localization was affected in Gorasp2-deficient mice . We found a partial co-localization of JAM-B and JAM-C in spermatocytes and round spermatids in wild-type mice , and this co-localization was lost in Gorasp2-/- mice ( Fig B in S3 Fig ) . This result suggests that GRASP55 plays a role in the polarized re-localization of JAM-C with JAM-B at germ/Sertoli cell contacts during spermatid maturation . Spermatid maturation is associated to acrosome formation and apical ectoplasmic specialization assembly . Therefore , testes sections were stained with a well-known marker of apical ectoplasmic specializations , Nectin3 [27] . We observed a complete loss of Nectin3 staining which is consistent with defects in acrosome formation ( Fig A in S4 Fig ) . Other features of seminiferous tubule organization such as JAM-A/ZO-1 localization to basal ectoplasmic specialization or the number of Sertoli cells by seminiferous tubules were not affected in Gorasp2-/- mice ( Fig B-D in S4 Fig ) , which suggests that the spermatogenic defects in Gorasp2-/- mice were due to acrosome defects and reduced JAM-C expression in spermatids . “Golgi phase” initiates acrosome formation in step 1 round spermatids and GRASP55 is involved in Golgi apparatus assembly/disassembly [28 , 29] . Therefore , we investigated whether Gorasp2 deficiency also affected the Golgi remodeling that occurs during spermatogenesis . We used antibodies directed against the Golgi Matrix protein of 130kD ( GM130 ) to stain testes sections [30] . The results revealed that GM130 staining surrounded GRASP55 signals in spermatocytes of control mice . GM130 staining was more diffuse in spermatocytes from 35-days old Gorasp2-/- mice compared to littermate controls ( Fig 4A , arrowheads ) . We analyzed Golgi area using GM130 staining in seminiferous tubules at different differentiation stages . We found that few germ cells harbored a Golgi area greater than 5μm2 at early stages of seminiferous tubule differentiation ( II-III ) , but cells with an enlarged Golgi area were easily detected at later differentiation stages ( VIII ) in littermate control mice ( Fig 4B ) . In contrast , we found numerous enlarged Golgi in cells of the early stage tubules of Gorasp2-/- mice . Quantification indicated a specific increase in Golgi apparatus with areas greater than 5μm2 in stage II-IV seminiferous tubules of Gorasp2-/- mice compared to control animals ( Fig 4C ) . Golgi size increases during pachytene spermatocytes maturation prior to separation in four spermatid daughter cells [31] . Therefore , we investigated whether cells with enlarged Golgi corresponded to early spermatocytes using an antibody directed against SYCP3 [32] . Enlarged Golgi in Gorasp2-/- mice were present in pachytene spermatocytes of stage II-III seminiferous tubules ( Fig 4D ) . This result indicates that GRASP55 plays a role at an early stage of spermatogenic cell differentiation via regulation of Golgi reassembly at an early stage of meiotic pachytene spermatocyte maturation . We thus tested whether Golgi morphology of somatic cells was also affected by the loss of GRASP55 expression . Primary mouse embryonic fibroblasts ( MEFs ) isolated from Gorasp2-deficient embryos exhibited enlarged Golgi ribbons , which recovered a more compact appearance after GRASP55 re-expression ( Fig A in S5 Fig ) . We developed a dedicated image analysis protocol ( Fig B in S5 Fig and Supporting Information ) and quantified a two-fold reduction in Golgi density in cells lacking GRASP55 expression . Re-expression of the C-terminal mCherry-tagged form of GRASP55 rescued the Golgi density to the level of wild-type cells ( Fig C-D in S5 Fig ) . The mode of interaction of GRASP55 with JAMs may aid our understanding of the dual function of GRASP55 in Golgi stacking and JAM-B/JAM-C clustering . Therefore , we co-crystallized GRASP55 PDZ domains with peptides corresponding to the C-terminal 19-mer of mouse JAM-B ( JAM-B_P19 ) and JAM-C ( JAM-C_P19 ) . Following the nomenclature for residues binding to PDZ motifs [33] , the JAMB_P19 peptide C-terminal Isoleucine residue was designated Ile0 and subsequent residues toward the N-terminus were negatively decreased Ile-1 , Phe-2 , Ser-3 , Lys-4 , Thr-5 , His-6 , Lys-7 and Phe-8 . The bound structures of GRASP55 with an uncleaved 6xHis-Tag crystallized in the I4122 space group and contained 2 molecules in the asymmetric unit . The two structures of the complex with JAM-B ( PDB ID 5GMJ ) or JAM-C ( PDB ID 5GMI ) were solved at a resolution of 2 . 99 and 2 . 71 Å , respectively , using molecular replacement and refined to Rfree values of 27 . 4% and 29 . 1% , respectively ( S3 Table ) . Notably , GRASP55/JAM-C and GRASP55/JAM-B structures exhibited an unexpected ‘closed’ conformation that was characterized by a 33 degree rotation angle of PDZ2 towards the PDZ1 domain and a 12 . 1 Å root mean square deviation ( rmsd ) after superimposition of PDZ1 domains to the previously reported structure of the ‘ligand-free’ GRASP55 PDZ domains ( Fig 5A ) [34] . Normal mode analyses revealed that the transition between the ‘open/ligand-free’ and ‘closed/cargo bound’ conformations was confirmed using the three-lowest frequency normal modes [35 , 36] , which indicates that both conformations may exist in solution . The cargo bound conformation may be preferentially selected in the presence of C-terminal JAM peptides ( Fig A in S6 Fig ) . These structures indicate that JAM-B_P19 and JAM-C_P19 bind to a groove on the PDZ1 surface , and C-terminal residues penetrate the conventional hydrophobic cavity found in this PDZ domain ( Fig 5B; Fig B-C in S6 Fig ) . Most of the observed interactions occurred via the last four residues of JAM-B_P19 or JAM-C_P19 , where the carboxylate group of Ile0 is coordinated by a network of hydrogen bonds to the main chain amide groups in the “carboxylate binding loop” of GRASP55 PDZ1 ( Fig 5C; Fig D in S6 Fig ) . This well conserved loop generally exhibits the sequence motif: ϕ-G-ϕ ( Leu96-G97-Val98 in GRASP55 ) . Residues at positions 0 and -2 are inserted in an extended conformation and present supplementary hydrogen bonds with the 5th β-strand , which adds a 6th antiparallel β-strand to the conventional structure of the interface . Notably , one very unique feature and non-conventional interaction of GRASP55/JAM-B_P19 was the positioning of Arg101 at a close distance from the interface , which allows hydrogen-bonding interactions with PDZ2 domain amino acids ( such as Ala139 ) and Thr5 from JAM-B ( Fig 5D and 5E ) . In silico screening for inhibitors of GRASP55/JAM interaction was performed based on the allosteric structural differences between published ‘open/ligand-free’ [34] and ‘closed/cargo bound’ conformations of GRASP55 ( this study ) . The experimental approach was based on a dual strategy using molecular docking and pharmacophore filtering ( described in S1 Information ) . The first step consisted in high-throughput docking of a >200K compounds chemical library dedicated to protein-protein interactions into the binding site of the ‘closed/cargo-bound’ GRASP55 crystal structure ( PDB ID 5GMJ ) . This step was used to generate several conformations that would fit each compound of the chemical library into the binding pocket . The second step filtered million poses using a pharmacophore model . This model was based on the conventional binding interactions observed in the 3D structures of the GRASP55/JAM complex and consisted in 4 hydrogen bond donor/acceptor features and 2 hydrophobic constraints . Several compounds were selected as hits , which were confirmed using orthogonal screening assays . Compound PubChem CID #3113208 , referred to as Graspin for “GRASP55 INhibitor” hereafter , exhibited an IC50 of 8 . 4 μM towards GRASP55/JAM-B and 12 μM towards GRASP55/JAM-C as measured by HTRF ( Fig 6A and 6B ) . Graspin did not affect the irrelevant Erbin/P0071 PDZ-mediated interaction . Orthosteric validation using differential scanning fluorimetry ( DSF ) revealed that , Graspin , but not the JAM-C peptide , decreased the GRASP55 melting temperature ( Fig 6C ) , which suggests that Graspin affected GRASP55 protein stability and should mimic the loss of GRASP55 expression in a biological context . Notably , a reduction in Golgi density in wild-type MEFs was observed after 48 hours of Graspin treatment , but the Golgi density of Gorasp2-deficient MEFs was not changed ( Fig 6D ) . We next tested if GRASP55 expression or Graspin treatment affected JAM-C expression or localization in MEFs . No differences in JAM-C expression levels were observed between wild-type and Gorasp2-deficient cells in control conditions , but Graspin treatment induced a dose-dependent and specific decrease in GRASP55 and JAM-C expression in wild-type MEFs ( Fig 6E ) . This result indicated that Graspin treatment affected JAM-C expression in a GRASP55 dependent manner , likely due to decreased GRASP55 stability as suggested by the DSF results . In contrast , alternative pathways likely compensate for the constitutive loss of GRASP55 expression in somatic cells to maintain JAM-C expression . Gorasp2 deficiency results in spermatogenesis defects and loss of JAM-C localization in the acrosomal region . Therefore , we examined whether Graspin treatment affected spermatogenesis in vivo . Treatment was initiated in 27-days old mice to begin experiments in animals that did not experience a single wave of germ cell development , which ends on day 35 ( Fig A in S7 Fig ) . No obvious toxicity or changes in seminiferous tubule composition were observed under these conditions ( Fig B-C in S7 Fig ) . However , obvious tubule disorganization was visualized using DAPI/PNA staining of testes sections ( Fig D in S7 Fig ) , which suggests that tubule content was affected . This result was confirmed using flow cytometry and DAPI staining which allow quantification and discrimination of elongated spermatids ( ES ) , round spermatids ( RS , 1C ) , spermatogonia ( 2C ) and primary spermatocytes ( 4C ) [37] . A marked reduction of all spermatogenic cells was observed in Graspin-treated and Gorasp2-deficient mice ( Fig 7A and 7B ) . Quantification of flow-cytometry experiments revealed a specific decrease in the percentage of elongated cells in testes of Graspin-treated mice ( Fig 7C ) . This result is consistent with the twofold decrease in ES content observed on histological sections ( Fig E in S7 Fig ) . The expression of flow-cytometry results as absolute numbers revealed an overall two-fold reduction in testes cellularity ( Fig F in S7 Fig ) . This result suggests that Graspin induced ES depletion and affected spermatogenesis at earlier stage of differentiation , which decreased cellularity . Analysis of testes sections isolated from treated mice and stained for JAM-C and GRASP55 revealed that the down-regulation of JAM-C at the transition between spermatocyte and spermatids and the re-localization of JAM-C in the acrosomal region of RS were severely affected ( Fig 7D ) . Quantification of the frequency of co-polarized GRASP55/JAM-C staining in RS at stage V-VI and stage VIII revealed that the co-clustering of JAM-C staining with GRASP55 was severely decreased with Graspin treatment ( Fig 7E ) . This result prompted us to investigate whether Graspin treatment affected acrosomes . A strong decrease in SP56 staining and obvious reduction in ES content of some tubules was found after Graspin treatment ( Fig 7F ) . These effects were not due to increased apoptosis as revealed by TUNEL staining ( S8 Fig ) , which suggests that it may be due to disruption of apical ectoplasmic specialization and “premature spermiation” , as previously reported for other compounds that affect spermatogenesis [38] . Flow-cytometry comparison of epididymis content of Graspin- and vehicle- treated mice revealed a threefold increase in spermatozoa and cell debris in epididymis of Graspin treated mice ( Fig 8A and 8B ) . This increase was accompanied by a mislocalization of residual JAM-C staining to the acrosome of mature spermatozoa ( Fig 8C ) , which suggests that Graspin impaired the coordinated regulation of apical ectoplasmic specialization via inhibition of GRASP55 PDZ-mediated interactions . Graspin treatment also affected the Golgi density of pachytene spermatocytes which exhibited a significant increase in the frequencies of Golgi with areas greater than 5 μm2 in stage II-III tubules ( Fig 9 ) . Altogether , our data demonstrate that Graspin treatment mimics Gorasp2 deficiency and affects spermatogenesis via targeting Golgi reassembly in spermatocytes and inhibition of acrosomal related functions in spermatids . JAM-C interacts with PAR3 via its PDZ-binding motif and it associates with CRUMBS ( CRUMBS3/PALS1/PATJ ) and PAR ( PAR3/PAR6/aPKC ) polarity complexes in spermatids [6 , 39] . In addition , JAM-C localizes to the acrosome of spermatozoa isolated from epididymis [40] . The constitutive or conditional deletion of Jam3 in germ cells results in a loss of cytoskeletal protein polarization with an arrest of differentiation at the stage of round spermatid [6] . The role of JAM-C in germ cell polarity and adhesion to Sertoli cell was further confirmed using a small compound that destabilizes apical ectoplasmic specializations , adjudin [41] . In this study , the authors reported that adjudin-induced germ cell loss was accompanied by a decrease in JAM-C association with PALS1/PAR6 , which may contribute to sperm cell release . However , the dynamic localization and trafficking of JAM-C to apical ectoplasmic specialization or acrosome is still poorly understood . The present study identified GRASP55 as an endogenous interacting partner of the JAM-C PDZ-binding motif in developing germ cells . Gorasp2-/- mice display male infertility but do not present other gross morphological defects , similarly to Jam3-deficient mice . The major defects of Gorasp2-/- developing germ cells were defects in acrosome formation , a reduced number of elongated spermatids , a lack of polarized localization of JAM-C in round spermatids and a dramatic enlargement of Golgi apparatus in early pachytene spermatocytes . These results raise the question of how a single Golgi protein can interfere with meiosis , acrosome formation and JAM-C trafficking ? Landmark studies have documented changes in Golgi morphology during meiotic division of spermatocytes or during early spermiogenesis [42 , 43] . However , the underlying molecular mechanisms are poorly understood . The Golgi size of rat pachytene spermatocytes increases from a diameter of 0 . 5–1 μm at stages I-III to 2–3 μm at stages IV-XII of seminiferous cycle [31] . This increase is consistent with our results showing that the threshold value of 5 μm2 for Golgi area discriminates between the spermatocytes in early ( II-III ) and late stage seminiferous tubules ( VI-XII ) . One remarkable finding was that chemical or genetic inhibition of GRASP55 resulted in Golgi enlargement of early pachytene spermatocytes , which suggests a delay of Golgi reassembly in these cells . The pachytene spermatocytes represent the longest phase of prophase during the first meiotic division [9] . Therefore , defects in Golgi reassembly may delay pachytene spermatocytes maturation and decrease cellularity as a consequence of meiotic phase lengthening . These changes are consistent with the known function of GRASP55 in Golgi stacking and breakdown in mitotic somatic cells [16 , 44] . Another finding was that chemical inhibition of GRASP55 resulted in defects of acrosome formation and premature spermiation . Acrosome development occurs during early spermiogenesis and results from the assembly of pro-acrosomic vesicles . These vesicles originate from the Golgi apparatus and GRASP55 has been reported to be specifically associated to the Golgi apparatus and acrosome of step 1–7 rat spermatids , which suggests that this protein plays a specific function in acrosome development [45] . Our results confirmed this hypothesis and demonstrated that this function relies , at least partially , on the transient interaction between GRASP55 and JAM-C during early spermiogenesis in mice ( step 1–7 spermatids ) . Therefore , we propose a model in which GRASP55 is involved in the coordinated regulation of JAM-C expression and localization in spermatids that contribute to apical ectoplasmic specialization polarity complex anchoring . Indeed , Gorasp2 deficiency resulted in acrosomal defects and the subsequent lack of polarized localization of JAM-C in the acrosomal region . Graspin inhibition of GRASP55 PDZ-mediated interactions induced more subtle changes in JAM-C expression and localization and resulted in “premature spermiation” . These effects are similar to what has been described for adjudin , which is a potential male contraceptive that specifically perturbs the function of apical ectoplasmic specializations [38 , 46] . Notably , adjudin treatment affects the association of JAM-C with polarity complex proteins [41] . These JAM-C-mediated interactions are PDZ-binding-motif-dependent and should be mutually exclusive from the interaction of JAM-C with GRASP55 [39] , which suggests that the JAM-C-interacting PDZ network plays a central role during spermiogenesis . Finally , our study revisits the structural properties of GRASP55 . A previously published , 3D structure of GRASP55 ( PDZ12 ) revealed an unusual metazoan circularly permutated PDZ domain-containing protein in which one PDZ domain contains a unique internal peptide ligand for the second PDZ domain . This intermolecular interaction between GRASP55 proteins was thought to form a strong and stable complex that bridged adjacent molecules and maintained Golgi stacks [34] . GRASP55 interaction with Golgin45 may also contribute to the Golgi stacking function of GRASP55 [47] . These intermolecular interactions between GRASP55 PDZ domains may be disrupted during the post-meiotic transition between spermatocytes and spermatids , and the PDZ1 ligand-binding domain of GRASP55 may be re-affected to JAM-C receptor function . This hypothesis is consistent with a proposed model in which the allosteric regulation of GRASP via phosphorylation disrupts it self-association and leads to Golgi breakdown during mitosis [14 , 48 , 49] . Our 3D structure pinpoints that the GRASP55/JAM-C ( or JAM-B ) complex involves a 3D interaction that induces significant conformational changes between the ‘ligand-free’ ( ‘Golgi bound’ conformation as described by Truschel et al [48] ) and the ‘cargo-bound’ conformation of the protein ( this study ) . The overlay of our structures with the published ‘ligand free’ form of GRASP55 reveals that the conformational changes occur in the main chain of the second PDZ domain ( PDZ2 ) and its relative orientation to PDZ1 , which compacts the PDZ2 into a ‘closed/cargo-bound’ conformation . Most of the interactions are present around the conventional hydrophobic cavity in the PDZ1 , but our structures reveal a very unique feature outside the conventional binding mode of the PDZ domain . Several supplementary intramolecular hydrogen bonds involving the Arg101 residue from the PDZ1 and Ala139 from the PDZ2 of GRASP55 were identified and contributed to the conformational exchange between free and bound conformations . In summary , our findings report the first non-redundant function for GRASP55 in mammals and establish a link between the function of GRASP55 in germ cells and the subcellular localization of JAM-C in spermatids . We also provide evidence that the inhibition of GRASP55 PDZ-mediated interactions using a small compound affects spermatogenesis via reduction of overall cellularity and induction of “premature spermiation” . These results demonstrate that the chemical targeting of PDZ scaffolds involved in complex biological pathway may be achieved in vivo which paves the way toward therapeutic targeting of PDZ-mediated interactions . Rabbit anti-GRASP55 ( ref . 10598-1-AP , ProteinTech ) , rabbit anti-JAM-B 829 [50 , 51] , rabbit anti-JAM-C 501 [51] , goat anti-JAM-C ( ref . AF1213 , R&D system ) , mouse anti-GM130 ( ref . 610822 , BD Biosciences ) , mouse anti-SP56 ( ref . MA1-10866 , ThermoFisher Scientific ) , rabbit anti-Nectin3 ( ref . ab63931 , Abcam ) , rabbit anti-SYCP3 ( ref . ab15093 , Abcam ) and mouse anti-actin ( ref . A3853 , Sigma ) primary antibodies and biotinylated PeaNut Agglutinin ( PNA , ref . L6135 , Sigma ) were used for immunostaining and immunoblotting . Appropriate anti-rabbit , anti-goat or anti-mouse secondary antibodies were obtained from Jackson Immuno-research Laboratories . The full-length Gorasp2 cDNA encoding the GRASP55 protein was amplified by polymerase chain reaction ( PCR ) using the oligonucleotides 5'-CTCGAGATGGGCTCCTCGCAGAGC-3' and 5'-GGATCCCCAGAAGGCTCTGAAGCATCTGC-3' , containing XhoI and BamHI sites , respectively . The amplification product was cloned in the pGEM-T Easy vector ( Promega ) . The insert was recovered by XhoI/BamHI digestion and subcloned in the pmCherry-N1 vector ( Clontech ) . To generate fusion proteins , mouse Gorasp2 cDNA cloned into pGEM-T was used as template for PCR amplification of the open reading frame ( ORF ) for mGRASP55 PDZ1 ( aa 2–107 ) , PDZ12 ( aa 2–208 ) , mGRASP55 Full-length ( FL ) ( aa 2–451 ) or GRASP55Δ ( aa 106–451 ) using forward and reverse oligonucleotides flanked by attb1 and attb2 recombination sites . The following primers pairs were used: PDZ1 For: 5'- GGGGACAAGTTTGTACAAAAAAGCAGGCTTCCTGGTTCCGCGTGGATCCGGCTCCTCGCAGAGCGTCG-3’ or 5'-GGGGACAAGTTTGTACAAAAAAGCAGGCTTCGGCTCCTCGCAGAGCGTCGAGAT-3’ ( with and without sequence coding for a thrombin cleavage site , respectively ) ; PDZ2 For: 5'-GGGGACAAGTTTGTACAAAAAAGCAGGCTTCGGGGCCAACGAAAACGTTTGGCATGTGCTG-3’ PDZ1 Rev: 5'-GGGGACCACTTTGTACAAGAAAGCTGGGTCTTACCCGTCAAAGCTGCAGAAACGAATGCT-3' , PDZ2 Rev: 5'-GGGGACCACTTTGTACAAGAAAGCTGGGTCTTATTCAAAGGGGCGTGTAGGTATTCGGTGCA-3' and GRASP55 FL Rev: 5'-GGGGACCACTTTGTACAAGAAAGCTGGGTCTTAAGAAGGCTCTGAAGCATCTGCATCAGAC-3' . The amplicons were cloned by the BP reaction into pDONRZeo ( Gateway® Technology ) to produce the corresponding entry vectors . The coding sequences were transferred by LR cloning in pDESTTM15 and pDESTTM17 prokaryotic expression vectors intended to produce the corresponding N-terminal GST- or 6His-tagged fusion protein , respectively . This was accomplished by induction for 3 h at 37°C or 18 h at 25°C with 0 . 2 mM isopropyl-β-D-thiogalactopyranoside in E . coli BL21 ( DE3 ) bacteria cells transformed with the purified plasmids . Fusion proteins were recovered from the cell lysates by conventional affinity chromatography on Glutathione sepharose 4B ( GE17-0756-01 , Sigma-Aldrich ) or Ni-NTA Agarose ( R90115 , ThermoFisher ) . The 6His-tagged PDZ12 used for crystallography was further purified using Resource Q Sepharose anion exchange ( 17-1177-01 , GE Healthcare ) followed by Superdex 75 gel-filtration chromatography ( 17-5174-01 , GE Healthcare ) . Biotinylated 19-mer peptides corresponding to the carboxy-terminal sequences of JAM-A ( Biotin-SQPSTRSEGEFKQTSSFLV ) , JAM-B ( Biotin-SKVTTMSENDFKHTKSFII ) , JAM-C ( Biotin-NYIRTSEEGDFRHKSSFVI ) , and the same sequences lacking the three last amino acids ( Covalab , France ) were immobilized on streptavidin Sepharose high-performance beads ( GE Healthcare ) . Wild-type mouse testes were isolated , frozen in nitrogen , crushed with a pestle and solubilized in lysis buffer ( 50 mM HEPES pH 7 . 3 , 10% glycerol , 0 . 1 mM EDTA , 150 mM NaCl , 1% Triton X100 and protease inhibitors ) . One milliliter of testis lysate ( 5 mg of protein ) was added to the peptide-coupled beads ( 20 μL ) and incubated for 2 h . The beads were washed 5 times in lysis buffer , boiled in Laemmli buffer , and proteins were analyzed by mass spectrometry or immunoblotting . To visualize proteins by silver staining , 10% of the denatured protein extracts were loaded in a 4–12% Bis-Tris gradient pre-cast NuPAGE gel and run with MOPS buffer according to the manufacturer’s instructions ( Invitrogen ) . For mass spectrometry analysis , 90% of the denatured protein extracts were also loaded in a 4–12% Bis-Tris acrylamide gel , but running of the samples was stopped as soon as the proteins had stacked as a single band . Protein-containing bands were stained with Imperial Blue ( Thermo Scientific ) , cut from gel , and following reduction and iodoacetamide alkylation , digested with high sequencing grade trypsin ( Promega ) . The extracted peptides were further concentrated before analysis . Mass spectrometry analysis was conducted by liquid chromatography-tandem mass spectrometry ( LC-MSMS ) using a LTQ-Velos-Orbitrap ( Thermo Scientific ) online with a nanoLC RSLC Ultimate 3000 chromatography system ( Dionex ) . Five microliters corresponding to 1/5th of the whole sample was injected into the system in triplicate . After pre-concentrating and washing the sample on a Dionex Acclaim PepMap 100 C18 column ( 2 cm × 100 μm i . d . , 100 Å , 5 μm particle size ) , the peptides were separated on a Dionex Acclaim PepMap RSLC C18 column ( 15 cm × 75 μm i . d . , 100 Å , 2 μm particle size ) at a flow rate of 300 nL/min with a two-step linear gradient ( 4–20% acetonitrile/H2O; 0 . 1% formic acid for 90 min and 20-45-45% acetonitrile/H2O; 0 . 1% formic acid for 30 min ) . For peptide ionization using the nanospray source , the spray voltage was set at 1 . 4 kV , and the capillary temperature was 275°C . All of the samples were measured in data-dependent acquisition mode . Each run was preceded by a blank MS run to monitor system background . The peptide masses were measured using a full scan survey ( scan range of 300–1700 m/z , with 30 K FWHM resolution at m/z = 400 , target AGC value of 1 . 00 × 106 and maximum injection time of 500 ms ) . In parallel to the high-resolution full scan in Orbitrap , the data-dependent CID scans of the 10 most intense precursor ions were fragmented and measured in the linear ion trap ( normalized collision energy of 35% , activation time of 10 ms , target AGC value of 1 . 00 × 104 , maximum injection time of 100 ms , isolation window of 2 Da ) . Parent masses obtained in the Orbitrap analyzer were automatically calibrated using a locked mass of 445 . 1200 . The fragment ion masses were measured in the linear ion trap to obtain the maximum sensitivity and the maximum amount of MS/MS data . Dynamic exclusion was implemented with a repeat count of 1 and exclusion duration of 30 s . Raw files ( triplicates ) generated from mass spectrometry analysis were processed with Proteome Discoverer 1 . 4 ( ThermoFisher Scientific ) . This software was used to search the data using an in-house Mascot server ( version 2 . 4 . 1 , Matrix Science Inc . ) against the Mouse subset ( 16 , 696 sequences ) of the SwissProt database ( version 2014_11 ) . Database searches were performed using the following settings: a maximum of two trypsin miscleavages allowed , methionine oxidation and N-terminal protein acetylation as variable modifications , and cysteine carbamido-methylation as a fixed modification . A peptide mass tolerance of 6 ppm and fragment mass tolerance of 0 . 8 Da were used for the search analysis . Only peptides with high stringency Mascot score threshold ( identity , FDR < 1% ) were used for protein identification . Only proteins that interact with full-length peptides and not with peptides lacking the last three amino acids or bead control are listed in S1 Table . Number of peptide-spectrum matches was indicated to show the relative amount of pulled down proteins . Entry clones were used in a Gateway LR reaction to transfer the DNA coding for the full-length coding sequence or GRASP55Δ into the Y2H activation domain expression vector pACT2 [52] . DNA fragments encoding the cytoplasmic sequences of JAM-A , JAM-B ( 41 last residues ) and JAM-C ( 48 residues ) or lacking the sequence encoding the last three aa of the proteins ( JAMsΔ constructs ) were cloned into the Y2H binding domain expression vector pGBT9 . The vectors were then co-transformed in the AH109 yeast strain ( MATa , trp1-901 , leu2-3 , 112 , ura3-52 , his3-200 , gal4Δ , gal80Δ , LYS2∷GAL1UAS-GAL1TATAHIS3 , GAL2UAS-GAL2TATA-ADE2 , URA3∷MEL1 UASMEL1TATA-lacZ , MEL1 ) using the lithium acetate method [53] . Following transformation , the yeast were plated onto synthetic complete ( SC ) medium lacking leucine ( -L ) and tryptophan ( -W ) and were incubated at 30°C for 4 to 5 days . The yeast clones were then transferred in liquid SC-WL for 3 days at 30°C with agitation to normalize the yeast cell concentration used for the phenotypic assay . The cells were then diluted 1/20 in water and spotted onto selective medium ( -WHL ) for the phenotypic assay . The binding parameters for the JAM peptides to the fusion proteins were evaluated using the homogenous time-resolved fluorescence assay ( HTRF ) . Peptide binding to the fusion proteins was measured in 0 . 05 M HEPES , 0 . 15 M NaCl , and 0 . 25% BSA ( w/v ) pH 7 . 3 at equilibrium ( 18 h , 4°C ) in reaction mixtures consisting in: fusion proteins at the indicated concentrations ( Fig 1D ) or 2 . 5 x 10−9 M ( Fig 6B ) , anti-GST or anti-6His antibody coupled to terbium cryptate ( 1 x 10−9 M ) , streptavidin-d2 ( 1 . 25 x 10−9 M ) ( Cisbio ) , and biotinylated peptide ( 6 x 10−9 M ) with competing non-biotinylated peptide or organic compound at the indicated concentration . In the latter case , DMSO concentration was kept constant . Upon excitation of the reaction mixture at 337 nm , a 615 nm fluorescence emission is produced by the donor terbium that excites a 665 nm emission by the acceptor streptavidin-d2 bound to the biotinylated peptide , only if it resides in close vicinity to the donor , i . e . bound to the fusion protein . The intensity of light emission at 615 and 665 nm was measured using a Polarscan Omega ( BMG Labtech ) microplate reader equipped for HTRF . For each condition , the A665/A615 ratio ( R ) of fluorescence was calculated . The change in fluorescence , delta F ( ΔF ) , was then computed as follows: [ ( RSample-RNSB ) /RNSB]x100 , where RNSB is the A665/A615 fluorescence ratio produced by the reaction mixture without fusion protein or biotinylated peptide . The EC50 was determined by plotting the ratio of the ΔF with homologous non-biotinylated or pharmacological inhibitor over ΔF0 ( ΔF without competitor ) against the log of the inhibitory compound using dose-response and curve-fitting analyses in Prism software ( variable slope , four parameters ) . Values with an R square value greater than 0 . 99 were considered as significant . Isothermal titration calorimetry ( ITC ) was used to evaluate the thermodynamic parameters of the binding between GRASP55 and the selected JAM peptides . Purified GRASP55 was extensively dialyzed in 100 mM NaPO4 buffer at pH 7 . 5 . Peptide powders were dissolved directly in the last protein dialysate prior to the experiments . The protein concentration was calculated by measuring the absorbance at 280 nm using a NanoDrop ND1000 ( Thermo Scientific ) , and the titrations were conducted using a MicroCal ITC200 microcalorimeter ( GE Healthcare ) . Each experiment was designed using a titrant concentration ( peptide in the syringe ) set at 10 to 30 times the analyte concentration ( protein in the cell ) and generally using 17 injections of 2 . 3 μL at 25°C ( see S2 Table for details ) . A small initial injection ( generally 0 . 2 μL ) was included in the titration protocol to remove air bubbles trapped in the syringe prior to the titration . Integrated raw ITC data were fitted to a one-site nonlinear least-squares fit model using the MicroCal Origin plugin ( http://www . originlab . com/ ) after subtraction of the control experiments ( titration of the ligand from the syringe into the buffer ) when necessary . Finally , the ΔG ( G: Gibbs free energy ) and TΔS ( T: absolute temperature , S: entropy ) values were calculated from the fitted ΔH ( H: enthalpy ) and KA values using the following equations: ΔG = -R . T . lnKA and ΔG = ΔH–TΔS . Purified GRASP55 was concentrated to approximately 15 mg/mL in a solution of 20 mM Tris . HCl pH 8 . 0 , 150 mM NaCl for crystallization . Initial hits were obtained using commercially available sparse matrix screens ( Hampton Research ) using the sitting drop vapor diffusion method at 20°C . Optimization was conducted with the hanging drop vapor diffusion method , and diffraction-quality crystals were obtained in a solution of 2 . 0 M sodium formate , 0 . 1 M sodium acetate , pH 4 . 6 . Crystals were soaked with JAM-B or JAM-C peptide using a 1:1 molar ratio for one day . The crystals were cryoprotected in reservoir solution supplemented with 25% glycerol and then flash frozen in liquid nitrogen . For structural characterization and refinement , see Supplemental Experimental Procedures . Differential scanning fluorimetry ( DSF ) was performed as previously described [54] . A protein/SYPRO orange dye mix containing 4 μM GRASP55 and a 1:5 , 000 dilution of dye ( Life Technologies ) were prepared in phosphate-buffered saline ( PBS ) extemporally . Then , 19 . 5 μL of the protein/dye mix was aliquoted into a 96-well plate , and 0 . 5 μL of Graspin ( 2 mM stock solution in 100% DMSO , 50 μM final concentration ) or DMSO control ( 2 . 5% final DMSO concentration ) was dispensed . The GRASP55/JAM-C DSF experiment was performed by adding JAM-C peptide ( 1 mM stock in PBS ) to the protein/dye mix at a final concentration of 50 μM in the presence of 2 . 5% DMSO . After sealing with optical tape , thermal melting experiments were performed using a CFX96 ( Bio-Rad ) Real-time PCR detection system . The plates were first equilibrated at 25°C for 5 min and then heated at increments of 1°C every 60 s , from 20 to 90°C . The fluorescence intensity was recorded at every temperature step using the built-in FRET filter . Raw fluorescence data were evaluated using Microsoft Excel and GraphPad Prism template files adapted from Niesen et al [54] . After normalization , the melting temperatures ( Tm ) were measured using a Boltzmann fit equation in GraphPad Prism 5 . 03 . Mice were used in compliance with the laws and protocols approved by the animal ethics committees ( Agreement #02294 . 01 ) . Gorasp2-deficient animals were generated as described in Supporting Information . Mice used in this study were backcrossed for more than six generation onto C57BL/6J background . Knock-out mice or littermate controls were obtained from heterozygous crossing . Sperm analysis was outsourced to Charles River Company . Sperm concentration , percentage of cells with normal morphology , abnormal head , bent midpiece , normal motility but also weight of testis , epididymis and seminal vesicle were determined . The GRASP55 inhibitor Graspin ( PubChem CID #3113208 , Vitas-M Laboratory Ltd . , Ref . STK700118 ) was dissolved in 10% DMSO , 90% corn oil and was injected intraperitoneally into 27-day-old wild type male mice at 50 mg/kg on days 0 , 3 , 7 , 10 , and 14 ( see Fig A in S8 Fig ) . Mice receiving Graspin or vehicle treatments were sacrificed 16 days after treatment initiation . Gorasp2+/+ and Gorasp2-/- primary Mouse Embryonic Fibroblasts ( MEFs ) were isolated from 14-day embryos . The two uterine horns were collected in sterile conditions . Each embryo was released in PBS , head was collected for genotyping , liver and viscera were removed . Embryos were crushed on 70 μm cell strainer , washed in culture medium and seeded in 25 cm2 flask . MEFs were cultivated in DMEM supplemented with 10% fetal calf serum ( FCS ) , 2 mM L-Glutamine , 100 U/ml penicillin-streptomycin , 1% essential amino acids , 25 μM β-mercaptoethanol and 1 mM sodium pyruvate at 37°C in a 5% CO2 humidified atmosphere . Re-expression of GRASP55 in Gorasp2-/- MEFs was achieved upon transfection of GRASP FL fused to mCherry using MEF 2 Nucleofector Kit according to manufacturer instruction ( Lonza ) . For Graspin treatment , MEFs plated at 70% confluency were incubated overnight and treatment was started the following morning for 48hours with Graspin at indicated concentrations . Graspin stock solution was dissolved at 10mM concentration in anhydrous DMSO ( Cat# D12345 , Life Technology ) . Treatment with DMSO 0 . 5% corresponding to the highest Graspin concentration ( 50μM ) was used as control . For co-immunoprecipitation of GRASP55 with JAM-C , Gorasp2+/+ and Gorasp2-/- mouse testes were frozen in nitrogen , crushed with a pestle and solubilized in lysis buffer ( 50 mM HEPES pH 7 . 3 , 10% glycerol , 0 . 1 mM EDTA , 150 mM NaCl , 1% Triton X100 and protease inhibitors ) . Protein G Sepharose 4 Fast Flow ( GE Healthcare ) was coupled to an anti-GRASP55 or rabbit IgG control antibody and incubated with pre-cleared testis lysate ( 5 mg/mL of proteins , approximately 45 mg per condition ) overnight at 4°C . For GRASP55 immunoblotting , Gorasp2+/+ and Gorasp2-/- mouse lung , heart and testis tissues were frozen in nitrogen , crushed with a pestle and solubilized in RIPA buffer ( 50 mM Tris HCL pH 7 . 5 , 150 mM NaCl , 1% Triton X100 , 0 . 1% SDS , 1% Na deoxycholate and protease inhibitors ) . Denatured proteins were separated by electrophoresis in 8% or 10% acrylamide gels and transferred to nitrocellulose membrane . The membranes were blocked with 5% non-fat dry milk , 0 . 05% Tween , and 1X PBS for 1 h at room temperature and incubated with primary antibodies overnight at 4°C , followed by secondary antibodies for 1 h at room temperature ( RT ) . Testes were carefully collected , and surrounding tissues were removed . The organs were fixed in 4% paraformaldehyde in PBS overnight and conserved in ethanol 70% before paraffin-embedding . 3-μm-thick deparaffinized sections were stained with PAS or used for immunofluorescence . Primary antibodies were incubated overnight at 4°C , and secondary antibodies were incubated for 1 h RT . Same-species primary antibodies ( pAb 501 rabbit anti-mouse JAM-C and rabbit anti-mouse GRASP55 antibodies in Figs 3 and 7 ) were detected using tyramide signal amplification ( TSA ) according to manufacturer instructions ( PerkinElmer Inc . ) . The slides were mounted with Prolong Gold Antifade Reagent ( Invitrogen ) . For JAM-A , ZO-1 and SOX 9 staining , testes were fixed in 4% paraformaldehyde in PBS overnight , washed in PBS and transferred in 30% sucrose overnight before soaking , embedding and freezing in gelatin-sucrose solution ( 7 . 5% and 15% respectively , in PBS ) . Sections ( 14-μm-thick ) were generated with a CryoStar NX70 cryostat ( Thermo Scientific ) . IF were performed in same conditions as previously described . Detection of apoptotic cells on testis section after Graspin treatment was assessed by TUNEL staining according to manufacturer instructions ( DeadEnd Fluorometric TUNEL System , Promega ) . Images were acquired using LSM510 META and LSM880 AiryScan confocal microscopes ( Zeiss ) and analyzed using Zen , ImageJ and Adobe Photoshop software . Periodic Acid Schiff coloration of testis sections was performed on Bouin’s solution fixed tissues as previously described [55] using hematoxylin-eosin as counterstain . Intact epididymes ( caput , corpus and cauda ) were collected from Gorasp2+/+ , Gorasp2-/- , vehicle or Graspin treated mice . Epididymes were minced and placed into 500μl of PBS at 37°C from 30 min to allow sperm to swim-out . Diffused cell suspension were filtered through a 70μM cell stainer and resuspended in 500μl PBS solution . 50 μL of cell suspension were loaded into a cytospin chamber and centrifuged for 10 min at 250 rpm on poly L-Lysine coated slides . After centrifugation , supernatant were removed and cells on microscope glass slides were fixed 10 min in ice-cold methanol . Then , cells were washed in PBS and incubated with primary antibody solution ( JAM-C ) overnight at 4°C and secondary antibody , PNA-FITC and DAPI solution 1h , RT . The slides were mounted with Prolong Gold Antifade Reagent ( Invitrogen ) . Seminiferous tubules from decapsulated testes or epididymes were minced in PBS , warmed to 37°C and incubated for 15 min at room temperature with agitation ( 200 rpm on an orbitary shaker ) . Diffused germ cell suspension were collected in PBS , filtered through a 70μM cell strainer ( Ref 352350 , BD Falcon ) , fixed and permeabilized using the Cytofix/Cytoperm kit ( BD Biosciences ) . DNA was stained by incubation with DAPI for 30 min at room temperature . Flow cytometry analysis was performed using a BD-FORTESSA ( BD Biosciences ) cytometer , and the results were analyzed using BD-DIVA version 8 ( BD Bioscience ) , FlowJo version 10 ( TreeStar ) and Kaluza version 1 . 3 ( Beckman Coulter Inc . ) softwares . Data were analyzed for statistical significance using GraphPad Prism software with the methods that are mentioned in figure legends .
Spermatogenesis defects are a common cause of male sterility . Spermatogenesis occurs in the seminiferous tubules of the testes and involves adhesive interactions between developing germ cells and Sertoli cells . Knock-out mouse models identified several adhesion molecules that are critically involved in spermatogenesis . We previously demonstrated that the Junctional Adhesion Molecule-C ( JAM-C ) plays a crucial role in establishing spermatids polarity . The latter involves rearrangements of the Golgi apparatus in spermatids which contribute to acrosome formation . The present study demonstrated that the C-terminal cytosolic region of JAM-C interacted with the Golgi reassembly stacking protein of 55 kDa ( GRASP55 ) encoded by Gorasp2 and that spermatogenesis was impaired in Gorasp2-deficient mice . We developed an inhibitor of GRASP55 interaction with JAM-C and demonstrated that treatment of wild-type mice with the inhibitory compound induced germ cell loss . Therefore , the male infertility-associated pathway identified in this study is important not only from a genetic point of view , but also as a potential target for male contraception .
You are an expert at summarizing long articles. Proceed to summarize the following text: Screening of herbal remedies for Cl− channel inhibition identified Krisanaklan , a herbal extract used in Thailand for treatment of diarrhea , as an effective antidiarrheal in mouse models of secretory diarrheas with inhibition activity against three Cl− channel targets . Krisanaklan fully inhibited cholera toxin-induced intestinal fluid secretion in a closed-loop mouse model with ∼50% inhibition at a 1∶50 dilution of the extract . Orally administered Krisanaklan ( 5 µL/g ) prevented rotavirus-induced diarrhea in neonatal mice . Short-circuit current measurements showed full inhibition of cAMP and Ca2+ agonist-induced Cl− conductance in human colonic epithelial T84 cells , with ∼50% inhibition at a 1∶5 , 000 dilution of the extract . Krisanaklan also strongly inhibited intestinal smooth muscle contraction in an ex vivo preparation . Together with measurements using specific inhibitors , we conclude that the antidiarrheal actions of Krisanaklan include inhibition of luminal CFTR and Ca2+-activated Cl− channels in enterocytes . HPLC fractionation indicated that the three Cl− inhibition actions of Krisanaklan are produced by different components in the herbal extract . Testing of individual herbs comprising Krisanaklan indicated that agarwood and clove extracts as primarily responsible for Cl− channel inhibition . The low cost , broad antidiarrheal efficacy , and defined cellular mechanisms of Krisanaklan suggests its potential application for antisecretory therapy of cholera and other enterotoxin-mediated secretory diarrheas in developing countries . Secretory diarrhea is a major health challenge in developing countries , representing the second leading cause of mortality globally in children under age 5 [1] . Repeated episodes of hypovolemia from diarrhea can produce malnutrition and impaired development [2] . The mainstay of diarrhea therapy is oral rehydration solution ( ORS ) , which consists of an aqueous mixture of salts and carbohydrates [3] , [4] . Though ORS has reduced mortality from diarrhea four-fold in the last 3 decades , its efficacy is limited , particularly in the young and elderly , and because of practicalities in its availability and compliance [5] . Antisecretory drug therapy for diarrhea may be efficacious when ORS is not available , as during natural disasters , and it may potentiate the efficacy of ORS . The intestinal epithelium absorbs and secretes large volumes of fluid , with net absorption under normal conditions and net secretion in secretory diarrheas . Intestinal fluid secretion involves Cl− transport from the blood into the intestinal lumen through Cl− channels on the enterocyte apical plasma membrane , which include the cAMP-gated channel CFTR ( cystic fibrosis transmembrane conductance regulator ) and one or more CaCCs ( Ca2+-activated Cl− channels ) whose molecular identity is not known [6]–[8] . CFTR is the primary route for Cl− secretion in secretory diarrheas caused by bacterial enterotoxins in cholera and Travelers' diarrhea ( caused by enterotoxigenic E . coli ) . CaCCs are likely involved as well in these diarrheas because of cross-talk between cyclic nucleotide and Ca2+ signaling [9] , [10] , and may provide the primary route for Cl− secretion in some viral and drug-induced diarrheas , including childhood rotaviral diarrhea [11] , [12] and antiretroviral drug-induced diarrhea [13] . The Ca2+-activated Cl− channel TMEM16A is expressed intestinal pacemaker cells , the interstitial cells of Cajal , where it is required intestinal smooth muscle contraction and motility [14] , [15] . TMEM16A is widely expressed in secretory epithelia in the airways and salivary gland , but probably plays at most a minor role as a CaCC in intestinal epithelium [16] . There is currently no approved antisecretory drug for treatment of major secretory diarrheas such as cholera . Our laboratory has identified , by high-throughput screening , several classes of small-molecule CFTR and CaCC inhibitors ( reviewed in ref . [17] ) , and has shown their efficacy in mouse models of secretory diarrheas [18] , [19] . As an alternative approach to the costly and lengthy development of a new chemical entity , here we investigated the possibility that effective , natural-product antisecretory therapeutics may already be available , but unappreciated . Screening of diarrhea remedies from around the world for enterocyte Cl− channel inhibition identified Krisanaklan , a herbal extract used widely in Thailand for treatment of diarrhea , as effective in inhibiting intestinal Cl− secretion and motility . We previously reported that one component of Krisanaklan , eugenol , inhibited the CaCC TMEM16A [20] . Here , we report here on the antidiarrheal efficacy and cellular mechanisms of Krisanaklan , and suggest its potential utility for antisecretory therapy of major , life-threatening diarrheas in developing countries . This study was approved by the UCSF Institutional Animal Care and Use Committee ( IACUC approved protocol AN089748 ) , and was performed in accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . FRT cells stably expressing human CFTR or TMEM16A were generated and cultured as described [16] , [21] . T84 cells ( ATCC CCL-248 ) were cultured as described [22] . The Thai herbal formulation Krisanaklan was purchased from Osotspa Inc . ( Bangkok , Thailand ) . Snapwell inserts containing T84 or FRT cells were mounted in Ussing chambers ( Physiologic Instruments , San Diego , CA ) , as described [16] , [23] . Activators and inhibitors were added to the apical solution and an equal volume of vehicle was added at the same time to the basolateral solution . Symmetrical HCO3−-buffered solutions were used for T84 cells . For FRT cells , the hemichambers were filled with a half-Cl− solution ( apical ) and the HCO3−-buffered solution ( basolateral ) , and the basolateral membrane was permeabilized with 250 µg/mL amphotericin B . Under these conditions short-circuit current is a direct measure of apical membrane Cl− conductance . Cells were bathed for a 10 min stabilization period and aerated with 95% O2/5% CO2 at 37°C . Short-circuit current was measured using an EVC4000 Multi-Channel V/I Clamp ( World Precision Instruments , Sarasota , FL ) . T84 cells were grown on 12-mm diameter collagen-coated transwell inserts ( 0 . 4-µm pore size Costar , Corning , Tewksbury , MA ) . Cells were cultured for 5–7 days to form tight monolayers with transepithelial resistance 900–1 , 000 Ω cm2 . Krisanaklan ( 1 . 5 ml of 6% solution ) in Ringers bicarbonate buffer was added into the basolateral chamber , and 0 . 5 ml of Ringers bicarbonate alone was added into the apical chamber . Apical chamber fluid ( 200 µL ) was collected at 0 , 30 and 60 min ( and replaced with the identical volume of buffer ) . The fluid samples were bioassayed for Cl− transport inhibition by short-circuit current measurement on T84 cells as described above . The percentage transport of inhibitory substance ( s ) was computed from activities of apical samples versus the original basolateral fluid , correcting for dilution . Mice ( CD1 strain , 25–35 g ) were deprived of food for 24 h and anaesthetized with intraperitoneal 2 , 2 , 2-tribromoethanol ( Avertin , Sigma-Aldrich , St . Louise , MO ) ( 125 mg/kg ) . Body temperature was maintained at 36–38°C using a heating pad . Following a small abdominal incision , three closed mid-jejunum loops ( length 20–30 mm ) were isolated by sutures , as described [18] . Loops were injected with 100 µl of PBS or PBS containing cholera toxin ( 1 µg ) without or with Krisanaklan . The abdominal incision was closed with suture and mice were allowed to recover from anesthesia . At 4 h the mice were anaesthetized , intestinal loops were removed , and loop length and weight were measured to quantify net fluid secretion . Fluid absorption was measured separately , from the reduction in loop weight/length ratio at 30 min after injection of 200 µL PBS . PBS containing 10 mM glucose was used as a positive control for fluid absorption . Mice were killed by an overdose of Avertin . Mice ( CD1 strain , weight 25–35 g ) were deprived of food for 24 h before experiments . Krisanaklan ( 3% in 100 µL PBS ) was administered either orally or by intraperitoneal injection . Fifteen min later mice were orally administered a charcoal meal ( 0 . 2 ml of 10% activated charcoal suspended in 5% gum acacia ) with or without 3% Krisanaklan . Thirty minutes later the mice were sacrificed and the small intestine was isolated . The peristaltic index was calculated as the percentage of distance traveled of the charcoal meal relative to the total length of small intestine . Neonatal C57bl/6 mice ( age 5–7 days , weight 1 . 8–2 . 5 g ) were inoculated with 30 µL ( 1 . 2×107 pfu/mL ) of Simian SA-11 rotavirus ( ATCC VR 1739 ) by oral gavage , as modified from prior reported models [10] , [24] . The treated group received 10 µL Krisanaklan one day after rotavirus infection . Stool specimens were collected by gentle palpation of the mouse abdomen 2 day after rotavirus inoculation . For quantification of stool water content we fabricated a polydimethylsiloxane slab of 1 . 5-mm thickness with a 1 . 91-mm diameter hole to contain a cylindrical 4 . 3-mm3 volume of stool , as described [24] . The stool plug was expelled onto absorbent tissue in a humidified atmosphere and allowed to contact the tissue for 1 min . The wetted area was measured and related to absolute water content using stool standards . In some studies the mid-jejunum was perfusion-fixed at 2 days after rotavirus inoculation for preparation of 5-µm thick , hematoxylin and eosin-stained , paraffin-embedded sections . For measurement of cytosolic Ca2+ , FRT-TMEM16A cells were plated in 96-well black-walled microplates . After removal of growth medium 100 µl of 10 µM Fluo-4 NW ( Invitrogen , Carlsbad , CA ) was added and incubated at 37°C for 30 min , then at room temperature for an additional 30 min . Fluo-4 fluorescence was measured with a plate reader at excitation/emission wavelengths of 485/538 nm . cAMP was assayed in T84 cells treated for 30 min with 0 or 10 µM forskolin , without or with Krisanaklan , lysed by repeating freeze/thaw , centrifuged , and the supernatant was assayed ( Parameter cAMP immunoassay kit; R&D Systems , Minneapolis , MN ) . Fractionation was performed on an AKTA Explorer 10 system ( GE Healthcare Life Science , Piscataway , NJ ) equipped with a C18 reversed-phase column ( Varian Pursuit XRs , 250×10 mm , 5 mm particle size , Waldbronn , Germany ) , as described [20] . In separate studies Krisanaklan was dialyzed using 1- , 10- , and 50- kDa cut-off membranes ( Float-A-Lyzer G2 , Spectrum Laboratories , Rancho Dominguez , CA ) . Wild-type CD1 mice ( age 7–10 weeks ) were killed by avertin overdose ( 200 mg/kg ) . The ileum was isolated and washed with ( in mM ) : 120 NaCl , 5 KCl , 1 MgCl2 , 1 CaCl2 , 10 D-glucose , 5 HEPES , and 25 NaHCO3 ( pH 7 . 4 ) . The ends of the ileal segments were tied and connected to a force transducer , as described [25] . Ileal segments were stabilized for 60 min with a resting force of ∼1 mN , with changes of the bathing solution every 20 min . Whole-cell recordings were made at room temperature on T84 cells , and CFTR- and TMEM16A-expressing FRT cells . The bath solution contained ( mM ) : 140 N-methyl-D-glucamine-Cl , 1 CaCl2 , 1 MgCl2 , 10 glucose and 10 HEPES ( pH 7 . 4 ) for the TMEM16A and CFTR . The pipette solution contained ( in mM ) : 130 CsCl , 0 . 5 EGTA , 1 MgCl2 , 1 Tris-ATP and 10 HEPES ( pH 7 . 2 ) . TMEM16A was activated by 400 nM free Ca2+ in the pipette solution . CFTR currents were recorded by test pulse from −80 to +80 mV from a holding potential of 0 mV in the presence of forskolin . Cl− currents in FRT-TMEM16A cells were elicited by applying voltage pulses from a holding potential of 0 mV to potentials between −100 mV and +100 mV with increases of 20 mV . CaCC was activated by 1000 nM free Ca2+ in T84 cells . To record CaCC in T84 cells , external solution contained ( mM ) : 150 NaCl , 6 CsCl , 2 CaCl2 , 1 MgCl2 , 10 glucose and 10 HEPES ( pH 7 . 4 ) were used . The pipette solution contained ( in mM ) : 40 CsCl , 100 Cs-aspartate , 5 EGTA , 1 MgCl2 , 4 . 33 CaCl2 , 4 Na2-ATP and 10 HEPES ( pH 7 . 2 ) . The currents in T84 cells were evoked by test pulse from −100 mV to 100 mV with increases of 20 mV from a holding potential of −50 mV . Pipettes ( 3–4 MΩ ) were fabricated on a model P-97 electrode puller ( Sutter Instrument , Novato , CA ) and polished with a MF-900 Micro Forge ( Narishige Scientific Instruments Laboratories ) . Whole-cell currents were recorded using an Axopatch-200B ( Axon Instruments ) and currents were filtered at 1–2 kHz and digitized at 2–4 kHz . Statistical analysis was done with Prism 5 software ( GraphPad Software Inc . , San Diego , CA ) using 2-tailed Student's t test , Mann-Whitney rank-sum test , or one-way analysis of variance ( ANOVA ) , where appropriate . Data are presented as the mean ± S . E . M . A P value of 0 . 05 or less was considered significant . The Thai herbal medicine Krisanaklan ( Fig . 1A ) was identified from testing of diarrheal remedies for inhibition of intestinal Cl− channels . Fig . 1B shows inhibition of CFTR Cl− current in a human intestinal epithelial cell line ( T84 cells ) in response to stimulation by the cAMP agonists forskolin , an adenylyl cyclase activator , and IBMX , a phosphodiesterase inhibitor . The IC50 for inhibition of CFTR Cl− current was <0 . 01% Krisanaklan ( 1∶10 , 000 dilution ) , with complete inhibition at higher concentrations . CFTR Cl− current was inhibited by the CFTR inhibitor CFTRinh-172 ( red curve in Fig . 1B ) . Krisanaklan also inhibited CaCC Cl− current in T84 cells following stimulation by ATP , with IC50 ∼0 . 02% Krisanaklan ( Fig . 1C ) . The CaCC measurement was done in the presence of a CFTRinh-172 to eliminate ATP-dependent CFTR Cl− currents that arise from cross-talk between cAMP and Ca2+ signaling . CaCC Cl− current was inhibited by the non-selective CaCC inhibitor tannic acid ( red curve in Fig . 1C ) . Krisanaklan did not inhibit cAMP or Ca2+ signaling in T84 cells . Addition of Krisanaklan up to 0 . 1% did not reduce cytoplasmic cAMP accumulation in response to forskolin ( Fig . 1D ) , nor did it reduce cytoplasmic Ca2+ elevation in response to ATP ( Fig . 1E ) . These results suggest direct action of component ( s ) of Krisanaklan on CFTR and CaCC Cl− channels . Whole-cell patch-clamp was done to further investigate Krisanaklan effects on CFTR and CaCC currents . CFTR Cl− current was measured in CFTR-expressing FRT cells following forskolin addition ( Fig . 2A ) . Approximately linear Cl− currents were seen before and after CFTR inhibition by addition of a 1∶2000 dilution of Krisanaklan . CaCC Cl− current was measured in T84 cells following activation by high pipette Ca2+ in the presence of CFTR inhibitor CFTRinh-172 ( Fig . 2B ) . Outwardly rectifying Cl− currents were seen before and after Krisanaklan addition , which were fully inhibited by the CaCC inhibitor CaCCinh-A01 . Cl− current was also measured in FRT cells expressing TMEM16A ( Fig . 2C ) . The outwardly rectifying currents elicited by high pipette Ca2+ were ∼50% inhibited by a 1∶2000 dilution of Krisanaklan , and fully inhibited by the TMEM16A inhibitor T16Ainh-A01 . To investigate whether the active Cl− inhibitory component ( s ) in Krisanaklan might act from the inside or outside of cells , we used a bioassay to measure transepithelial transport in T84 cells grown on a porous filter . Following addition of Krisanaklan to the basolateral membrane bathing solution , the apical solution was sampled at 30 and 60 min and assayed for CFTR and CaCC activity by short-circuit current in T84 cells . While the component ( s ) of Krisanaklan responsible for CFTR inhibition were cell permeable , those responsible for CaCC inhibition were not ( Fig . 2D ) . Therefore , different components of Krisanaklan are responsible for CFTR and CaCC inhibition activities , as investigated further below . The results also suggest an intracellular site of action for CFTR inhibition and an extracellular site of action for CaCC inhibition . Krisanaklan was tested for antisecretory activity in a mouse model of CFTR-dependent secretory diarrhea caused by cholera toxin and of CaCC-dependent secretory diarrhea caused by rotavirus infection . An established model of cholera toxin-induced intestinal fluid secretion was used in which fluid accumulation is measured in closed loops of mouse mid-jejenum in vivo at 4 hours after injection of cholera toxin into each loop . Fig . 3A shows marked fluid accumulation in a cholera toxin-injected loop compared to a control ( PBS-injected ) loop . Inclusion of small quantities of Krisanaklan reduced loop fluid accumulation . Fig . 3B shows a dose-dependent reduction in intestinal fluid accumulation , with IC50 of 1–2 µl Krisanaklan per loop , with near complete inhibition of loop fluid accumulation at higher concentrations . The determinants of intestinal fluid accumulation include fluid secretion and absorption . To verify that Krisanaklan did not affect intestinal fluid absorption , measurements of fluid absorption were made in closed , mid-jejunal loops at 30 min after injection of 200 µl PBS , in which ∼65% of the injected fluid was absorbed . Fig . 3C shows no significant effects of Krisanaklan on loop fluid absorption . Rotaviral diarrhea in neonates is thought to result from activation of CaCC by the rotaviral enterotoxin NSP4 , which causes elevation of cytoplasmic Ca2+ in enterocytes by mechanisms involving enteric nerves , and perhaps galanin or integrin receptors [26]–[28] . To study Krisanaklan action , neonatal mice were inoculated with live rotavirus by oral gavage , which consistently produced watery diarrhea 1–3 days later . A single dose of Krisanaklan ( or saline control ) was administered at day 1 , and stool water content was determined at day 2 . Fig . 4A ( left ) shows watery stool in rotavirus-inoculated mice , and near-normal , non-watery stool in the Krisanaklan-treated mice . Stool water content was judged both from stool appearance , and semi-quantitatively from the wetted area on absorbent paper after deposition of a defined stool volume ( Fig . 4A , right ) . The prevention of watery stool by Krisanaklan could be a result of its antisecretory action and/or inhibition of rotaviral infection of the intestine . Fig . 4B shows the most characteristic finding of rotaviral infection of the small intestine , prominent enterocyte vacuolization [29] . Similar pathological changes were seen in intestine from Krisanaklan-treated mice , suggesting that Krisanaklan did not prevent the rotavirus infection . Based on our prior study of TMEM16A inhibition by Krisanaklan [20] , we postulated that the antidiarrheal action Krisanaklan may also involve a third mechanism – inhibition of intestinal smooth muscle contraction , as TMEM16A is expressed in interstitial cells of Cajal , where it is required for intestinal smooth muscle contraction [14] . Fig . 5A shows Krisanaklan inhibition of TMEM16A Cl− current in TMEM16A-expressing FRT cells , with IC50 ∼0 . 06% Krisanaklan , and complete inhibition at higher concentrations . Krisanaklan inhibition of intestinal smooth muscle contraction was measured in ex vivo mouse ileal strips using a force transducer and a 37°C physiological bath . Fig . 5B ( top ) shows spontaneous ileal contractions with amplitude ∼1 . 5 mN . In agreement with our prior data [20] , addition of Krisanaklan to the bath produced a concentration-dependent reduction , to near zero , of contraction amplitude , without effect on contraction frequency . Krisanaklan also reduced the amplitude of intestinal contractions following application of the agonist carbachol ( Fig . 5B , bottom ) . To investigate whether Krisanaklan inhibition of intestinal smooth muscle contraction found ex vivo may be relevant to gastrointestinal motility in vivo , we used a standard assay of intestinal motility involving transit of an orally administered activated charcoal meal . While intraperitoneal Krisanaklan at a dose similar to that used in humans significantly reduced peristaltic index , oral Krisanaklan did not ( Fig . 5C ) . The difference is likely due to minimal accumulation of TMEM16A-inhibiting components in Krisanaklan in interstitial cells of Cajal in the intestinal wall following oral administration . We investigated the nature of the component ( s ) responsible for Cl− channel inhibition by Krisanaklan . Initial studies showed that the Cl− channel inhibition activities of Krisanaklan were heat-insensitive ( 100°C for 2 min , data not shown ) . Several rough size fractions of Krisanaklan were prepared by dialysis using 1- , 10- and 50-kDa cut-off membranes and tested for Cl− channel inhibition . Fig . 6A shows inhibition of CFTR by the <1 kDa fraction , but little effect of the >1 , >10 and >50 kDa size fractions , suggesting that the CFTR inhibitor molecule ( s ) have molecular size <1 kDa . Similar CaCC inhibition was seen for <1 and >1 kDa size fractions , whereas the >10 and >50 kDa showed little inhibition ( Fig . 6B ) . Strong TMEM16A inhibition was seen for the <1 kDa fraction , with less inhibition for the higher molecular size fractions ( Fig . 6C ) , suggesting that the TMEM16A inhibitor molecule ( s ) have a molecular size <1 kDa . Fig . 6D shows that the >1 kDa fraction produce little inhibition of intestinal smooth muscle contraction , whereas the original Krisanaklan showed strong inhibition . Fig . 6E shows reverse-phase HPLC fractionation of Krisanaklan , done as reported previously [20] . Testing of individual fractions reveals distinct fractions as responsible for the CFTR , CaCC and TMEM16A inhibition actions of Krisanaklan . CaCC inhibition activity was found in several fractions , suggest a heterogeneous mixture of relatively large molecules as responsible . To determine which of the four herbal constituents of Krisanaklan are responsible for its Cl− channel inhibition activities , extracts were prepared from each individual herb and tested in T84 and FRT-TMEM16A cell cultures . Concentrations were adjusted to correspond to the original Krisanaklan formulation consisting of an ethanol/water ( 54∶46 ) extract in which each 100 mL is extracted from 10 g Aquilaria crassna bark ( agarwood ) , 33 . 3 g clove flower bud , 2 g Terminalia triptera Stapf bark and 4 . 8 g camphor . CFTR inhibition activity was found in the agarwood and clove tracts , but not in the camphor and Terminalia triptera extracts ( Fig . 7A ) . CaCC inhibition activity was found in the agarwood and clove extracts , but not in the camphor and Terminalia triptera extracts ( Fig . 7B ) . TMEM16A inhibition activity was found mainly in the agarwood and clove extracts ( Fig . 7C ) . There is an unmet need for effective drug therapy for secretory diarrheas , especially in developing countries where cholera and other enterotoxin-mediated secretory diarrheas remain a major cause of morbidity and mortality . Potential targets for antisecretory therapy include the causative bacterial or viral agent ( vaccines and antibiotics ) , elaborated endotoxins and endotoxin-enterocyte interactions , as well as enterocyte signaling effectors ( cAMP , cGMP , Ca2+ ) and membrane transporters involved in fluid secretion ( Cl− and K+ channels , NKCC1 ) and absorption ( NHE3 , SGLT1 ) [6] . Cl− channels are attractive targets for antisecretory therapy because they are the final , rate-limiting step in Cl− ( and hence Na+ and water ) secretion . Unlike vaccines and antimicrobials that target the causative microbial agent , therapies targeting host secretory mechanisms , such as enterocyte Cl− channels , are not subject to the emergence of resistance . Here , we identified a widely used Thai herbal remedy , Krisanaklan , as having broad antidiarrheal efficacy in bacterial and viral models of secretory diarrhea , which , at the cellular level , inhibits the two major enterocyte Cl− channels , CFTR and CaCC . CFTR and CaCCs are responsible for Cl− secretion across the luminal membrane of enterocytes in the intestinal epithelium . Several lines of evidence support the conclusion that CFTR is the major apical membrane Cl− pathway in secretory diarrheas caused by the bacterial enterotoxins in cholera and Traveler's diarrhea; ( i ) The small intestine and colon show robust cAMP-activated CFTR Cl− currents [30]; ( ii ) intestinal Cl− and fluid secretion are reduced in CFTR-deficient mice and humans [31]–[33]; and ( iii ) CFTR inhibitors are effective in various rodent models of cholera [18] , [19] . CaCC ( s ) are likely involved as well in diarrheas caused by bacterial endotoxins , as experimental evidence supports cross-talk in cAMP and signalling mechanisms in which cAMP elevation increases cytoplasmic Ca+2 [9] and Ca+2 elevation increases cytoplasmic cAMP [34] . CaCC ( s ) are proposed to be the primary route for Cl− secretion in diarrheas caused by rotaviral and other viral enterotoxins [24] , [35] and various anti-retroviral and chemotherapeutic agents [13] , [36]; however , definitive quantification of the involvement of CaCC ( s ) in diarrheas awaits their molecular identification . From these considerations therapeutics targeting both enterocyte CFTR and CaCC ( s ) are predicted to have the greatest and broadest efficacy in secretory diarrheas . Krisanaklan is an inexpensive , natural-product extract containing ingredients that fully inhibit the major enterocyte Cl− channels , CFTR and CaCC . There are two antisecretory agents currently under clinical evaluation , one natural product and one synthetic small molecule . Crofelemer , a mixture of proanthocyanidin oligomers extracted from the bark latex of Croton lechleri , was recently approved for HIV-associated diarrhea [37] . Crofelemer is a weak and partial inhibitor of CFTR ( IC50>100 µM ) , though it fully inhibits enterocyte CaCC , albeit with low potency ( IC50∼10 µM ) [23] . Crofelemer is thus unlikely to be beneficial in secretory diarrheas such as cholera and Traveler's diarrhea in which CFTR is the major Cl− secretory pathway and in which fluid secretion is very high . A small molecule , iOWH032 , is in clinical trials for cholera [38] . iOWH032 is a close chemical analog of the glycine hydrazide GlyH-101 [39] that targets the extracellular ( lumen-facing ) surface of CFTR . However , iOWH032 has low CFTR inhibition potency ( IC50>5 µM ) and hence rapid ( seconds or less ) dissociation from CFTR . Mathematical modeling of an orally administered drug targeting the extracellular surface of intestinal crypts predicts little antisecretory efficacy of a micromolar-affinity CFTR inhibitor under conditions of high fluid secretion because of convective washout [40] . Alternative candidates for CFTR-targeted antidiarrheal therapy include glycine hydrazide conjugates with IC50∼50 nM that resist convective washout [19] , [41] , and thiazolidinones and quinoxalinediones that act on the cytoplasmic surface of CFTR with IC50 as low as 4 nM [18] , [21] , [42] , [43] . The three distinct actions of Krisanaklan , including inhibition of CFTR and non-TMEM16A CaCC ( s ) , and TMEM16A , are mediated by different components of the herbal extract . HLPC fractionation showed each of the inhibition activities in different fractions , and testing of size fractions prepared by dialysis indicated that small molecules of <1 kDa molecular size account for the CFTR and TMEM16A inhibition activities , and more heterogeneous , larger molecules for CaCC inhibition . We previously reported that the small molecule eugenol , a major component of clove , as a small-molecule TMEM16A inhibitor that likely accounts , at least in part , for the TMEM16A inhibition activity of Krisanaklan [20] . The molecular identities of the CFTR and CaCC inhibitors in Krisanaklan were not determined in this study , though testing of individual herbs suggest that they arise from two of the four herbal constituents , agarwood and clove . Based on prior studies of Crofelemer [23] and red wines [44] , the compounds responsible for CaCC inhibition are probably relatively large , heterogeneous and polyphenolic , whose molecular identities would be very difficult to determine . Agarwood extracts have been shown to contain several classes of phytochemical components including alkaloids , saponin , tannins , anthroquinones , glycosides and triterpenoids [45] , [46] , some of which may be responsible its Cl− channel inhibition activity . Clove is the dried flower bud of Caryephyllus aromaticus L , which contains the volatile compound eugenol , as well as non-volatile tannins , flavonoids , sterols and glycosides [47] , [48] . Though eugenol and tannins lack CFTR inhibition activity [20] , [44] , flavonoids are known to bind to CFTR and may be responsible for CFTR inhibition . Our results suggest that Krisanaklan , or extracts/components from its individual herbal constituents , is a potential candidate for antisecretory therapy of life-threatening diarrheas in developing countries . The potential advantages of Krisanaklan over alternative antisecretory agents under development include broad Cl− channel specificity with proven efficacy in mouse models , a long history of use in adults and children , low cost , and immediate availability for clinical testing . However , data from in vitro and animal models should be extrapolated cautiously to human diarrheas because of differences in intestinal anatomy , fluid secretion rates and , potentially , enterocyte signaling mechanisms . We also note that , as found for vaccines , the efficacy of antisecretory therapeutics may differ in different target populations because of genetic and environment factors . Notwithstanding these caveats , the preclinical data reported here support clinical trials of Krisanaklan for antisecretory therapy of diarrheas .
Secretory diarrhea is a major health challenge in developing countries . Causative agents include bacteria , as in cholera , and viruses , as in childhood rotaviral diarrhea . Though oral rehydration solution ( ORS ) has reduced mortality from diarrhea four-fold in the last three decades , its efficacy is limited , particularly in the young and elderly , and because of practicalities in its availability and compliance . Antisecretory drug therapy for diarrhea may be efficacious when ORS is not available , as during natural disasters , and it may potentiate the efficacy of ORS . As an alternative approach to the costly and lengthy development of a new chemical entity , in this study we investigated the possibility that effective , natural-product antisecretory therapeutics may already be available , but unappreciated . Screening of diarrhea remedies from around the world for enterocyte chloride channel inhibition identified Krisanaklan , a herbal extract used widely in Thailand for treatment of diarrhea , as effective in inhibiting intestinal chloride secretion . We report the antidiarrheal efficacy and cellular mechanisms of Krisanaklan , providing proof-of-concept for its potential utility for antisecretory therapy of major , life-threatening diarrheas in developing countries .
You are an expert at summarizing long articles. Proceed to summarize the following text: Circadian clocks are endogenous time-keeping systems that temporally organize biological processes . Gating of cell cycle events by a circadian clock is a universal observation that is currently considered a mechanism serving to protect DNA from diurnal exposure to ultraviolet radiation or other mutagens . In this study , we put forward another possibility: that such gating helps to insulate the circadian clock from perturbations induced by transcriptional inhibition during the M phase of the cell cycle . We introduced a periodic pulse of transcriptional inhibition into a previously published mammalian circadian model and simulated the behavior of the modified model under both constant darkness and light–dark cycle conditions . The simulation results under constant darkness indicated that periodic transcriptional inhibition could entrain/lock the circadian clock just as a light–dark cycle does . At equilibrium states , a transcriptional inhibition pulse of certain periods was always locked close to certain circadian phases where inhibition on Per and Bmal1 mRNA synthesis was most balanced . In a light–dark cycle condition , inhibitions imposed at different parts of a circadian period induced different degrees of perturbation to the circadian clock . When imposed at the middle- or late-night phase , the transcriptional inhibition cycle induced the least perturbations to the circadian clock . The late-night time window of least perturbation overlapped with the experimentally observed time window , where mitosis is most frequent . This supports our hypothesis that the circadian clock gates the cell cycle M phase to certain circadian phases to minimize perturbations induced by the latter . This study reveals the hidden effects of the cell division cycle on the circadian clock and , together with the current picture of genome stability maintenance by circadian gating of cell cycle , provides a more comprehensive understanding of the phenomenon of circading gating of cell cycle . For organisms living on the surface of the earth or in shallower aquatic biotopes , the ability to adjust their metabolic processes and behaviors according to a 24-hour periodicity , and the synchronization of their internal molecular processes may provide important evolutionary advantages . Circadian clocks are endogenous time-keeping devices that are responsible for the ≈24-hour biochemical rhythm of almost all organisms ranging from simple single cellular prokaryotes to complex multi-cellular eukaryotes . Circadian clocks coordinate synchronization between internal biological processes and between environmental cues and internal biological processes . An endogenous circadian clock consists of single or multiple autoregulatory oscillator ( s ) composed of interconnected transcriptional feedback loops [1]–[4] . These molecular feedback loops contain positive and negative elements . Positive elements activate transcription of the negative elements , while negative elements inhibit the positive elements . This regulatory regime between positive and negative elements causes oscillatory fluctuation of the concentrations of both components . Recent years have seen great advances in deciphering the molecular components and concomitant regulatory logic of circadian controlling systems in at least five model systems: the cyanobacterium Synechococcus elongates , the filamentous fungus Neurospora crassa , the fruitfly Drosophila melanogaster , plant and mammals [5] . One important feature of circadian clock is that it is flexible in response to environmental and physiological changes and can be entrained or reset by many environmental factors like light , food cues and many other physiological chemical factors [6]–[9] . Chemicals with transcriptional inhibition activity has also been reported being able to entrain the circadian clock [10] . With this flexibility , circadian clocks can easily adapt to environmental conditions and reconcile and coordinate various physiological processes . The cell cycle is another fundamental clock-like periodic biological process for which interesting molecular details have been elucidated . At the molecular level , a similar regulatory scenario to the circadian clock is observed , with transcriptional and translational feedback loops underlying the cell cycle engine mechanism . The phenomena of coupling between cell cycle and circadian cycle were observed and investigated over 40 years ago [11] , [12] . In 1964 , Edmunds et . al . found that the autotrophic Euglena gracilis Klebs , grown on defined medium with a regime of 14 hours of light and 10 hours of darkness , double their cell number every 24 hours , dividing synchronously during the dark period [13] . This observation was subsequently further confirmed by Edmunds' group [12] , [14] , [15] . Such circadian phase specific distribution of cell cycle phases of DNA synthesis or mitosis was also observed in mammals both in vivo and in vitro [16] and even in tumor cells [17] . In the last few decades , this phenomenon was also observed in many other organisms [18] , [19] . These observations were all interpreted as gating of specific events of cell division by a circadian clock [11] , [20]–[22] . This prompts two questions . Why is there widespread gating of the cell cycle by a circadian clock mechanism in most organisms ? And is there any reciprocal “gating” effect of the cell cycle on the circadian clock ? As yet , there is no clear answer to this second question . However , recent findings by Nagoshi demonstrate that cell division can indeed influence circadian period length [23] , although it is not clear whether this effect on circadian period length is a gating effect on the circadian clock . Regarding the first question , the current opinion emphasizes the role of circadian clock in genome stability maintenance [24] . In order to obtain meaningful answers to these questions , one has to have a closer look at the molecular mechanisms of the circadian clock and the cell cycle engine . Because circadian rhythms involve complex transcriptional feedback loops , unperturbed transcriptional regulation of clock genes is critical for the stability of circadian rhythms . This was partially supported by the observation that treatment with the reversible transcription inhibitor 5 , 6-dichloro-1-beta-D-ribobenzimidazole alters both circadian phases and periods in the isolated eye of Aplysia [10] . During cell cycle progression , transcriptional regulation continuously changes . The most prominent changes occur at M-phase when the chromosomes condensed into compact structures . Most factors necessary for active gene expression are inaccessible to their binding site on DNA and cells undergo global transcriptional inhibition . In proliferating cells , this cell cycle-dependent transcriptional regulation occurs simultaneously with transcriptional programs of circadian regulatory machinery and , thus , transcriptional regulation events of these two molecular processes very possibly interact with each other . In this way , the two periodic molecular clock processes may interlock , especially during the global transcriptional inhibition during M-phase , which could potentially disturb the transcriptional feedback loops of the circadian clock machinery . With this possibility in mind , we reasoned that gating of the cell division cycle might help to minimize or eliminate potential disturbance of the transcriptional feedback loops of the circadian rhythm machinery . It is not easy to experimentally study the cell cycle mediated effects of transcription inhibition on the circadian clock . It is , however , feasible to investigate this problem with mathematical modeling . A number of modeling approaches have already been successfully employed to individually study circadian clocks and the cell cycle [1] , [25]–[28] . Modeling can not only reveal the underlying intrinsic molecular design principles of circadian clocks and the cell cycle machinery , but also help to predict and identify unknown components and regulatory principles . For example , using mathematical modeling approaches , Locke and colleagues predicted the presence of a new regulatory loop in the plant circadian clock system , which was supported by experimental results [29] . In this study , we investigate the hypothetical effects of global transcription inhibition in cell cycle M phase on the properties of the mammalian circadian clock and explore the implications of this effect on circadian gating of the cell cycle . Our simulation results show that transcriptional inhibition could entrain the circadian clock and at equilibrium entrainment , transcriptional inhibition pulses are always located at certain circadian phases , where they minimize inhibition induced circadian perturbation . Entrainment of a circadian cycle to light is a well established biological observation . Light induced transcriptional alteration or protein degradation contributes to such entrainment . To assess whether M-phase transcriptional inhibition can also serve as an entrainment cue for the circadian clock , we numerically simulated a mammalian circadian model modified from the model published by Goldbeter et . al . [30] by incorporating periodic transcriptional inhibition ( we will call this modified model henceforth the “coupled model” ) using fourth and fifth order Runge-Kutta method . In the coupled model , the cell cycle M-phase was mimicked by periodic transcriptional inhibition of clock genes . With this modification , maximum transcription rates of clock genes fluctuate according to a square wave ( Figure 1 ) . The trough phase of the square wave represents M phase where transcription activities lower down to zero , while the peak phase represents other phases where transcriptions take place unchanged . The cycling period was set between 10 to 50 hours with steps of one hour , which practically covers the spectrum of mammalian cell cycle periods . Figure 2 gives an overview of the equilibrium circadian periods of the coupled system . When cells divide with a period close to 23 . 85 hours , which is the intrinsic period of the original mammalian circadian model from Goldbeter et . al . , the equilibrium period of the coupled system is constant and equal to the imposed cell cycle period regardless of the circadian phase of the initiation of the M-phase transcriptional inhibition . This clearly indicates that entrainment occurs . Interestingly , such entrainment also occurred with a cell cycle period of 11 hours , approximately one half of 23 . 85 hours , or of about 48 hours ( 46 , 47 and 48 hours in Figure 2 ) , twice the 23 . 85-hour period . At other cell cycle periods , entrainment occurred irregularly and was strictly dependent on the phase of the circadian rhythm where transcriptional inhibition is initiated ( data not shown ) . This latter case can be referred to as conditional entrainment . Although we did not extend our simulation to cycle periods longer than 50 or shorter than 11 hours , we think the extrapolation is reasonable . Next , we assessed the distribution of cell cycle M-phase ( transcriptional inhibition pulse ) on the circadian phase of the coupled system at equilibrium entrainment . To this end , the phases of the circadian cycles where inhibition pulses occurred were determined at equilibrium of every simulation and plotted against the cell cycle periods . As shown in Figure 3 , patterns similar to those in Figure 2 emerge . At cell cycle periods close to half of 24 h , 24 h or twice 24 h , where period entrainment occurs , inhibition pulses were also entrained to specific circadian phases . At other phases of the period , no such phase entrainment could be detected . Figure 4 shows the details of the simulation results for cell cycle periods of 18 , 22 , 23 , 24 and 25 hours , where entrainment occurred at periods of 22 , 23 and 24 hours . For the 22 hours cell cycle period , the circadian cycle period was strictly entrained to 22 hours . The standard deviations of the circadian periods were for none of the circadian phases larger than 0 . 1 h ( data not shown ) . The inhibition pulse occurred at a single circadian phase close to peak of Per mRNA curve which is defined as CT0 . Similar strict entrainment was also observed at a period of 24 hours . In this case , the circadian period was entrained to 24 hours and the inhibition pulse occurred at a single circadian phase close to CT13 . There is a subtle difference between the case of a 23 h period and the 22 and 24 h periods . The circadian cycle of the 23 h period was still entrained to 23 hours , but equilibrium inhibition pulses occurred at two circadian phases , one that was close to CT0 and another close to CT13 , corresponding to the entrainment phases of the 22 and 24 hour periods , respectively . If inhibition occurs at circadian phases where synthesis of clock gene mRNAs are actively expressed , circadian rhythms will possibly be perturbed . However , if inhibition occurs at circadian phases either without clock gene mRNA expression or with balanced synthesis of two antagonistic genes , there will be no or minimal effect on the circadian clock . Figure 5 displays the mRNA synthesis rates of clock genes across the circadian period . Since the synthesis of Per mRNAs ( NM_011065 . 3 , NM_011066 . 3 , NM_011067 . 2 ) and Cry mRNAs ( NM_007771 . 3 , NM_009963 . 3 ) are roughly in-phase , only the synthesis rates of Per mRNA and Bmal mRNA ( NM_007489 . 3 ) are displayed in Figure 5 . The synthesis rate curves of the two mRNA molecules intersect at two points across the circadian period . These two intersection points are close to those two locking circadian phases where inhibition pulses occurred at equilibrium , as shown in Figure 4 . Since the syntheses of the Per and Bmal1 mRNAs oscillate in anti-phase , transcriptional inhibition at any point other than these two intersection points will lead to unbalanced inhibition , e . g . the less the inhibition of one gene , the greater that of the other , thus resulting in larger system perturbations . On the other hand , inhibition at these two points results in equal inhibition of both molecules and thus the least perturbation of the circadian clock . This would explain why entrainment of the circadian clock by the cell division cycle always occurs at these two phase points . Our simulation so far studied the effect of M-phase transcriptional inhibition in DD condition . In reality , light cycle and cell cycle always influence the circadian cycle simultaneously . Furthermore , experiments studying circadian entrainment of cell cycle phases are all conducted under the condition of a light-dark cycle . To directly compare experimental results with our simulation , we have to introduce a LD cycle into our model . Our working hypothesis is that entrainment of cell cycle phases , especially of the M-phase , to certain circadian phases is meant to minimize circadian perturbation induced by cell cycle progression , in particular by M-phase global transcriptional inhibition . Our objective is to determine whether , in the presence of a LD cycle , one or more circadian phase ( s ) can be identified , where the imposition of transient transcriptional inhibition does not significantly alter the circadian cycle . To this end , we conducted simulations with a model incorporating both a light-dark cycle and transcriptional inhibition cycle effects . There are three ways to conduct such a simulation study . Two different effects can be introduced either simultaneously or sequentially . Since mammals normally live under light-dark cycle conditions , we assume a light cycle factor intrinsic to the mammalian circadian clock and that a LD cycle is the background condition of other molecular processes . Thus , we first introduced a light cycle into the model , and the transcriptional inhibition cycle was introduced after the system reached a new equilibrium state . Since human and mouse cells in vivo normally show proliferation with a periodicity of 24 h or longer , we began with a 24 h transcriptional inhibition cycle . The results show that , as under the DD condition , the transcription inhibition cycle altered phase and period of the circadian clock . The magnitude of change depends on the phase of the circadian cycle at which transcriptional inhibition is imposed . Transcriptional inhibition initiated at some circadian phases induced large changes of the system , which took a long time to relax into a new equilibrium state . In these cases , systems normally do not return to the previous equilibrium state . On the other hand , imposing transcriptional inhibition at certain other circadian phases induced relatively small changes of the system , which rapidly returned to the previous equilibrium . At still other circadian phases , transcriptional inhibition induced no system changes at all . Some aspects of our results are shown in Figure 6 . It is apparent that at a circadian phase close to 14 . 5 and 19 . 5 ( phase 0 corresponds to onset of light , CT0 ) , little perturbation was induced by transcriptional inhibition ( middle and bottom panels of left Figure 6 ) , while at other phases , larger deviations were observed ( right side Figure 6 ) . At phase 1 , the system simply transits into quasi-periodicity ( top panel of left Figure 6 ) When simulations were performed with transcriptional inhibition cycles of periods other than 24 hours , phases where transcriptional inhibition induced minimum or no changes can not be detected . We further did similar simulation study in the mammalian circadian model with 19 equations published by Goldbeter et al . [30] and a Drosophila circadian model published by Udea et al . [31] to see whether this kind of phase specific difference also exists in other circadian models . Our results clearly indicated that these different models also exhibit this phase specific difference in transcriptional inhibition induced perturbation although the exact phases where transcriptional inhibition induced lest perturbations in Drosophila model are different from the two mammalian models ( see Figure S1 and Figure S2 ) . It has been demonstrated that circadian systems are robust to molecular noise and entrainment of circadian clock by light cycles can occur in the presence of molecular noise [32] , [33] . To study the effect of noise on the entrainment of circadian clock by transcriptional inhibition cycles , noises were introduced into the differential equations of the mammalian circadian model . System trajectories of the model were then simulated as above mentioned . Simulation results showed that the model exhibits robust periodic behavior in the presence of noise ( see Figure S3 ) and such periodic behavior remained when either light cycles or transcriptional inhibition cycles is imposed onto the model ( data not shown ) . For transcriptional inhibition cycles , those with periods close to 24 hours are easier to entrain the model , reflected by more focused distribution of the circadian phases where inhibition pulses occur and more centered distributions of entrained circadian periods to values identical to transcription inhibition cycle ( Figure 7 ) . When transcriptional inhibition cycles and light cycles of 24 hour are imposed onto the model , inhibition cycles fluctuating with specific phasing relationships with light cycles will induce lest rhythms changes in the model system ( Figure 8 ) . These results are compatible with the previous results in the absence of noise . Interactions between the circadian clock and the cell cycle engine have been suggested by many experimental observations in various organisms [11] , [15] , [20] , [34]–[41] . However , the interaction and communication structure between these two systems remain to be revealed . In this study , we applied a computational simulation approach to this problem . Our results show that global transcriptional inhibition during the cell cycle M phase can shift the circadian phase and serve as entrainment cue for the circadian clock . Experimental observations suggesting an interaction between the circadian clock and the cell cycle are , in most cases , simply the non-random distribution of certain cell cycle events across circadian phases or fluctuations of cell cycle regulatory gene expression with circadian periodicity . Mechanistic details of this interaction are so far not known , yet in some instances , specific molecular links have been proposed [35] , [42] . In 2003 , Matsuo et al . provided the first evidence in mouse that Wee1 , an important cell cycle regulator kinase , is under direct control of circadian clock genes and that both Wee1 expression and mitosis follow a circadian rhythm . This report provides support for the idea that the circadian clock must have a direct influence on cell cycle progression . Based on this assumption , Calzone et al . created a coupled model of circadian clock and cell cycle ( https://hal . ccsd . cnrs . fr/docs/00/07/01/91/PDF/RR-5835 . pdf ) . Since a potential influence of the cell cycle on circadian clock was not considered in their coupled model , it exhibited a bias towards the effects of the circadian clock on the cell cycle , while any reverse effect was neglected . To simulate the effects of the cell cycle on the circadian clock , appropriate molecular links have to be identified and corresponding parameters have to be determined . Compared to the evidence for a dependence of the cell cycle on the circadian clock , evidence for the reverse effect is rare . The most pertinent evidence came from fluorescent imaging of gene expression in individual NIH3T3 mouse fibroblasts with circadian rhythm [23] . It was found that cell division shifted the period length of the circadian clock . Although there is no direct evidence of the molecular mechanism underlying this phenomenon , the period length change after cell division was attributed to global transcription inhibition during cell division . Interestingly , transient transcriptional inhibition by chemicals has been demonstrated by Eskin et al . to be able to alter circadian phases and periods [10] . Considering these observations and the fact that the most prominent transcriptional change during cell cycle progression is global transcriptional inhibition associated with cell division , it is reasonable to assume that cell cycle events , in particular cell division at M-phase , exert direct effects on circadian clock . We thus focused here on the potential effects of M-phase global transcriptional inhibition on the circadian clock . One has to bear in mind , however , that cell cycle progression involves complicated transcriptional , translational and post-translational regulations . Consistent with Eskin's experimental observation , our simulation study confirmed that transcriptional inhibition changed both phase and period of the circadian clock . Two interesting points emerge from our computational simulation . The first one is the entrainment of the circadian period by the cell cycle . This entrainment occurs only at cell cycle periods close to one half , twice or equal to the intrinsic circadian model period of 23 . 85 h , namely 11 , 22 , 23 , 24 , 46 , 47 and 48 h . At other cell cycle periods , entrainment rarely occurred . The second point is that when the circadian clock system reaches a new equilibrium state after perturbation by periodic transcriptional inhibition , the circadian phase ( s ) where transcriptional inhibition pulses are locked , is ( are ) focused rather than randomly distributed across the whole circadian clock period . For the 22 hour period , transcriptional inhibition remains at the circadian phase following the Per mRNA peak , for the 23 hour period , two steady state phases exist , one equivalent to that of the 22 hour period , the other one close to the middle between two Per mRNA peaks . For the 24-hour period , one unique steady state appears again , in this case close to the middle between two Per mRNA peaks . Further inspection showed that these positions are close to phases where the synthesis rate curves of the Per and Bmal1 mRNAs intersect . It is evident that at the intersection points , the difference between the synthesis rates of these two molecules is zero and transcriptional inhibition pulses influence their synthesis to the same extent . According to the accepted mechanism of circadian clock regulation , Per exerts a negative feedback on itself , but positively affects Bmal1 expression . Similarly , Bmal1 regulates itself negatively , but regulates Per positively . This regulation regime causes an anti-phasic oscillation of these two molecules with respect to each other . When transcriptional inhibition is imposed on the circadian system , several different responses occur , depending on the circadian phase where transcriptional inhibition happens . At circadian phases where Bmal1 synthesis rate reaches maximum and Per synthesis rate is zero , transcriptional inhibition induces maximum delay of accumulation of Bmal1 mRNA , but does not affect Per mRNA synthesis . At these circadian phases , transcriptional inhibition causes maximal perturbation of the circadian system . At other phases , transcriptional inhibition delays the accumulation of one of these two mRNAs , while accumulation of the other is accelerated . The effects are also quantitatively different , depending on the exact circadian phase of transcriptional inhibition . In some phases , transcriptional inhibition delays Per mRNA accumulation but accelerates Bmal1 mRNA accumulation , while in other phases the reverse is observed . The influence on one mRNA is always associated by a simultaneous influence on the other mRNA . The magnitude of counterbalance is determined by the difference between the synthesis rates of the two molecules at that phase . The more the disturbances are balanced , the less is the circadian system affected by the transcriptional inhibition at that circadian phase . It is obvious that near the intersection points of Figure 5 , the influences are more balanced than at all other points and thus , the circadian system is less perturbed by transcriptional inhibition at phases near those points . For stable entrainment of the circadian clock , two conditions must be satisfied . One is that the circadian system must not be drastically perturbed . The other one is that the phase shift induced by the entraining cue equals the difference between the unperturbed period and the entraining cycle period . At phases near the intersection points , transcriptional inhibition induced perturbations and phase shifts satisfy these two conditions for steady entrainment , while at other phases they are less likely to be met . We assume that these special characteristics of phases near intersections may explain the fact that in most cases of the steady entrainment of the circadian clock by periodic transcriptional inhibition , inhibition pulses were , without exception locked at these unique circadian phases . Still , at cell cycle periods other than those mentioned above , transcriptional inhibition pulses were also found locked to other phases , e . g . circadian phase distribution for 10 and 43 hours in Figure 3 . We cannot yet explain this complex pattern . Further work has to be undertaken to unravel this complexity . In mouse fibroblasts cultures , it was found that cell division mainly occurred at three phases with an interval of roughly 8 hours . The reason for this discrepancy between observations in fibroblasts and our simulation is not clear . It may reflect differences between the endogenous fibroblasts circadian clock and the circadian model we used and/or differences between in vitro and in vivo conditions . In the physiological context , a circadian clock is always under the influence of a light-dark cycle . To place our simulation in a more physiological context , we also simulated the cell cycle and circadian clock interaction in the presence of a light-dark cycle . To this end , we incorporated both a light-dark cycle and the transcriptional inhibition cycle into the mammalian model . Our simulation results revealed two windows in the circadian cycle , where transient transcriptional inhibition induced only transient and small alterations to the circadian clock regulatory system . With the beginning of the light cycle taken as the 0 reference phase ( CT0 ) , one window is close to 15 h , and the other window is close to 19 h , corresponding to the middle and late night respectively . Although there is to our knowledge no experimental evidence for mammals supporting the entrainment of cell cycle M-phase to circadian phases close to the first window in our simulation , evidence from a mouse liver regeneration study revealed indeed the entrainment of hepatocyte cell cycle mitosis to phases close to this second window [42] . There are also reports on a circadian rhythm of the cell cycle M-phase in mouse and human skin and mouth mucosa epithelia [40] , [43] . According to one of these studies , mitosis occurs mainly at a phase roughly corresponding to the time before sunset [40] . This is in contrast to proliferating hepatocytes and the results of our simulation . Considering that cells of different tissue origin display distinct physiological circadian rhythms , the differences in occurrence of cell cycle M-phase between skin and mucosa epithelia and hepatocytes and our simulation study are not surprising . We do similar simulations with the mammalian circadian model of 19 equations from Goldbeter et . al . The results are similar to those of the 16 equation model . More interestingly , simulations with a Drosophila circadian model also revealed the existence of minimum perturbation at certain circadian phases . This indicates that circadian phase specific minimum perturbation by transcriptional inhibition is general to circadian systems from different species . The partial overlap between the simulated circadian phases with the smallest impact of transcriptional inhibition on the circadian clock and those experimentally observed circadian phases where mitosis most frequently occurs , suggests that the principle of minimal circadian perturbation might , at least partially , contribute to the phenomena of circadian entrainment of cell cycle mitosis in mammals . We also performed simulations with transcription cycle periods other than 24 hours . In these cases , steady entrainment can not be detected . This clearly means that cell cycles with periods different from circadian period can not result in steady entrainment and have to be gated by circadian clock to obtain steady coupling between circadian clock and cell cycle . The current view of circadian entrainment of the cell cycle is that the circadian clock helps to maintain genome stability by timing mutation sensitive cell cycle phases to circadian phases with least exposure to mutagens . Our simulation suggests that circadian entrainment of the cell cycle could also help to maintain circadian clock stability by minimizing cell division induced perturbation of the circadian clock . These two notions are not mutual exclusive . They complement each other and in combination provide for a fuller picture of an elusive phenomenon . In summary , highly regulated transcriptional processes are critical for normal functioning of the circadian clock . Global transcriptional inhibition during M-phase of the cell cycle might perturb normal progression of the circadian clock , and there might be circadian windows where transcriptional inhibition has little influence on normal circadian progression . One could therefore expect to find ( a ) molecular mechanism ( s ) which places the M-phase of the cell cycle in such windows to minimize or eliminate cell cycle induced perturbation . Our study is the first attempt to tackle this problem by computational simulation , and our results support this hypothesis . The circadian model used in this study is from the mammalian model published by Leloup and Goldbeter in 1993 [30] . There are two versions of this model . One version is composed of 16 differential equations and , the other one is composed of the same 16 equations plus three additional equations . The 16 shared equations describe the dynamics of the Per , Cry , and Bmal1 mRNAs and their corresponding proteins . The additional 3 equations in model 2 describe the dynamics of the Rev-erbalpha mRNA ( NM_145434 . 3 ) and proteins . The two models gave similar simulation results . These models reflects mRNA transcriptional regulation , protein phosphorylation regulation and protein compartmental transportation dynamics ( see Figure S4 for details ) . The dynamic behaviors of these models are generally in agreement with characteristic features of mammalian circadian clocks . For details of the equations and descriptions , we refer the readers to the original publication by Leloup and Goldbeter [30] . A Matlab ODE file for the modified model is also provided ( see Text S1 ) . We did most of our simulations with the 16 equation model . In Goldbeter's circadian model , the dynamics of three clock gene mRNA levels are governed by the following three equations:where MP , MC , MB denote the Per , Cry and Bmal1 mRNA , respectively . vsP , vsC , vsB represent the maximum transcription rates of the Per , Cry and Bmal1 mRNA , respectively . To incorporate the effects of cell cycle M-phase global transcriptional inhibition on the circadian clock , we modified Leloup's mammalian circadian model by letting parameters vsP , vsC , vsB oscillate between the optimized values of the original model and zero ( or other values below optimum ) . The oscillation of these parameters reflects the periodic cell cycle M-phase . The periods of oscillation of these parameters mimic the cell cycle period , and the differences between the two oscillating values reflect the degree of M-phase transcriptional inhibition . Although it is well known that chromosomes are highly condensed and transcription is globally inhibited during M-phase , there is no quantitative experimental result concerning the duration and extent of transcription inhibition in M-phase . Because the M-phase of the mammalian cell cycle lasts roughly 1–2 hours and is relatively constant compared to other cell cycle phases , we assume that the variation of these three parameters follows a square wave with a trough phase of relatively constant length of 30 minutes corresponding to the M-phase transcriptional inhibition pulse . We assume that transcription inhibition of circadian clock genes occurs at least at the middle part of M-phase . Based on this assumption , a duration of 30–60 minutes ( roughly half the mammalian cell cycle M-phase length ) of transcription inhibition is introduced into the model . To implement this modification , we introduced a new parameter v into the original model , whose value is governed by the following formula:in which square is a square wave function , period denotes period of transcriptional inhibition , representing cell cycle period , t denotes time and p denotes the circadian phase with which we can control where the inhibition pulse begins . To simulate oscillation of Per , Cry and Bmal1 mRNAs , vsP , vsC and vsB are all multiplied with the parameter v . The three equations governing the dynamics of the three mRNAs are thus modified as follows: In this way , the decline of vsP , vsC and vsB mimics transcriptional inhibition , and the period of variation reflects the cell cycle period . We treat the two terms of transcriptional inhibition and cell cycle M-phase global inhibition as interchangeable in this study . To study the effect of noise on the entrainment properties of periodic transcriptional inhibition , we introduced a white noise term into the differential equations of the original model as follows:where dW = δ * G , with δ controlling the magnitude of the noise and Grepresenting the Gaussian process . Noise terms were added into one or several different equations to find a proper way to introduce noise into the model . In this study , we just add a noise term into the third equation governing the dynamics of Bmal1 mRNA concentration , which functions as an important regulatory factor for circadian clock . The equation with noise term is as follows: Although the mammalian circadian models we used in this study reflect general properties of mammalian circadian clock , the parameters are basically estimated from data collected from mouse experiments . So we just list mouse Refseq accession numbers for the genes and proteins . The three Per genes and proteins are collectively represented as one Per gene and protein respectively in the model and the two Cry genes and proteins are treated as is . Genes: Clock ( NM_007715 . 5 ) ; Per1 ( NM_011065 . 3 ) ; Per2 ( NM_011066 . 3 ) ; Per3 ( NM_011067 . 2 ) ; Cry1 ( NM_007771 . 3 ) ; Cry2 ( NM_009963 . 3 ) ; Bmal1 ( NM_007489 . 3 ) ; Rev-ERBa ( NM_145434 . 3 ) . Proteins: CLOCK ( NP_031741 . 1 ) ; PER1 ( NP_035195 . 1 ) ; PER2 ( NP_035196 . 2 ) ; PER3 ( NP_035197 . 2 ) ; CRY1 ( NP_031797 . 1 ) ; CRY2 ( NP_034093 . 1 ) ; BMAL1 ( NP_031515 . 1 ) ; REV-ERBA ( NP_663409 . 2 ) .
Circadian clock and cell cycle are two important biological processes that are essential for nearly all eukaryotes . The circadian clock governs day and night 24 h periodic molecular processes and physiological behaviors , while cell cycle controls cell division process . It has been widely observed that cell division does not occur randomly across day and night , but instead is normally confined to specific times during day and night . These observations suggest that cell cycle events are gated by the circadian clock . Regarding the biological benefit and rationale for this intriguing gating phenomena , it has been postulated that circadian gating helps to maintain genome stability by confining radiation-sensitive cell cycle phases to night . Bearing in mind the facts that global transcriptional inhibition occurs at cell division and transcriptional inhibition shifts circadian phases and periods , we postulate that confining cell division to specific circadian times benefits the circadian clock by removing or minimizing the side effects of cell division on the circadian clock . Our results based on computational simulation in this study show that periodic transcriptional inhibition can perturb the circadian clock by altering circadian phases and periods , and the magnitude of the perturbation is clearly circadian phase dependent . Specifically , transcriptional inhibition initiated at certain circadian phases induced minimal perturbation to the circadian clock . These results provide support for our postulation . Our postulation and results point to the importance of the effect of cell division on the circadian clock in the interaction between circadian and cell cycle and suggest that it should be considered together with other factors in the exploitation of circadian cell cycle interaction , especially the phenomena of circadian gating of cell cycle .
You are an expert at summarizing long articles. Proceed to summarize the following text: Closely related African trypanosomes cause lethal diseases but display distinct host ranges . Specifically , Trypanosoma brucei brucei causes nagana in livestock but fails to infect humans , while Trypanosoma brucei gambiense and Trypanosoma brucei rhodesiense cause sleeping sickness in humans . T . b . brucei fails to infect humans because it is sensitive to innate immune complexes found in normal human serum known as trypanolytic factor ( TLF ) 1 and 2; the lytic component is apolipoprotein-L1 in both TLFs . TLF resistance mechanisms of T . b . gambiense and T . b . rhodesiense are now known to arise through either gain or loss-of-function , but our understanding of factors that render T . b . brucei susceptible to lysis by human serum remains incomplete . We conducted a genome-scale RNA interference ( RNAi ) library screen for reduced sensitivity to human serum . Among only four high-confidence ‘hits’ were all three genes previously shown to sensitize T . b . brucei to human serum , the haptoglobin-haemoglobin receptor ( HpHbR ) , inhibitor of cysteine peptidase ( ICP ) and the lysosomal protein , p67 , thereby demonstrating the pivotal roles these factors play . The fourth gene identified encodes a predicted protein with eleven trans-membrane domains . Using chemical and genetic approaches , we show that ICP sensitizes T . b . brucei to human serum by modulating the essential cathepsin , CATL , a lysosomal cysteine peptidase . A second cathepsin , CATB , likely to be dispensable for growth in in vitro culture , has little or no impact on human-serum sensitivity . Our findings reveal major and novel determinants of human-serum sensitivity in T . b . brucei . They also shed light on the lysosomal protein-protein interactions that render T . b . brucei exquisitely sensitive to lytic factors in human serum , and indicate that CATL , an important potential drug target , has the capacity to resist these factors . The African trypanosomes are flagellated protozoan parasites comprising several species of the genus Trypanosoma , which cause devastating diseases in humans and livestock . One key feature that distinguishes members of this group is their sensitivity to innate trypanolytic factors ( TLFs ) found in human serum . T . b . brucei and related species cause nagana in livestock but these parasites are rapidly lysed by human TLFs [1] , [2] . T . b . gambiense and T . b . rhodesiense , on the other hand , although sharing >99% genome sequence identity with T . b . brucei [3] , have evolved distinct mechanisms to escape lysis by human serum; these are the causative agents of human African trypanosomiasis ( HAT ) , also known as sleeping sickness , in Western and Eastern Africa , respectively . T . b . gambiense is responsible for 97% of reported cases of HAT [4] . There are two classes of TLF found in normal human serum , TLF-1 , which is a component of high density lipoprotein [5] , [6] , and TLF-2 , which is an apolipoprotein-A1/IgM complex [7] , [8]; the active lytic component in both TLFs is apolipoprotein-L1 ( APOL1 ) [9] . Both TLFs also contain haptoglobin-related protein , which , in the case of TLF-1 , mediates binding to the T . b . brucei haptoglobin-haemoglobin receptor ( HpHbR ) and uptake into the cell [10] , [11] . Following uptake , APOL1 is inserted into endosomal and lysosomal membranes , where Bcl-2-like pore-formation is thought to be responsible for osmotic swelling and lysis [12] , [13] . Human TLF resistance mechanisms of T . b . gambiense and T . b rhodesiense have now been described , and these involve reduced TLF binding/uptake , APOL1 sequestration , or reduced APOL1 toxicity , possibly due to membrane stiffening . Reduced TLF binding/uptake operates in T . b . gambiense due to reduced expression of HpHbR and/or mutations in HpHbR [14]–[16] . Endosomal sequestration of APOL1 operates in T . b . rhodesiense due to the expression of a serum resistance-associated protein ( SRA ) related to a glycosyl-phoshatidylinositol membrane-anchored variant surface glycoprotein ( VSG ) [2] , [17] . Expression of a VSG-related protein also confers TLF-resistance to T . b . gambiense [18] , [19] , but in this case the VSG-like T . b . gambiense-specific glycoprotein or TgsGP may protect cells from APOL1 by stiffening endosomal membranes rather than through direct interaction with , or sequestration of , APOL1 [19] . The lysosomal membrane protein , p67 [20] and inhibitor of cysteine peptidase ( ICP ) [19] have also been shown to contribute to human TLF susceptibility using loss-of-function approaches in T . b . brucei . While HpHbR plays a role in TLF binding/uptake , the mechanism by which p67 contributes to human serum sensitivity in T . b . brucei remains unknown . Depletion of p67 causes lysosomal dysfunction , but does not increase lysosomal pH [20]; acidification has been proposed to be important for the insertion of APOL1 into membranes and the resulting lytic activity [12] , [13] , [21] . The role of the individual cysteine peptidases , the targets of ICP , has not previously been investigated , although T . b . brucei and T . b . gambiense cells exposed to a cysteine peptidase inhibitor display increased accumulation of TLF-1 [2] and APOL1 [19] , strongly suggesting that a cysteine peptidase contributes to the destruction of APOL1 . Cysteine peptidase inhibition by ICP likely similarly increases APOLI accumulation , explaining increased human serum resistance following ICP knockdown [19] . Thus , gain-of-function , through the expression of modified VSGs , or loss of TLF-receptor function , have contributed to the emergence of human-infective African trypanosomes . However , other undiscovered resistance mechanisms are thought to operate in these parasites [22]; expression of TgsGP does not confer human serum resistance to T . b . brucei [23] , and the main route of entry for TLF-2 in T . b . brucei is thought to be independent of HpHbR [10] , [19] . We sought to confirm those factors known to render T . b . brucei susceptible to lysis by human serum and to screen for additional factors . A genome-scale RNA interference library screen for increased resistance to human serum identified all three known genes and only one additional gene , encoding a novel putative trans-membrane channel , with high-confidence . This library was previously shown to yield read-outs representing approximately 5-fold genome coverage , or more than 99% of the >7 , 000 non-redundant protein coding sequences in the T . b . brucei genome [24] , and an approach related to the one described here was used to identify efficacy determinants for all five current anti-HAT drugs [25] . We next explored the unexplained role of the cysteine peptidase inhibitor in this process , and show that ICP impacts human serum resistance by specifically modulating the activity of the lysosomal cysteine peptidase , cathepsin-L ( CATL ) . Natural hosts for bloodstream form ( BSF ) T . b . brucei include bovids , and these parasites are typically propagated in a culture medium containing 10% bovine serum . In this culture environment , the half maximal effective growth-inhibitory concentration ( EC50 ) of normal human serum ( NHS ) against cultured BSF T . b . brucei was less than 0 . 00025% ( Figure 1A ) , revealing the exquisite sensitivity of these parasites to lytic factors in NHS . To identify T . b . brucei factors that contribute to the trypanolytic activity of NHS , we selected a multi-genome coverage BSF T . b . brucei RNAi library in 0 . 0005% NHS ( see Figure 1B ) . Using this loss-of-function approach , knockdown of factors that normally contribute to human serum sensitivity will generate cells with increased resistance to this toxin . Under RNAi-inducing conditions , population growth was severely curtailed for six days in the presence of NHS; the human serum was added to the growth medium 24 h after inducing RNAi with tetracycline ( Figure 1C ) . A population that displayed tetracycline-dependent tolerance of this concentration of NHS emerged thereafter ( Figure 1D ) , and was harvested for DNA extraction and RNA interference target sequencing ( RIT-seq ) two days later . Using a modified RIT-seq [24] methodology ( see Materials and Methods ) , we generated and mapped individual sequence reads representing the human serum-enriched RNAi target fragments , about 0 . 5 million reads in total . Approximately 24% of these reads incorporated a 14-bp RNAi construct signature found at the junction with each gene-specific RNAi target fragment , and this allowed us to focus on only ‘high-confidence hits’: genes identified in the screen by more than 99 reads per kilobase per CDS ( Figure 2A ) , with more than 99 reads containing the RNAi construct signature , and at least two independent RNAi target fragments ( Table 1 ) . We previously applied similarly stringent criteria to define the key efficacy determinants of the anti-HAT drugs [25] . As detailed above , three T . b . brucei genes have been shown to play a role in trypanolysis by NHS . Remarkably , we identified only four high-confidence hits in our screen . The presence of the three known genes within this set ( Figure 2A ) , haptoglobin-haemoglobin receptor ( HpHbR ) [11] , inhibitor of cysteine peptidase ( ICP ) [19] , and the lysosomal membrane protein , p67 [20] , provides excellent validation for the RNAi-screening approach . The schematic in Figure 2B shows the four high-confidence loci identified in the screen with mapped sequence reads . The novel high-confidence hit ( Tb927 . 8 . 5240 ) encodes a ‘conserved hypothetical’ protein ( Figure 2A and Table 1 ) . Although it is likely membrane-associated , as it contains 11 putative trans-membrane domains , we have been unable to establish its sub-cellular localisation by C-terminal epitope tagging ( data not shown ) . Specific stem-loop RNAi depletion of Tb927 . 8 . 5240 in three independent cell lines , confirmed by quantitative reverse transcriptase PCR ( Figure 3A; Text S1 ) , had no significant effect on bloodstream-form population growth over seven days ( Figure 3B ) , but resulted in a 2 . 3-fold average increase in NHS EC50 , demonstrating its contribution to NHS-sensitivity ( Figure 3C , D ) . The additional genes highlighted in blue in Figure 2A are listed in Table 1 . These failed to fulfil the stringent criteria for further analysis detailed above . The only exception being Tb927 . 8 . 6870 whose knockdown has previously been shown to lead to a significant gain of fitness [24]; we subsequently confirmed that loss of this protein did not influence sensitivity to NHS ( data not shown ) . ICPs are conserved in protozoal and bacterial pathogens [26] . The T . b . brucei and T . b . gambiense ICP genes are almost identical and are predicted to encode proteins of 13 . 5 kDa . They are thought to block cysteine peptidase activity by occupying the substrate-binding cleft [27] , and to play a role in regulating parasite infectivity and VSG coat exchange during differentiation [28] . African trypanosomes express two cathepsins , CATB and CATL [29] , which are highly conserved between T . b . brucei and T . b . gambiense , and at least one ( CATL ) localises to the lysosome [30] . We used chemical and genetic approaches to explore the potential roles of T . b . brucei CATB and CATL in resisting lysis by human serum . Initially , we tested the dual CATB/L inhibitor , FMK024 , in combination with NHS against T . b . brucei . Isobologram and EC50 analyses revealed that this inhibitor fails to synergise with NHS in cell-killing assays ( Figure 4A , B ) . Indeed , the addition of increasing amounts of FMK024 causes little change in parasite sensitivity to NHS ( Figure 4B ) , suggesting that the inhibitory function of endogenous ICP may be modulated as a consequence of changes in protease activity elicited by exogenous inhibitor . FMK024 applied at 10 or 20 µM ( 62 . 5 and 125-fold higher than the highest concentration used here ) has been shown to cause lysosomal accumulation of TLF in T . b . gambiense [19] and of APOL1 in SRA-expressing T . b . brucei [2] , respectively . It should be noted , however , that such high concentrations of FMK024 would likely lead to total inhibition of lysosomal cathepsin activity , and it is unlikely that ICP modulation would have any impact . Indeed , FMK024 treatment is lethal at these concentrations in our EC50 assays , independent of NHS exposure ( see below ) . We next used RNAi to knockdown CATB or CATL individually in T . b . brucei . Specific protein depletion was confirmed by western blot , and subsequent analyses revealed that only CATL activity appears to be particularly important for robust growth ( Figure 5A , B ) . Previous findings suggested that CATB but not CATL was essential for growth [31] , [32]; however , our results are consistent with the recent chemical and genetic validation of CATL as a more appropriate drug target [25] , [29] . Although we used a sub-lethal knockdown in the case of CATL , we were able to obtain substantial protein depletion compatible with continued growth [25] ( Figure 5B ) . Consistent with the results obtained above using chemical inhibition of cathepsin activity , both knockdowns failed to synergise with NHS in killing T . b . brucei ( Figure 5C , D ) . These data suggest that if a protease can resist lysis by human serum , its activity is suppressed . The results above show either that repression of cathepsin activity by ICP renders T . b . brucei sensitive to human serum and that cathepsin knockdown is compensated for by down-regulation of ICP activity , or that CATB and CATL play no role in resistance to lysis by human serum . To distinguish between these possibilities , we generated icp null T . b . brucei [33] ( Figure 6A , B ) . As previously shown for a distinct cathepsin inhibitor [28] , the icp null strains displayed a minor but significant increase in FMK024 EC50 ( Figure 6C , D ) , confirming up-regulation of a cathepsin activity required for robust growth , most-likely that due to the essential CATL ( see above ) . The icp null strains were , on average , 7 . 2-fold less sensitive to NHS ( Figure 6E , F ) , validating this RNAi screening output and also consistent with a recent report [19] . In striking contrast to the situation in wild-type T . b . brucei , isobologram and EC50 analyses revealed strong synergy between FMK024 and NHS in killing icp null T . b . brucei ( Figure 7A ) . Indeed , in the presence of 5 to 160 nM FMK024 , the NHS sensitivity was almost completely reversed to that of wild-type cells ( Figure 7B ) . These results suggest that one or both of the cathepsins can indeed confer resistance to lysis by human serum , but only effectively in the absence of ICP . We next set out to determine which of the cathepsins is responsible for this phenotype . In order to assess the contribution of the individual cathepsins to resisting NHS , we generated strains for the inducible RNAi-mediated knockdown of either CATB or CATL in an icp null background . Once again , we had to use a sub-lethal knockdown in the case of CATL ( see Figure 5B ) . CATB knockdown in these strains had no impact on NHS-sensitivity ( Figure 7C , D ) . Hence , although CATB may have a role in the degradation of other host-derived proteins , including transferrin [32] , our data suggests that it does not target human serum lytic factors . In contrast , CATL knockdown was associated with a highly significant increase in sensitivity to NHS ( Figure 7E , F ) . Failure to completely reverse the NHS-resistance phenotype following CATL RNAi , may be explained by a second contributing factor or , more likely in our view , is because these experiments had to be carried out under partial knockdown conditions . We conclude that ICP increases sensitivity to NHS primarily by inhibiting CATL activity . T . b . gambiense and T . b . rhodesiense can resist the APOL1-based trypanolytic factors found in normal human serum , while T . b . brucei fails to do so . We report here an RNAi library screen in bloodstream-form T . b . brucei for resistance to human serum and identify all three known genes , as well as a novel gene , that increase T . b . brucei susceptibility to this innate immune defence mechanism . We go on to show that one of these genes , encoding inhibitor of cysteine peptidase , acts by modulating the essential activity of CATL , a lysosomal cysteine peptidase . These findings illuminate the interactions between ICP , CATL and human serum , and have important implications for human infectivity , as well as for therapies based on cathepsin inhibitors [34] or serum lytic factors [35] . Finally , we have revealed a novel role for a putative trans-membrane domain protein , Tb927 . 8 . 5240 , in determining sensitivity to NHS . A loss-of-function phenotype , associated with HpHbR [14]–[16] , contributes to human serum resistance in T . b . gambiense , the most prevalent cause of sleeping sickness . As expected , our RNAi library screen for human serum resistance identified the gene encoding this protein and also the gene encoding the lysosomal membrane protein , p67 , also previously linked to this phenotype through experimental loss-of-function analysis [20] . This confirmed the power and utility of the RNAi-screening approach . Our screen also identified the gene encoding ICP , which was recently linked to human serum sensitivity by others [19] , and a fourth , novel gene ( Tb927 . 8 . 5240 ) , encoding a predicted multi-pass trans-membrane protein , with an almost identical homolog in T . b . gambiense . These outputs indicate a remarkably low rate of false positives , and suggest a similarly low rate of false negatives when using a multi-genome coverage RNAi library to identify high-confidence hits in T . b . brucei ( see Materials and Methods ) . To improve our understanding of sensitivity to human serum in African trypanosomes , we focussed on the role of the cysteine peptidase inhibitor , ICP . Our chemical and genetic evidence are entirely consistent , and reveal CATL as the cathepsin primarily responsible for the decreased sensitivity to human serum seen following ICP deletion . Specifically , chemical inhibition or knockdown of the individual cathepsins in an icp null background revealed that only CATL can resist human serum; CATB depletion had no detectable effect on this phenotype . CATL has been shown to accumulate in the lysosome [30] , and is responsible for proteolysis of the transferrin receptor [36] and of anti-parasite IgG [28] . The lysosome is also the major site of action of APOL1 [13] , the lytic component of both TLF1 and TLF2 . Thus , CATL may target TLF , and possibly APOL1 , for destruction in the lysosome ( Figure 8 ) . ICP , therefore , naturally maintains sensitivity to human serum , possibly by restricting the proteolytic degradation of TLF ( or APOL1 ) in the lysosome . This is consistent with previous pulse chase experiments that found little proteolytic degradation of the lytic factor and its components in T . b . brucei [37] , thereby allowing APOL1 to form membrane-spanning pores , leading to lysosomal swelling and cell lysis . Unmasking of the CATL activity only in the absence of ICP confirms natural control of this cathepsin , as suspected , by ICP . Using a similar RNAi-screening approach , uptake of the anti-trypanosomal drug , suramin , was shown to be via receptor-mediated endocytosis in T . b . brucei [25] . The identification of only p67 by both screens suggests distinct uptake and trafficking factors and mechanisms involved in suramin uptake following association with the type-I trans-membrane glycoprotein , ISG75 [25] , and TLF-uptake following association with the GPI-anchored HpHbR [11] . Interestingly , CATL has now been linked to both suramin efficacy [25] and human serum toxicity ( this study ) but , while CATL can protect T . b . brucei from killing by human serum , it sensitises T . b . brucei to killing by suramin . This suggests that suramin , a napthylamine , is liberated in active form by lysosomal proteolysis , while we suggest that TLF ( APOL1 ) is degraded by lysosomal proteolysis . Trypanosomal cathepsins are targets of ongoing drug development [29] , [38] , [39] . Although it was previously suggested that CATB was an appropriate drug target [31] , more recent genetic and chemical evidence indicates that CATL is the essential cysteine peptidase of T . b . brucei and the most appropriate target [25] , [29] . Our current findings also support this view . In this context , it is worth considering the potential impact of therapy targeting the essential cysteine peptidase activity . Our results indicate little impact of exposure to such inhibitors on human serum sensitivity in T . b . brucei . However , cysteine peptidases may be more active in T . b . gambiense [19] and/or T . b rhodesiense , possibly due to selective pressure through TLF exposure , and CATL inhibition could act synergistically with TLF in this case , increasing sensitivity to lytic activity and presenting a novel rational approach to therapy . On the other hand , T . b . gambiense and T . b . rhodesiense rely upon lysosomal/endosomal VSG variants to resist the toxic effects of APOL1 . Reduced proteolysis of these factors [19] could increase parasite resistance to human serum , meaning that targeting CATL could represent a risky therapeutic strategy . Indeed , FMK024 exposure leads to an accumulation of SRA in the lysosome of T . b . brucei engineered to express this VSG-variant [2] . It will clearly be important to develop an improved understanding of the interplay among these factors in human-infective trypanosomes . We link four factors to human serum sensitivity using a genome-scale loss-of-function screen in T . b . brucei . These include all three expected factors , based on previous reports , and a novel putative trans-membrane channel . It is interesting to note that , in the case of ICP , we uncovered a gain-of-function phenotype using a loss-of-function screen; this is possible when one protein antagonises the action of another . Our findings indicate that CATL can resist lysis by human serum , and this has important implications , since CATL is a promising potential drug target . In addition , the novel link to a putative trans-membrane channel presents an excellent candidate that may facilitate TLF transit . Notably , the gene encoding TgsGP is not present in T . b . gambiense group 2 [40] , indicating a distinct human serum resistance mechanism in these parasites , and the main route of entry for TLF2 in T . b . brucei is thought to be independent of HpHbR [11] , [19] . The outputs from our screen and our studies on ICP and CATL shed light on mechanisms of toxin delivery and stability in African trypanosomes and should facilitate studies aimed at understanding the multiple mechanisms employed by T . b . gambiense and T . b . rhodesiense to resist lytic factors in humans and other primates . MITat 1 . 2 clone 221a 2T1 bloodstream-form T . b . brucei were maintained and manipulated as previously described [33] . Transformants were selected in blasticidin ( 10 µg/ml ) , hygromycin ( 2 . 5 µg/ml ) or G418 ( 2 µg/ml ) , as appropriate . For growth assays , cells were seeded at ∼105/ml , counted using a haemocytometer , and diluted back every 24 hours , as necessary , for up to seven days in the absence of antibiotics . To determine NHS EC50 , cells were seeded at 2×103 ml−1 in 96-well plates in a 2-fold dilution series of NHS ( pooled mixed gender; Sera Laboratories International ) , starting from 0 . 01%; assays were carried out in the absence of antibiotics . After ∼3 days growth , 20 µl of 125 µg/ml resazurin ( Sigma ) in PBS was added to each well and the plates incubated for a further 6 hours at 37°C . Fluorescence was determined using a fluorescence plate reader ( Molecular Devices ) at an excitation wavelength of 530 nm , an emission wavelength of 585 nm and a filter cut-off of 570 nm [41] . To analyse the combined effect of NHS and FMK024 treatment , isobologram analysis was carried out using a checkerboard approach , as previously described [42] . Data were processed in Excel , and non-linear regression analysis carried out in GraphPad Prism . The bloodstream-form T . b . brucei RNAi library [24] was thawed into 100 ml HMI-11 media ( Life Technologies ) containing 10% foetal bovine serum ( FBS; Sigma ) at a density of approximately 1×105/ml . RNAi was induced in 1 µg/ml tetracycline for 24 hours prior to the addition of 0 . 0005% NHS; RNAi induction and NHS selection were maintained throughout . Daily counts were carried out using a haemocytometer , and the total population was maintained at no lower than 20 million cells for the duration of selection . Once robust growth had been achieved , the inducibility of the selected phenotype was tested [25] ( Figure 1D ) and genomic DNA prepared for RNAi target identification . The RNAi cassettes remaining in the NHS-selected library were specifically amplified from genomic DNA using the LIB2f/LIB2r primers [43] producing a ladder of bands ranging in size from 0 . 25–1 . 5-kbp following agarose gel-electrophoresis ( data not shown ) . High-throughput sequencing of the amplified DNA was carried out on an Illumina platform ( Beijing Genome Institute ) . Using paired 150-bp sequencing reads; presence or absence of a 14-bp RNAi-construct signature was recorded in the FASTQ header line . Sequence reads were then trimmed to remove lower-quality sequences and mapped to the T . b . brucei reference genome ( release 4 . 2 ) using bowtie [44] . BAM files were processed using the SAMtools bioinformatics suite [45] . The maps were explored visually in Artemis , and plots were derived using the Artemis graph tool and processed in Adobe Photoshop Elements 8 . 0 . Stacks of reads that included the 14-bp signature on the positive strand were used to define RNAi target fragment junctions and to assign high-confidence hits as those identified by >1 RNAi target fragment . ICP deletion was carried out as described [28] , except that targeting fragments were cloned in pBSD , and the blasticidin-S-deaminase cassette was then replaced with a neomycin phosphotransferase cassette to generate pBSDΔICP and pNPTΔICP constructs . We C-terminally cMyc-tagged an endogenous copy of CATB at a native allele [46] . A 990 bp CATB C-terminal fragment minus the stop codon was cloned into pNATx12MYC [46]; the construct was linearised with PstI prior to transfection . Stem-loop RNAi constructs targeting CATB ( Tb927 . 6 . 560 ) , CATL ( Tb927 . 6 . 960-1060 ) or Tb927 . 8 . 5240 were generated in pRPaiSL [46]; 206 bp ( CATB ) , 578 bp ( CATL ) and 417 bp ( Tb927 . 8 . 5240 ) target fragments were designed using the RNAit primer design algorithm to minimise off-target effects [47] . pRPaiSL constructs were linearised with AscI to enable targeted integration at the rDNA spacer ‘landing pad’ locus in 2T1 bloodstream form T . b . brucei [33] . Details of all oligonucleotides are available on request . Linearised constructs were transferred to icp null or wild-type 2T1 T . b . brucei using a nucleofector apparatus ( Lonza ) in conjunction with cytomix or T-cell nucleofection solutions . Protein expression following RNAi depletion was analysed by SDS-PAGE and western blotting with anti-CATL and anti-cMyc , using standard protocols [48] . For each cell line and treatment , 2 µg RNA was DNase-treated and reverse-transcribed using the Superscript VILO cDNA synthesis kit ( Invitrogen ) . 100 ng ( RNA-equivalent ) cDNA was subjected to qPCR using the Quantitect SYBR Green PCR kit ( Qiagen ) and primer pairs specific for telomerase reverse transcriptase ( TERT; Tb927 . 11 . 10190 ) and Tb927 . 8 . 5240 ( details of primer sequences are available on request ) . TERT was used as a reference for normalisation of gene expression , as previously described [49] . qPCR reactions were carried out in a Rotor-gene 3000 ( Corbett Research ) , using the following cycling conditions: 95°C ( 15 minutes ) , followed by 40 cycles of 94°C ( 15 seconds ) , 58°C ( 30 seconds ) , and 72°C ( 30 seconds ) . Standard curves , derived from a series of 10-fold dilutions of the target PCR products , were used to determine reaction efficiency . Fold-change in gene expression was calculated by the ΔΔCt method [50] .
The interplay among host innate immunity and resistance mechanisms in African trypanosomes has a major impact on the host range of these tsetse-fly transmitted parasites , defining their ability to cause disease in humans . A genome-scale RNAi screen identified a highly restricted set of four genes that sensitise trypanosomes to human serum: those encoding the haptoglobin-haemoglobin receptor , a predicted trans-membrane channel , a lysosomal membrane-protein and the cysteine peptidase inhibitor . An analysis of the cysteine peptidases revealed cathepsin-L as the protease regulated by the inhibitor – and with the capacity to render the parasite resistant to lysis by human serum . These findings emphasise the importance of parasite factors for the delivery and stability of host toxins . They also shed light on the control of proteolysis by parasites and potential unanticipated consequences of therapies that target the parasite proteases .
You are an expert at summarizing long articles. Proceed to summarize the following text: Hair growth disorders often carry a major psychological burden . Therefore , more effective human hair growth–modulatory agents urgently need to be developed . Here , we used the hypertrichosis-inducing immunosuppressant , Cyclosporine A ( CsA ) , as a lead compound to identify new hair growth–promoting molecular targets . Through microarray analysis we identified the Wnt inhibitor , secreted frizzled related protein 1 ( SFRP1 ) , as being down-regulated in the dermal papilla ( DP ) of CsA-treated human scalp hair follicles ( HFs ) ex vivo . Therefore , we further investigated the function of SFRP1 using a pharmacological approach and found that SFRP1 regulates intrafollicular canonical Wnt/β-catenin activity through inhibition of Wnt ligands in the human hair bulb . Conversely , inhibiting SFRP1 activity through the SFRP1 antagonist , WAY-316606 , enhanced hair shaft production , hair shaft keratin expression , and inhibited spontaneous HF regression ( catagen ) ex vivo . Collectively , these data ( a ) identify Wnt signalling as a novel , non–immune-inhibitory CsA target; ( b ) introduce SFRP1 as a physiologically important regulator of canonical β-catenin activity in a human ( mini- ) organ; and ( c ) demonstrate WAY-316606 to be a promising new promoter of human hair growth . Since inhibiting SFRP1 only facilitates Wnt signalling through ligands that are already present , this ‘ligand-limited’ therapeutic strategy for promoting human hair growth may circumvent potential oncological risks associated with chronic Wnt over-activation . The current pharmacological treatment for hair loss disorders is unsatisfactory , with patients being limited to only two FDA-approved hair growth promoters ( minoxidil and finasteride ) , neither of which is robustly and universally efficacious [1] . Given the severe psychological burden and negative quality of life that can be associated with hair loss , additional , but safe , human hair growth–promoting agents are urgently needed . There are relatively few known drugs that cause excessive hair growth ( hypertrichosis ) in patients . Among these , the immunosuppressive calcineurin inhibitor , Cyclosporine A ( CsA ) , most frequently and characteristically induces hypertrichosis [2 , 3] . CsA also prolongs active hair growth ( anagen ) in organ-cultured human scalp hair follicles ( HFs ) ex vivo [4 , 5] . Likely , the hair growth–stimulatory effects of CsA are independent of its T cell–inhibitory activity , because human HFs grafted onto immunocompromised nude mice treated with CsA also show anagen prolongation in vivo [6] . CsA effects on hair growth have been studied most extensively in mice ( S1 and S2 Tables ) , in which CsA induces anagen in quiescent ( telogen ) HFs [7] , reportedly through blocking the nuclear translocation of nuclear factor of activated T cells ( NFATc ) 1 in epithelial HF stem cells ( eHFSCs ) [8] . Conversely , CsA inhibits HF regression ( catagen ) [9] ( S2 Table ) , presumably through blocking NFATc2 within the murine hair matrix [10] . However , this does not satisfactorily explain how CsA prolongs anagen in human scalp HFs , because neither active NFATc1 nor NFATc2 protein are expressed within the human anagen HF bulb [5] , i . e . , where catagen development is initiated [1] . Therefore , we hypothesised that other molecular pathways must underlie the anagen-prolonging activity of CsA in human HFs [5] , and that modulating these novel targets could achieve hair growth with better efficacy and fewer side effects than CsA itself . To test these hypotheses , we treated organ-cultured anagen VI human scalp HFs [11] with a therapeutic dose of CsA ( 10−7M ) [5] , followed by microarray analysis , in situ hybridisation ( ISH ) , quantitative real-time PCR ( qRT-PCR ) , immunofluorescence microscopy , and pharmacological assays . To eliminate any potentially confounding effects on bulge eHFSCs [8] , and given that eHFSCs are not actively involved in mediating the anagen–catagen transition [12] , we exclusively focused on the effects of CsA within the human anagen hair bulb . Following these experiments we identified the endogenous Wnt inhibitor , secreted frizzled related protein 1 ( SFRP1 ) , as a novel target of CsA treatment . SFRP1 activity can be directly targeted with the antagonist , WAY-316606 , which enhances human hair growth ex vivo . To identify novel CsA targets , we treated human HFs with CsA ex vivo ( 6 hours ) and measured primary changes in gene transcription through microarray analysis . Among many other differentially regulated genes ( S1 Fig ) , the Wnt inhibitor , SFRP1 [13] , demonstrated the largest decrease ( −2 . 3 fold change , p = 4 . 90E−05 ) ( Fig 1A ) . As SFRP1 has not previously been investigated in either the CsA literature or in the human hair bulb ( S1 and S3 Tables ) , we next investigated which intrafollicular cell population is responsible for the CsA-induced change in SFRP1 transcription . Using ISH , we found SFRP1 mRNA to be exclusively transcribed by fibroblasts associated with the human HF bulb , with the highest SFRP1 expression seen within the dermal papilla ( DP ) ( Fig 1B–1D and S2 Fig ) . Next , we wanted to determine if SFRP1 protein is restricted to or secreted from the DP . Immunofluorescence microscopy revealed that SFRP1 protein can be detected in both the DP and the adjacent epithelial hair matrix and pre-cortex of human scalp HFs ( Fig 1E and S2 Fig ) . Based on the distinct expression patterns of SFRP1 mRNA ( DP ) and protein ( DP and hair matrix ) , these data suggest that SFRP1 is transcribed and translated in the DP and is then secreted into the hair matrix and pre-cortex ( Fig 1F ) . We next asked by quantitative immunohistomorphometry whether CsA inhibits SFRP1 protein production in the human anagen hair bulb . This revealed a significant decrease in SFRP1 protein expression within the DP ( Fig 1G and 1H ) and in the surrounding HF epithelium ( matrix and pre-cortex ) ( Fig 1G , 1I and 1J ) after 48 hours of CsA treatment . Therefore , CsA suppresses SFRP1 protein levels within the human HF bulb . This was corroborated by ISH , which demonstrated a decrease in SFRP1 mRNA within the DP after CsA treatment ( Fig 1K–1N ) , and by qRT-PCR , which showed a significant decrease in intrafollicular SFRP1 mRNA after 48 hours ( Fig 1O ) . To determine if CsA’s hair growth–promoting effects occur through SFRP1 and if SFRP1 negatively impacts human hair growth , we first treated human HFs ex vivo with CsA and vehicle control . As a positive control , we confirmed that the percentage of anagen VI HFs was significantly higher after CsA treatment compared to vehicle control on days 4 and 6 ( S4A–S4C Fig ) , validating previous reports that CsA prolongs the duration of anagen [4 , 5] ( S2 Table ) . Next , HFs were treated with vehicle control , recombinant human SFRP1 ( rhSFRP1 ) alone , and rhSFRP1 together with CsA . Macroscopically , the treatment of rhSFRP1 induced more HFs to prematurely enter catagen by day 4 compared to the vehicle control ( S4D–S4G Fig ) . The ability of CsA to prolong anagen ( S4A Fig ) was blocked by the addition of rhSFRP1 with CsA , as the percentage of anagen HFs was comparable to vehicle control over 6 days in culture ( S4D Fig ) . Next , we analysed morphological changes with vehicle control , rhSFRP1 alone , and rhSFRP1 with CsA-treated HFs at day 6 using Ki-67/TUNEL immunofluorescence ( S5A–S5C and S6 Figs ) . Surprisingly , the percentage of Ki-67+ cells below Auber’s line was highest with rhSFRP1 treatment ( S5E Fig ) . However , this did not functionally translate into hair growth stimulation , as rhSFRP1 alone drastically reduced hair matrix size ( S5D Fig ) , whereas treatment with rhSFRP1 and CsA together showed no significant difference in hair matrix size compared to vehicle control ( S5D Fig ) . Treatment with rhSFRP1 enhanced the emigration of DP fibroblasts to the connective tissue sheath ( S5H Fig ) , which occurs upon catagen induction , leading to a reduced DP volume [14 , 15] . Also , rhSFRP1 promoted apoptosis in fibroblasts emigrating from the DP , which is indicative of catagen induction ( S5I Fig ) [14 , 15] . Conversely , there was no difference between control and rhSFRP1 with CsA in the emigration of fibroblasts ( S5H Fig ) or TUNEL+ cells in the DP stalk ( S5I Fig ) . Collectively , this ( a ) demonstrates that enhanced SFRP1 activity induces premature catagen and ( b ) suggests that CsA hair growth–promoting effects occur at least in part through inhibiting SFRP1 , since the addition of rhSFRP1 prevented CsA treatment from maintaining anagen and modulating many of these parameters above vehicle control baseline . Therefore , irrespective of any effects CsA might also exert on human eHFSCs , CsA treatment can target the DP of human scalp HFs , i . e . , the mesenchymal inductive control centre [12 , 16] . This important mesenchymal HF target had been unrecognised in previous murine work ( S1 Table ) yet is in line with our previous finding that CsA inhibits the emigration of fibroblasts from the DP in human HFs [5] . This also demonstrates that CsA suppresses a mesenchymal-to-epithelial signal via SFRP1 . Given the key role of SFRP1 as an inhibitor of canonical Wnt signalling [17] , we next asked whether Wnt ligand activity modulates canonical β-catenin in the human HF bulb . While Wnt ligand activity maintains anagen and hair shaft formation in the murine HF [18 , 19] , it is unclear whether this directly applies to humans . The distribution of cells that are Wnt active has been described in the human HF bulb [20 , 21] . However , there are clear discrepancies with the reported nuclear localisation of both β-catenin and the key canonical transcription factor lymphoid enhancer binding factor 1 ( LEF1 ) in the pre-cortex [21 , 22] and the DP [20 , 23] . Therefore , we wanted to identify which cells are precisely active for canonical β-catenin signalling . Immunofluorescence microscopy showed nuclear localisation of β-catenin in both selected human HF fibroblasts ( DP and DP stalk cells ) and epithelial cells ( hair matrix and pre-cortical hair matrix keratinocytes ) of human scalp HFs ( Fig 2A and S7 Fig ) . In addition , ISH revealed that the direct canonical β-catenin target genes axis inhibition protein 2 ( AXIN2 ) and LEF1 are both transcribed in these cell populations ( Fig 2B and 2C ) . This identifies that Wnt/β-catenin signalling is active in both epithelial and mesenchymal cells . Importantly , this also demonstrates that β-catenin activity in the epithelial regions is not restricted to the pre-cortex , as previously thought [21] , and also occurs in the proliferating hair matrix . We then asked which cells are secreting Wnt , beginning with analysing the expression of Wntless ( WLS ) , which is required for Wnt ligand secretion [18] , followed by a panel of Wnt ligands . ISH showed that WLS is present throughout the HF bulb , although with varying levels of expression ( Fig 2D and S8 Fig ) . Conversely , among the seven Wnt ligands we probed for , WNT3 , WNT4 , WNT10A , and WNT10B were expressed and restricted to the human epithelial hair matrix and pre-cortex , while WNT1 , WNT2 , and WNT3A were undetectable ( S9 Fig ) . Next , we assessed whether Wnt ligand activity modulates the canonical β-catenin pathway in the human HF bulb by treating human HFs for 48 hours with inhibitor of Wnt production-2 ( IWP-2 ) , a small molecule inhibitor of Wnt ligand secretion [24] . This significantly reduced the transcription of both AXIN2 and LEF1 ( Fig 2G and 2J ) . Therefore , Wnt secretion is required for maintaining canonical β-catenin activity in human scalp HFs , perfectly in line with the most recent demonstration that WNT10A is required for normal human skin appendage function [25] . These important background data define the location of key molecular players within the concert of Wnt ligands , β-catenin activity , and SFRP1 production in the human anagen HF . Notably , the secreted SFRP1 protein from the DP overlaps with specific Wnt ligands expressed in the immediately adjacent HF epithelium ( S9 Fig ) , suggesting that SFRP1 may inhibit canonical β-catenin activity . This hypothesis was tested by treating human HFs ex vivo with rhSFRP1 . After 48 hours , both AXIN2 and LEF1 transcription were significantly reduced ( Fig 2H and 2K ) . Conversely , when we incubated human HFs with WAY-316606 , a specific and reportedly well-tolerated antagonist of SFRP1 [26] , both AXIN2 and LEF1 transcription were significantly increased ( Fig 2I and 2L ) . Therefore , SFRP1 functions as an inhibitor of canonical Wnt/β-catenin signalling in the human hair bulb . Furthermore , ISH showed a significant increase in AXIN2 mRNA levels in situ after WAY-316606 treatment within both the pre-cortex and DP ( Fig 2P–2T ) . This demonstrates that despite the presence of spatially very well-defined Wnt ligands ( S9 Fig ) , the general presence of secreted SFRP1 protein within the epithelium serves to moderate Wnt activity as AXIN2 levels become significantly up-regulated after SFRP1 inhibition by WAY-316606 , while still retaining its spatial pattern within the human HF bulb ( Fig 2P–2T ) . In addition , this increase in Wnt activity is not due to an increase in ectopic nuclear β-catenin , as anagen HFs already express maximally high levels of nuclear β-catenin ( S10 Fig ) . Therefore , the intrafollicular increase in β-catenin activity after SFRP1 suppression is due to a response of both pre-cortical HF keratinocytes and DP fibroblasts ( both regions are Wnt active , i . e . , β-catenin+ LEF1+ AXIN2+ ) . Notably , SFRP1 transcription itself did not change after treatment with either IWP-2 , rhSFRP1 , or WAY-316606 ( Fig 2M–2O ) . This suggests that SFRP1 does not negatively regulate its own expression and indicates that Wnt ligand activity does not control SFRP1 expression . Taken together , our data introduce SFRP1 as a physiologically important regulator of canonical β-catenin activity in a human ( mini- ) organ , the HF . We also present the first evidence that the SFRP1 inhibitor , WAY-316606 , effectively enhances β-catenin activity in mammalian skin cell populations , namely in both human hair pre-cortex keratinocytes and DP fibroblasts ex vivo . β-catenin signalling is essential for murine hair growth through both the anagen-inducing properties of the DP [19 , 27] and differentiation of hair matrix keratinocytes into hair shaft trichocytes [28 , 29] . Therefore , we next addressed the clinically crucial question of whether stimulating canonical β-catenin activity within both human HF compartments ( Fig 2P–2T ) enhances human hair growth . To answer this , human HFs were treated ex vivo with WAY-316606 for 6 days , and hair shaft production was measured . WAY-316606 significantly increased hair shaft production ( elongation ) as early as 2 days following treatment ( Fig 3A ) , even faster than CsA-induced hair shaft elongation ex vivo , which occurs several days later [4 , 5] . As canonical β-catenin signalling appears to control the expression of hair keratins necessary for hair shaft production [21 , 28 , 29] , we also investigated whether WAY-316606 treatment can also impact on human hair keratin expression . Quantitative immunohistomorphometry showed that WAY-316606 treatment rapidly and significantly up-regulated protein expression of the hair shaft keratin K85 ( Fig 3B ) , which is expressed within the pre-cortical hair matrix and the hair shaft [30] . Therefore , antagonising SFRP1 activity enhances human hair shaft production , and do so more effectively than CsA . Clinically , the most important challenge in effective hair loss management is to prolong the duration of anagen [1] . CsA achieves this through blocking catagen within both mice and man [5 , 6 , 9] . It was therefore crucial to determine if antagonising SFRP1 activity through WAY-316606 treatment replicates this effect of CsA . Indeed , after 6 days of treatment with WAY-316606 , a greater percentage of organ-cultured anagen scalp HFs had remained in anagen VI , both macroscopically ( Fig 3C and S11 Fig ) and microscopically , as validated by standardised , quantitative hair cycle histomorphometry [15] ( Fig 3D–3H ) . This corresponded to a significantly higher percentage of proliferating ( Ki-67+ ) hair matrix keratinocytes ( Fig 3D and 3E ) , a greater number of DAPI+ cells below Auber’s line ( Fig 3D and 3F ) , and a significantly higher melanin content of WAY-316606–treated HFs ( Fig 3H ) . In addition , the number of fibroblasts emigrating from the DP ex vivo was reduced ( Fig 3D and 3G ) , exactly mimicking the effects of CsA [5] . However , there did not appear to be any significant changes in cell death , as analysed by TUNEL staining of the hair matrix , DP , or DP stalk ( S12 Fig ) . Therefore , directly inhibiting SFRP1 activity with WAY-316606 not only stimulates intrafollicular Wnt signalling but maintains anagen human HFs within the maximally proliferative stage of the HF cycle . Collectively , our study ( a ) identifies Wnt signalling as a novel target of CsA treatment that appears unrelated to its immunosuppressive activities; ( b ) introduces SFRP1 as a physiologically important regulator of canonical β-catenin activity in a human ( mini- ) organ , the HF , by showing that this Wnt inhibitor is a key partner in a potent new mesenchymal-epithelial interaction; and ( c ) demonstrates that WAY-316606 is a promising new pharmacological promoter of human hair growth , whose toxicity profile [26] is expected to be more favourable than that of CsA . While we have demonstrated that CsA can modulate SFRP1 , it cannot be excluded that CsA has additional effects on the immune system or other cell types and may thus indirectly affect catagen entry . Interestingly , no hair abnormalities have been reported in Sfrp1 knockout mice . Once again , this cautions against extrapolating from murine to human HF biology and only underscores the clinical importance of studying SFRP1 functions directly in human tissue . However , investigators interested in murine SFRP1 biology might wish to probe whether some of the other abnormalities reported in Sfrp1 knockout mice , e . g . , increased mammary ductal branching [31] or dysregulated glucose metabolism [32] , are pharmacologically reproducible in wild-type mice by SFRP1 inhibitor treatment . Remarkably , the extensive hair CsA literature had missed canonical Wnt signalling as a clinically important target of this widely administered immunosuppressant . The novel CsA-SFRP1-Wnt connection elucidated here may also help to better explain previously ill-understood adverse effects of CsA therapy , namely gingival hyperplasia [33] . The next key challenge is to dissect the as yet unknown molecular mechanisms by which CsA inhibits SFRP1 transcription ( to clarify this was beyond the scope and reach of the current translational study ) . The underlying intrafollicular signalling is depicted in Fig 3I: the secreted Wnt inhibitor , SFRP1 , controls human HF cycling and hair shaft production after secretion from the DP by interacting with Wnt ligands in the immediately adjacent HF epithelium . Manipulating this novel mesenchymal-epithelial signal with the SFRP1 inhibitor , WAY-316606 , increases canonical β-catenin activity in both the DP and the pre-cortex . This boost in Wnt signalling enhances human hair shaft formation and inhibits catagen entry . Therefore , Dickkopf-related protein 1 ( DKK1 ) is not the only important regulator of Wnt activity in the human HF [34] , and SFRP1 is a viable alternative target molecule for the therapeutic up-regulation of hair growth–promoting Wnt signalling . Since CsA binds and inhibits multiple targets ( e . g . , cyclophilin and calcineurin ) [35] , which is thought to underlie its serious toxicity profile [3 , 33] , topical inhibition of SFRP1 using WAY-316606 would be a much more targeted approach for stimulating human hair growth without having to use a potent immunosuppressant , particularly as thus far there are no known off-target effects from WAY-316606 treatment . In addition , WAY-316606 is highly selective against other closely related SFRP family members ( SFRP2 and SFRP5 ) . For example , at 2 μM , WAY-316606 inhibits SFRP1 activity by about 40% , whereas SFRP2 and SFRP5 activity is only inhibited by about 5% and about 2% , respectively [26] . Moreover , this Wnt disinhibition technique may be a safer long-term therapeutic strategy for stimulating β-catenin activity in the human HF . Because inhibiting SFRP1 by WAY-316606 only facilitates Wnt signalling through ligands that are already present in the human HF , this ‘ligand-limited’ strategy for promoting human hair growth may circumvent potential oncological risks typically associated with β-catenin stabilisation [36] . Tissue samples were harvested with written informed patient consent and with approval from the Manchester Skin Health Biobank ( UK North West—Haydock Research Ethics Committee approved study 14/NW/0185 ) . Male occipital scalp HFs were obtained from patients undergoing hair transplant surgery at the Crown Cosma Clinic , Manchester , UK . Tissue was dissected , with individual full-length anagen VI HFs cultured in serum-free Williams’ E medium ( Gibco , Paisley , UK ) supplemented with 2 mM L-glutamine ( Invitrogen , Paisley , UK ) , 10 ng/mL hydrocortisone ( Sigma , Dorset , UK ) , and 1% antibiotic/antimycototic mixture ( 100× , Gibco ) and incubated overnight at 37 °C and 5% CO2 as described [5 , 11] . The following day , HFs that had macroscopically remained in anagen VI had media replaced with the following additions: Frozen sections from microdissected fresh , 2- , and 6-day cultured HFs were made using a cryostat ( OTF5000 , Bright , London , UK ) at 6 μm . Masson-Fontana staining and Ki-67/TUNEL dual immunofluorescence and histomorphometric analysis was carried out as previously described [15] . Masson-Fontana , Ki-67/TUNEL , SFRP1 , and K85 staining were imaged using Biozero 8000 Keyence microscope ( Biozero , Osaka , Japan ) for image analysis . β-catenin was imaged using the Olympus BX53 upright microscope ( Olympus , Tokyo , Japan ) . Representative images for ISH were imaged using Olympus BX53 upright microscope . ImageJ was used to quantify immunofluorescent intensity and ISH signal . For some representative images , the contrast was changed globally within CorelDraw or PowerPoint , and matching settings were applied to both test and control . Frozen HF sections were fixed in 4% PFA for 20 minutes at 4 °C , then washed with Tris-buffered saline ( TBS [all wash steps used TBS] ) . Tissue was then permeabilised with 0 . 5% Triton X-100 ( in TBS ) for 10 minutes . Sections were washed and incubated with 10% normal goat serum ( NGS [in TBS] ) for 30 minutes . Next , sections were incubated with either SFRP1 ( 1:200 in 10% NGS; abcam Cat No . ab4193 ) or β-catenin ( 1:200 in 10% NGS; BD Transduction Laboratories Cat No . 610154 ) primary antibodies overnight ( 4 °C ) . The following day , sections were washed and incubated with AF-488 ( 1:200 in TBS; Invitrogen Cat No . A11008 ) or AF-594 ( 1:200 in TBS; Invitrogen Cat No . A11032 ) secondary antibodies ( 1 hour at room temperature ) . Sections were then washed and nuclei were counterstained by using DAPI ( 1 μg/mL in PBS ) for 1 minute . For negative controls , primary antibody raised against SFRP1 and β-catenin were omitted . Frozen human testis was used as a positive control for SFRP1 [38] ( S2J and S2K Fig ) , as well as analysing the differential pattern of SFRP1 mRNA and protein throughout the human HF ( S3 Fig ) . K85 immunofluorescence was performed on microdissected HFs as per SFRP1 and β-catenin , except that samples were fixed with acetone ( 10 minutes at −20 °C ) ; no permeabilisation was required and phosphate buffered saline was used for washes . A primary K85 antibody ( gift from Dr . Lutz Langbein , Heidelberg , Germany ) was used at 1:1 , 000 concentration ( in 2% NGS ) and secondary antibody goat anti–guinea pig AF-488 ( Invitrogen Cat No . A11073 ) 1:200 concentration ( in 2% NGS ) . For negative controls , primary antibody raised against K85 was omitted . OCT-embedded HFs were sectioned at 6 μm , dried at −20 °C for 1 hour , and then stored at −80 °C for subsequent use . Sections were then processed for RNA in situ detection using the RNAscope 2 . 5 HD Reagent Kit-Red ( Advanced Cell Diagnostics , Milan , Italy ) following manufacturer’s instructions . The following probes were used: SFRP1 ( NM_003012 . 4 , target 401–1971 ) , AXIN2 ( NM_004655 . 3 , target 502–1674 ) , LEF1 ( NM_001166119 . 1_ target 793–1919 ) , WLS ( NM_001193334 . 1 , target 355–1325 ) , WNT1 ( NM_005430 . 3_target 390–1863 ) , WNT2 ( NR_024047 . 1 , target 1324–2598 ) , WNT3 ( NM_030753 . 4 , target 1014–1945 ) , WNT3A ( NM_033131 . 3 , target 1212–2328 ) , WNT4 ( NM_030761 . 4 , target 132–1770 ) , WNT10A ( NM_025216 . 2 , target 658–1949 ) , WNT10B ( NM_003394 . 3 , target 865–2282 ) , PPIB as a positive control ( NM_000942 . 4 , region 139–989 ) , DapB as a negative control ( EF191515 , target 414–862 ) . Quantification of ISH was carried out as previously described [39] . Total RNA was extracted from 5 full-length HFs for each condition using the RNAeasy mini kit ( Qiagen , Manchester , UK ) according to manufacturer’s protocol and guidelines . One hundred nanograms of RNA were converted to cDNA using Tetro cDNA synthesis kit ( Bioline , London , UK ) according to manufacturer’s protocol . Quantitative PCR was performed in triplicate using Taqman probes ( Life Technologies , Taqman assay ID: SFRP1: Hs00610060_m1 , AXIN2: Hs01063170_m1 , LEF1: Hs01547250_m1 , GAPDH: Hs02758991_g1 ) . Reactions were performed and analysed using the StepOnePlus Real-Time PCR system and associated software ( Applied Biosystems , Paisley , UK ) . Relative expression was calculated using the ΔCT method against the housekeeping gene , GAPDH . Human HFs were treated for 6 hours with CsA ( 10−7M ) and total RNA was extracted using RNAeasy mini kit . Extracted RNA was taken to the Genomic Core Facilities at the University of Manchester and run onto Affymetrix U133 plus 2 . 0 GeneChip . Data were deposited in NCBI Geo ( GSE109632 ) . Technical quality control and outlier analysis were performed with dChip ( V2005 ) using the default settings . Background correction , quantile normalisation , and gene expression analysis were performed using RMA in Bioconductor . To establish relationships and compare variability between samples , principal components analysis ( PCA ) was used , because this method is able to reduce the effective dimensionality of complex gene-expression space without significant loss of information . Differential expression analysis was performed with Limma using a paired test and functions lmFit and eBayes . Gene lists of differentially expressed genes were controlled for false discovery rate ( fdr ) errors using the method of QVALUE . The microarray data set was uploaded to ingenuity pathway analysis ( IPA ) and filtered with a p-value of 0 . 05 and examined through core pathway analysis . All experiments used HFs from at least three individual male patient samples , unless stated otherwise . Results were first checked for normal distribution using D’Agostino-Pearson omnibus normality test and equal variance using F-test . Then , depending on these results , statistical analysis was performed with suitable t tests , either parametric ( unpaired t test ) or nonparametric tests ( unpaired Mann-Whitney test ) . For experiments with more than one group , unpaired one-way ANOVA was chosen for normally distributed data with Tukey test to correct for multiple comparisons , while unpaired Kruskal-Wallis test was selected for nonparametric data with Dunn’s test to correct for multiple comparisons . For qRT-PCR analysis , one-sample t test was used , with a hypothetical value set to 100 for normalised control . P-values <0 . 05 were considered significant . All statistical analysis was carried out in GraphPad Prism version 7 .
Hair loss is a common disorder and can lead to psychological distress . Cyclosporine A , a fungal metabolite commonly used as an immunosuppressant , can potently induce hair growth in humans . However , it cannot be effectively used to restore hair growth because of its toxic profile . In this study , we used Cyclosporine A as a lead compound to identify novel therapeutic targets that can aid the development of new hair growth–promoting agents . Through microarray analysis , we found that the level of the secreted Wnt inhibitor , SFRP1 , was significantly reduced by Cyclosporine A . This inspired us to design a new pharmacological approach that uses WAY-316606 , a reportedly well-tolerated and specific antagonist of SFRP1 , to prolong the growth phase of the hair cycle . We show that WAY-316606 enhances human hair growth ex vivo , suggesting that it is a more targeted hair growth promoter with the potential to treat human hair loss disorders .
You are an expert at summarizing long articles. Proceed to summarize the following text: Recent findings indicate that perturbations of the mitochondrial electron transport chain ( METC ) can cause extended longevity in evolutionarily diverse organisms . To uncover the molecular basis of how altered METC increases lifespan in C . elegans , we performed an RNAi screen and revealed that three predicted transcription factors are specifically required for the extended longevity of mitochondrial mutants . In particular , we demonstrated that the nuclear homeobox protein CEH-23 uniquely mediates the longevity but not the slow development , reduced brood size , or resistance to oxidative stress associated with mitochondrial mutations . Furthermore , we showed that ceh-23 expression levels are responsive to altered METC , and enforced overexpression of ceh-23 is sufficient to extend lifespan in wild-type background . Our data point to mitochondria-to-nucleus communications to be key for longevity determination and highlight CEH-23 as a novel longevity factor capable of responding to mitochondrial perturbations . These findings provide a new paradigm for how mitochondria impact aging and age-dependent diseases . Alterations of mitochondrial function broadly impact animal physiology and physiopathology , including aging and age-related diseases . Correlative evidence has long demonstrated that mitochondrial function gradually declines with age , while oxidative damage and mitochondrial DNA mutations accumulate [1]–[4] . Interestingly , recent studies revealed that reduced mitochondrial electron transport chain ( METC ) function can cause substantial longevity increase in a wide range of organisms . In yeast , some respiration-deficient strains exhibit lifespan increase [5] . In worms , particular METC mutations can greatly extend lifespan . These include mutations in isp-1 , which encodes the iron sulfur protein of Complex III [6] , and in clk-1 , which encodes the hydroxylase protein necessary for the biosynthesis of the METC electron transporter coenzyme Q [7] . Furthermore , RNAi knockdown of several sub-units of the METC also results in greater longevity in worms [8]–[13] and in fruit flies [14] , [15] . In mice , heterozygous loss of the mouse clk-1 homolog ( mclk-1 ) [16] as well as defects in the assembly of the complex IV of the METC [17] extend lifespan . Therefore , the observation that reduced mitochondrial function can prolong lifespan appears highly conserved among evolutionarily diverse species and is likely to be relevant to human physiology . Not surprisingly , METC mutations can also lead to deleterious manifestations such as developmental arrest or shorter lifespan . For instance , mev-1 mutant worms , which harbor a mutation in the subunit C of the Complex II , are characterized by a drastic lifespan shortening compared to wild-type worms [18] . Additionally , genetic manipulations that reduce mitochondrial function are often associated with slower physiological rates , regardless of whether they cause a prolonged or shortened longevity phenotype [5]–[7] , [12] , [14] , [15] , [19] . In many instances , the severity of the mitochondrial perturbations correlates with their effects on lifespan [12] , [20] . A model has emerged in which moderate mitochondrial impairment positively impacts longevity until a threshold is reached , beyond which animal survival is compromised . In yeast , impaired mitochondria can signal the nucleus through a retrograde signaling pathway that leads to nuclear gene expression changes that in turn extend longevity [21]–[23] . Similarly , in mammalian cells , changes in mitochondrial state trigger a retrograde signaling pathway that results in nuclear transcription factors activation and metabolic or stress-related responses ( [24] , [25] and [26] for review ) , but its effect on lifespan is unknown . In general , the molecular mechanisms that enable altered METC to positively impact longevity in multicellular organisms are still unclear . Emerging evidence indicates that longevity extension induced by METC impairment does not correlate with a decrease in oxidative stress response [1] , [3] , [27] or respiratory capacities [6] , [14] , [19] , [28] . Therefore , the mitochondrion is likely to play a causative role in longevity determination via novel mechanisms that are yet to be uncovered . We hypothesized that , similarly to what was shown in yeast , altered METC in C . elegans results in signaling that impinges upon specific transcription factor ( s ) to promote longevity . Our study revealed the identity of several nuclear transcription factors that are specifically important for the longevity increase associated with altered METC in C . elegans , and points to a novel molecular basis by which mitochondria impact longevity in animals . To identify the putative transcription factor ( s ) that mediate the lifespan increase of worms with reduced METC function , we employed a targeted RNAi screening approach using the Transcription Factor RNAi Library ( Gene Service Inc . ) , which covers ∼41% of the predicted transcription factors in C . elegans [29] . We performed the RNAi screen using the isp-1;ctb-1 mitochondrial mutant , which exhibits a robust lifespan increase with only mild delays in rates of developmental processes [6] . We systematically inactivated , by RNAi feeding [30] from the time of hatching , each of the 387 transcription factors in the library and looked for RNAi targets that suppress the prolonged lifespan of the isp-1;ctb-1 mutant worms . From the primary screen , we identified 32 RNAi candidates that caused a decrease in the isp-1;ctb-1 mutant lifespan by at least 15% when compared to empty vector control RNAi ( p≤0 . 001 , log-rank test ) . The 32 primary candidates were then retested in at least two independent trials ( Figure S1 ) . To select the RNAi candidates that consistently suppressed the prolonged lifespan of isp-1;ctb-1 , we used two different methods of data analysis . The percentage of lifespan suppression caused by each RNAi clone was either calculated after pooling the data from the independent trials and comparing the mean lifespan by stratified log-rank test ( p≤0 . 001 ) or averaged after a comparison of mean lifespan within each trial by log-rank-test ( p≤0 . 001 ) ( see Experimental Procedures ) . The results revealed that 17 RNAi candidates consistently shortened the lifespan of the isp-1;ctb-1 mutant by more than 10% ( Tables 1 and S1 ) . Among these RNAi candidates , four correspond to transcription factors previously shown to be important for longevity maintenance in C . elegans: the heat shock factor HSF-1 [31] , the nuclear hormone receptor NHR-49 [32] , and the forkhead factors DAF-16 [33] and PHA-4 [34] . The other 13 candidate transcription factors have not been previously implicated in directly affecting C . elegans longevity . Many transcription factor families are represented within these 13 candidates , with an overrepresentation of the homeobox class ( ∼40% of the candidate clones are homeobox proteins , whereas ∼10% of all the clones tested in the RNAi screen are predicted to be homeobox proteins ) . Under the conditions of our lifespan assays , isp-1;ctb-1 mutants typically exhibit a 1 . 35-fold increase in mean lifespan compared to wild-type ( Figure 1 ) . Thus , RNAi clones that decrease the lifespan of isp-1;ctb-1 mutant worms by ∼30% ( nhr-119 , nhr-265 , ceh-37 , aha-1 , ceh-23 , ZC123 . 3 , ceh-20 , and nhr-25; Figure 1 ) are of special interest since they represent RNAi knockdowns that restore the lifespan of the isp-1;ctb-1 mutant to that of wild-type worms . Although cep-1 , the C . elegans homolog of the tumor suppressor p53 , has previously been shown to be required for the extended lifespan of isp-1 mutant [35] , cep-1 RNAi did not exhibit significant lifespan suppression in our screen . This might be due to cep-1 RNAi only partially knocked down cep-1 in our screen conditions or that cep-1 is not required for longevity increase of isp-1;ctb-1 mutant . We reasoned that if the METC affects lifespan through specific transcription factors , RNAi knockdown of those transcription factors should decrease the isp-1;ctb-1 mutant lifespan to an extent greater than that of wild-type worms or other longevity mutants thought to act independently of mitochondria . To assay the specificity of the transcription factors identified in our primary screen ( Figure 1 and Table 1 ) , we tested the 13 RNAi candidates for an effect on the lifespan of wild-type worms , the short-lived FOXO transcription factor daf-16 mutant [33] , [36] , and the long-lived phosphatidyl inositol 3-kinase age-1 mutant [37] , [38] that exhibits a lifespan increase as robust as that of the isp-1;ctb-1 mutant . Both daf-16 and age-1 represent components of the insulin/insulin-like growth factor signaling ( IIS ) pathway , which is thought to act mostly independently of the METC to affect lifespan in C . elegans [39] . As expected , several of the RNAi candidates ( dve-1; lin-40; nhr-49; ceh-20; lin-11; and nhr-77 ) appeared to non-discriminately shorten lifespan in all strains tested ( Tables 2 and S1 ) , suggesting that the corresponding transcription factors are broadly required for survival . Interestingly , four RNAi clones ( ZC123 . 3; nhr-119; ceh-37; and aha-1 ) affected wild-type and isp-1;ctb-1 mutant worms' lifespan to the same extent but exerted only a moderate or no effect on daf-16 and age-1 mutant longevity ( Tables 2 and S1 ) . Those transcription factors are unlikely to specifically mediate the effects of METC mutations on longevity as their knock-down affected longevity of wild-type and isp-1;ctb-1 worms to a similar extent . Interestingly , four other RNAi clones , targeting the predicted transcription factors C52B9 . 2 , NHR-25 , CEH-23 , and NHR-265 , substantially shortened the lifespan of the isp-1;ctb-1 mutant , but either had a lesser or no effect on the other strains tested including wild-type worms ( Tables 2 and S1 ) , suggesting that the corresponding transcription factors are preferentially required for the longevity of isp-1;ctb-1 mutants . To test whether these four transcription factors ( C52B9 . 2 , NHR-25 , CEH-23 , and NHR-265 ) may contribute to the longevity effect caused by different METC perturbations , we next tested the effects of their RNAi knockdown on the lifespan of two additional long-lived mitochondrial mutant worms , isp-1 and clk-1 , which display a similar degree of lifespan extension ( Figure S1 ) . Among the four candidates tested , nhr-265 is the only one that is specifically required for isp-1;ctb-1 mutant worms to exhibit greater lifespan but not for the other long-lived mitochondrial mutants tested ( Tables 2 and S1 ) . The other three factors ( C52B9 . 2 , ceh-23 , and nhr-25 ) appear to be important for the longevity effects of all three long-lived METC mutant strains ( Tables 2 and S1 ) . We also tested whether RNAi of these four factors affected the lifespan of the short-lived mev-1 mitochondrial mutant worms . Three of the RNAi clones ( nhr-25 , ceh-23 , and nhr-265 ) did not affect the lifespan of mev-1 mutants . C52B9 . 2 RNAi shortened mev-1 mutant lifespan ( −12% ) but to a lesser degree than that of wild-type worms ( −23% ) ( Tables 2 and S1 ) . All together , our data suggest that specific transcription factors play an important role in the longevity of long-lived mitochondrial mutants without affecting the lifespan phenotype of short-lived mitochondrial mutants . Next , as the mitochondrial clk-1 mutation and the genetic mimic of dietary restriction eat-2 mutation have previously been shown to act in a similar genetic pathway [40] , we examined the three transcription factors ( C52B9 . 2 , ceh-23 , and nhr-25 ) that are important for the longevity of the clk-1 mutant ( Tables 2 and S1 ) for an effect on the lifespan of the eat-2 mutant worms . RNAi knockdown of C52B9 . 2 and nhr-25 decreased the lifespan of the eat-2 mutant to a greater extent than that of wild-type worms ( respectively −30% and −44% in eat-2 mutant versus −23% and −8% in wild-type; p≤0 . 001 , log-rank test; Table 2 ) . In contrast , knockdown of ceh-23 had no significant consequence on the lifespan of eat-2 mutant worms ( Table 2 ) . Therefore , all together , our targeted RNAi screen data identified the homeobox protein CEH-23 as a candidate uniquely required for the extended longevity of mitochondrial mutants . Although based on sequence alignment ( BLAST ) , the ceh-23 RNAi clone used in the screen is specific , we constructed three additional RNAi constructs targeting different regions of the ceh-23 gene to rule out possible off-target effects of the ceh-23 RNAi from the Gene Service Library ( Figure S2 ) . All three of the newly generated RNAi constructs significantly ( p≤0 . 001 , log-rank test ) suppressed the longevity of clk-1 , isp-1;ctb-1 , and isp-1 mutant worms to a similar extent and had no effect on the lifespan of wild-type worms , age-1 , daf-16 , and eat-2 mutant worms ( Figure S3 and unpublished data ) . Taken together , our data indicate that knockdown of ceh-23 specifically shortens the lifespan extension of the long-lived mitochondrial mutant worms and is not likely to compromise the general health of the worm . To avoid any pitfalls associated with the RNAi strategy [41] , we next examined the ceh-23 ( ms23 ) mutant , which harbors a deletion that covers 75% of the ceh-23 gene , including half of the homeobox domain ( [42] and Figure S2 ) . While the ceh-23 single mutant exhibited wild-type lifespan [43] , we found that loss of ceh-23 decreased the lifespan of isp-1;ctb-1 mutant by ∼10% and that of isp-1 by ∼20% ( Figure 2 and Table S2 ) . The ceh-23 genetic mutation suppressed the METC mutants' lifespan to a similar degree as the three ceh-23 RNAi constructs we generated in the lab ( Figure S2 ) , but to a lesser degree compared to ceh-23 RNAi construct from the screen library . We purposely performed the screen with a mixture of RNAi colonies for each RNAi construct; thus , it is possible that the ceh-23 RNAi bacteria used in the screen had unexpected off-target effects that caused greater shortening of METC mutant lifespan . Overall , the ceh-23 mutant data corroborated the ceh-23 RNAi results and indicated that ceh-23 inactivation specifically shortens the lifespan extension of the long-lived mitochondrial mutants without compromising the general health of the worm . Taken together , our data highlight a crucial role of CEH-23 in mediating the extended longevity caused by METC mutations . Having established that CEH-23 is required for the prolonged lifespan phenotype of mitochondrial mutants ( Figure 2 ) , we next asked whether CEH-23 also plays a role in the other phenotypes often associated with reduced mitochondrial function , such as slower development rate and reduced self-brood size [6] . We examined the effects of ceh-23 deletion and ceh-23 RNAi inactivation on the development rate and brood size of mitochondrial mutants and wild-type worms . We found that ceh-23 mutants and ceh-23 RNAi treated worms exhibit development rate and brood size indistinguishable from that of wild-type worms ( Table 3 ) . As previously published [6] , isp-1 and isp-1;ctb-1 mutant worms develop substantially slower and produce a much lower brood compared to wild-type worms ( Table 3 ) . Importantly , ceh-23;isp-1 and ceh-23;isp-1;ctb-1 mutants have development rate and brood size similar to those of isp-1 or isp-1;ctb-1 mutants , respectively . Similar results were obtained when ceh-23 was knocked down by RNAi in the isp-1;ctb-1 mutant ( Table 3 ) . These data indicate that CEH-23 is unlikely to participate in the regulation of development rate and brood size in mitochondrial mutants . Whereas increased longevity in C . elegans is often associated with stress resistance [44] , an interesting characteristic of the long-lived mitochondrial mutant worms is that they do not exhibit consistent resistance to different stresses , especially oxidative stress [3] , [8] . In examining different oxidative stress conditions , we noticed that isp-1 mutant worms exhibited different responses to the superoxide-inducing agent paraquat depending on the developmental stage at which they were exposed to the chemical ( Figure S4 ) . We found that isp-1 mutant worms exhibited a better mean survival than wild-type worms when paraquat treatment was initiated at the L4 stage ( Figure S4 and Table 3 ) . Under this condition , loss of ceh-23 had no effect on the mean survival of wild-type or isp-1 mutants worms ( Table 3 ) , indicating that CEH-23 is not required for the paraquat resistance of isp-1 mutant worms under this assaying condition . Taken together , the results suggest that CEH-23 is specifically important for the prolonged longevity , but not the slow rates of development and reproduction , or the oxidative stress resistance associated with the isp-1 mutation . To further characterize CEH-23 , we examined its expression pattern in wild-type and mitochondrial mutant worms . Because an antibody capable of recognizing endogenous CEH-23 is not available , we established multiple independent transgenic lines overexpressing an N-terminal GFP-fused CEH-23 . This construct is likely to reflect the authentic CEH-23 expression pattern as it contains the entire coding region of ceh-23 ( including introns ) , as well as its predicted promoter and 3′-UTR ( Figure S2 ) . Interestingly , the expression of CEH-23::GFP was restricted to a handful of neurons and the intestine of the worm ( Figure 3A–D represent the data obtained in two independent lines ) . Although we have not identified the neurons in which CEH-23 is expressed , the observed neuronal expression of our CEH-23::GFP construct ( Figure 3B and 3C ) resembles that of a previously published GFP construct fused to a partial CEH-23 ( gmIs18[ceh-23::gfp]; [45] , [46] and Figure 3F ) , which was reported to express in the pair of CAN neurons in the central body and 12 sets of sensory neurons in the head ( 10 pairs ) and the tail ( 2 pairs ) . While the neuronal expression of CEH-23 was found in all the transgenic worms , the intestinal expression was only detected in ∼50% of the transgenic worms with a stronger expression in the posterior intestine ( Figure 3D ) . We currently do not understand what determines the mosaicism of the intestinal expression . However , it is of note that intestinal localization of ceh-23 has been previously observed using a promoter ( ceh-23 ) ::GFP fusion [47] . Enriched nuclear localization of CEH-23 was detected in both the neurons and the intestine , consistent with a putative role of CEH-23 as a transcription factor . No systematic differences in CEH-23 expression pattern were detected in isp-1;ctb-1 or isp-1 mutant worms compared to wild-type animals ( unpublished data ) , suggesting that METC mutations are unlikely to affect the tissues nor the cellular compartments in which CEH-23 is expressed . We next compared the expression levels of ceh-23 in wild-type and in long-lived METC mutants using quantitative reverse-transcription PCR ( qRT-PCR ) . Interestingly , we found that ceh-23 mRNA levels were elevated in the isp-1;ctb-1 , isp-1 , and clk-1 mutant worms compared to wild-type worms ( Figure 3E , p≤0 . 05 , Student's t test ) . Our finding is consistent with a recent microarray study indicating that ceh-23 mRNA level is elevated upon certain mitochondrial perturbations [48] . To further corroborate our qRT-PCR data , we compared ceh-23::gfp expression in wild-type and in the isp-1;ctb-1 mutant . We used the gmIs18[ceh-23::gfp] strain ( see above ) for this imaging experiment due to its stable and homogenous expression among worm populations of the same genotype . Using confocal microscopy , we detected a substantially higher level of ceh-23::gfp expression in the isp-1;ctb-1 mutant compared to wild-type worms throughout different developmental stages of the worms ( Figure 3F–G and Figure S5 ) . Our data indicate that METC mutations can lead to increased CEH-23 expression and suggest that CEH-23 is able to respond to altered mitochondrial function ( Figure 4C ) . Since mitochondrial mutant worms require the presence of ceh-23 to maintain their lifespan ( Figure 2 ) and exhibit higher levels of ceh-23 ( Figure 3 ) , we hypothesized that overexpression of ceh-23 might confer a long-lived phenotype , possibly by mimicking induced ceh-23 expression in the mitochondrial mutants . To test this , we established multiple independent transgenic lines overexpressing ceh-23 ( ceh-23 ( o/e ) ) using a construct identical to the one we used for examining CEH-23 expression above , except that it lacks the GFP fusion ( Figure S2 ) . We tested the lifespan of six independent extrachromosomal ceh-23 ( o/e ) lines and observed that three of the lines showed a consistent and significant lifespan increase compared to the co-injection marker only transgenic controls ( Figure 4A and Table S3 , p≤0 . 001 log-rank test ) . In the three other lines tested , overexpressing CEH-23 significantly increased longevity only in half of the experiments ( Table S3 ) . Mosaicism among transgenic lines is commonly observed in C . elegans . The differences in longevity of the different transgenic lines assayed may be due to silencing of the transgene in some of the lines upon propagation [49] and/or different levels of CEH-23 expression in the different lines . To further address this point , we generated transgenic worms expressing lower levels of CEH-23 by transforming worms with 10 times less DNA ( 1 ng/µl instead of 10 ng/µl ) . Overall , the 1 ng/µl lines exhibited only marginal lifespan increase , indicating that the effect of CEH-23 overexpression on the longevity of otherwise wild-type worms appears to be proportional to the quantity of DNA injected into worms ( Figure 4A and Table S3 ) . To further ensure that the lifespan increase phenotype we observed was due to elevated CEH-23 expression , we performed two additional control experiments . First , we treated one ceh-23 ( o/e ) line ( rwEx16 , line 5 ) that consistently showed significant lifespan extension with ceh-23 RNAi and showed that the lifespan increase phenotype was abrogated when ceh-23 was knocked down in the transgenic worms ( Figure S6A ) . Second , we introduced one ceh-23 ( o/e ) line ( rwIs21 ) , which showed no obvious lifespan increase in wild-type background , into the ceh-23;isp-1 mutant background . Overexpression of ceh-23 in this scenario was able to significantly prolong the normally shortened lifespan of the ceh-23;isp-1 mutant worms , suggesting that re-expression of functional ceh-23 was able to revert the lifespan suppression caused by ceh-23 mutation ( Figure S6B ) . Because overexpressing ceh-23 is sufficient to increase wild-type lifespan ( Figure 4A ) , we hypothesized that increased ceh-23 levels contribute to the longevity increase of mitochondrial mutants and that elevated ceh-23 expression and METC mutations increase lifespan through common mechanisms . With such a model , one might expect that overexpressing CEH-23 in METC mutants will not cause further extension of lifespan . To test our hypothesis , we examined the effect of CEH-23 overexpression in isp-1;ctb-1 mutant worms using three independent isp-1;ctb-1;ceh-23 ( o/e ) lines . Two isp-1;ctb-1;ceh-23 ( o/e ) lines further increased lifespan when compared to the isp-1;ctb-1;mec-7::rfp control lines ( Figure 4B and Table S3 , isp-1 ( qm150 ) ;ctb-1 ( qm189 ) ;rwEx16[ceh-23+mec-7::rfp] , lines 4 and 3 versus control isp-1 ( qm150 ) ;ctb-1 ( qm189 ) ;rwEx18[mec-7::rfp] line 3 , p≤0 . 001 , log-rank test ) . Another isp-1;ctb-1;ceh-23 ( o/e ) line either exhibited the same or a shorter lifespan than the isp-1;ctb-1;mec-7::rfp control line ( Table S3 , isp-1 ( qm150 ) ;ctb-1 ( qm189 ) ;rwEx16[ceh-23+mec-7::rfp] , line 1 versus isp-1 ( qm150 ) ;ctb-1 ( qm189 ) ;rwEx18[mec-7::rfp] , line 3 ) . Thus , our data showed that overexpressing ceh-23 can provoke a lifespan extension that is greater than that normally produced by overexpressing ceh-23 in wild-type worms . This suggests that the effect of overexpressing CEH-23 on longevity can be enhanced in isp-1;ctb-1 mutants . One intriguing hypothesis is that there is an optimal elevated level of ceh-23 for maximal longevity increase that was not reached in wild-type worms overexpressing CEH-23 . Since METC mutants already have elevated levels of ceh-23 , the transgene introduced into isp-1;ctb-1 mutants could promote further lifespan increase due to higher levels of ceh-23 . We also noticed that worms overexpressing CEH-23 exhibited similar development rates and brood size as their respective control lines ( unpublished data ) . Thus , CEH-23 plays a role in longevity determination that is independent of any obvious effects on the pace of development of the worms . All constructs were verified by sequencing or restriction fragment length . Sequences of the primers used for PCR are available upon request . The genomic sequence of ceh-23 and the transgenes are illustrated in Figure S2 . RNAi bacteria from the commercial C . elegans Transcription Factors Library ( Gene Service Inc . ) or HT115 carrying the empty vector RNAi L4440 , daf-16 RNAi clone , or age-1 RNAi clone were grown in Luria broth with 50 µg/ml ampicillin at 37°C for 8–12 h and seeded onto 35 mm NGM plates containing 4 mM IPTG , and induced overnight at room temperature . Duplicate plates of each RNAi clones and quadruplicate plates of the empty vector L4440 control RNAi were used in each lifespan assay . Gravid isp-1;ctb-1 mutant worms were allowed to lay ∼30 eggs by plate and the progeny grew on RNAi plates at 20°C until they developed into the young adult stage . The young adult worms were re-fed with 3-fold concentrated RNAi bacteria and 50 µg/ml of 5-fluoro-2′-deoxyuridine ( FUDR ) to prevent the growth of progeny . Starting at day 10 of adulthood , worms were scored every 2–3 d , and those that failed to respond to a gentle prodding with a platinum wire were scored as dead . Worms that died of protruding or bursting vulva or bagged were censored on the day of death . We routinely tested 96 RNAi clones in each independent experiment . Lifespan is defined as the time elapsed from when FUDR was added to the worms ( day 0 of adult lifespan ) until the worms are scored as dead . The survival function of each worm population was estimated using the Kaplan Meier estimator ( SPSS software ) and statistical analysis was done using log-rank test . p≤0 . 001 was considered as significantly different from the control population . RNAi bacteria were freshly transformed before each trial and the bacteria were induced with 4 mM IPTG after reaching an optical density at 600 nm value of 0 . 8 . In some experiments the worms were fed as needed by adding 150 µl bacteria solution to the plates . In some experiments , the worms were transferred every other day into plates seeded with freshly induced bacteria . Using a feeding or a transfer protocol did not significantly change the results . The worms were scored every 1 or 2 d . Each lifespan assay was tested on duplicate or triplicate plates in at least two independent experiments . The independent trials were either analyzed separately as described above or , when appropriate , the data from the independent trials were pooled and compared using stratified log rank test after testing homogeneity among strata ( individually controlled experimental replicates ) . In some cases , the mean variation in lifespan for each strain tested was calculated and compared to wild-type worms for each experiment . The averaged mean variations were analyzed using mixed model analysis with ceh-23 mutation as the fixed effect and experiment as the random effect . Developmental rate and brood size were monitored as previously described [61] . Worms were grown on plates seeded either with OP50 or with RNAi bacteria induced with 4 mM IPTG . Self-brood size: Young adults were singled onto fresh plates incubated at 20°C and transferred onto fresh plates every 12 h to prevent overcrowding until egg laying ceased . The progeny produced on each plate was counted 36 h after removal of the parent . The mean self-brood size obtained for worms of each strain was compared using Student's t test . Time to reach adulthood: 60 synchronized eggs were plated and incubated at 20°C until they reached L4 stage and then scored for vulva formation every 6–8 h . The time elapsed from the egg stage until the stage of complete vulva formation was considered as the time necessary to reach adulthood . The mean number of hours required to reach adulthood obtained for worms of each strain was compared using Student's t test . Similar results were obtained when animals were scored for adult alae appearance . Thirty worms synchronized at the L4 stage or at the first day of adulthood or at day 4 of adulthood were transferred into plates seeded with OP50 bacteria and containing 16 mM paraquat and 50 µg/ml of 5-fluoro-2′-deoxyuridine ( FUDR ) . Worms were grown at 25°C for 3 d ( to avoid vulva bursting ) and then transferred to 20°C . Worms were scored every day . Survival on paraquat was defined as the time elapsed from when paraquat was added until the worms are scored as dead . The survival function of each worm population was estimated using the Kaplan Meier estimator ( SPSS software ) , and statistical analysis was done using log rank test . p≤0 . 001 was considered as significantly different from the control population . Synchronized populations of ∼4 , 000 eggs for each strain tested were either grown to late L4 staged worms ( confirmed by Normaski images of the gonad arms ) at 20°C and harvested or grown to the young adult stage , treated with FUDR , and harvested 4 d later . Total RNA was isolated using Tri-reagent ( Molecular Research Center , Inc . ) [62] . cDNAs were synthesized with random hexamers using SuperScript III First-Strand Kit ( Invitrogen ) . qRT-PCR reactions were performed using iQ SYBR Green Supermix ( BIO-RAD ) and the MyiQ Single Color Real-Time PCR Detection System ( BIO-RAD ) . Melting curve analysis was performed for each primer set at the end to ensure the specificity of the amplified product . act-1 was used as the internal control so that the RNA level of each gene of interest was normalized to the level of act-1 . As an additional control , in each experiment , mRNA expression of tbb-2 was measured to ensure that tbb-2 expression normalized to act-1 did not change between the strains tested . The qRT-PCR experiments were repeated at least three times using independent RNA/cDNA preparations . Quantitative PCR primer sequences are available upon request . For GFP localization , worms at L1–L2 stage were paralyzed with levamisole on an agar pad . The GFP and RFP expressions were visualized at 60× magnification using a Leica DM 5000B microscope . Images were captured using OpenLab software . For GFP quantification , worms at L1–L2 or L4 stage were paralyzed with levamisole on an agar pad . The GFP expression was visualized at 60× magnification using a Leica SP2 confocal microscope . Images were acquired using Leica software . In the GFP channel , images were collected using a z-stack acquisition at 1 µm step interval , with each frame averaged 4 times , and projected in a 20 µm z-stack covering the entire worm . Some of the experiments were performed as double-blind assays . The mean GFP intensities were quantified using Image J and compared between strains using Student's t test . Note: While this paper was in review , several studies were published [63]–[65] that greatly advanced our understanding of the molecular mechanisms by which METC mutations prolong lifespan in C . elegans . Future investigations of how CEH-23 integrates with the new pathways revealed by these studies will provide important new insights into how mitochondria impact animal physiology and longevity .
Mitochondria have long been associated with aging and age-related diseases . Recent research has shown that a slight dampening of mitochondrial function can dramatically increase the lifespan of a wide range of organisms , suggesting that a similar mechanism likely operates in humans . The molecular basis of this observation is largely unknown , however . Uncovering the genes that allow altered mitochondrial function to impact longevity will give us important new insights into how mitochondria affect the aging process and will pave the way for future therapeutic developments aiming to improve healthy aging and to treat age-related diseases . Here , we used an RNAi screen in the genetic model organism C . elegans , a nematode worm , to uncover how altered mitochondrial function can modulate longevity . We found that in order for mitochondria to affect lifespan , they must communicate with several unique transcription factors in the nucleus . Notably , we discovered that the putative homeobox transcription factor CEH-23 , which has not previously been implicated in longevity determination , is able to respond to changes in mitochondrial function and in turn causes an extension in lifespan .
You are an expert at summarizing long articles. Proceed to summarize the following text: Recent evidence demonstrates a role for paternal aging on offspring disease susceptibility . It is well established that various neuropsychiatric disorders ( schizophrenia , autism , etc . ) , trinucleotide expansion associated diseases ( myotonic dystrophy , Huntington's , etc . ) and even some forms of cancer have increased incidence in the offspring of older fathers . Despite strong epidemiological evidence that these alterations are more common in offspring sired by older fathers , in most cases the mechanisms that drive these processes are unclear . However , it is commonly believed that epigenetics , and specifically DNA methylation alterations , likely play a role . In this study we have investigated the impact of aging on DNA methylation in mature human sperm . Using a methylation array approach we evaluated changes to sperm DNA methylation patterns in 17 fertile donors by comparing the sperm methylome of 2 samples collected from each individual 9–19 years apart . With this design we have identified 139 regions that are significantly and consistently hypomethylated with age and 8 regions that are significantly hypermethylated with age . A representative subset of these alterations have been confirmed in an independent cohort . A total of 117 genes are associated with these regions of methylation alterations ( promoter or gene body ) . Intriguingly , a portion of the age-related changes in sperm DNA methylation are located at genes previously associated with schizophrenia and bipolar disorder . While our data does not establish a causative relationship , it does raise the possibility that the age-associated methylation of the candidate genes that we observe in sperm might contribute to the increased incidence of neuropsychiatric and other disorders in the offspring of older males . However , further study is required to determine whether , and to what extent , a causative relationship exists . The effects of advanced paternal age have only recently become of interest to the scientific community as a whole . This interest has likely arisen as a result of recent studies that suggest an association with increased incidence of diseases and abnormalities in the offspring of older fathers . Specifically , offspring sired by older fathers have been shown to have increased incidence of neuropsychiatric disorders ( autism , bipolar disorder , schizophrenia , etc . ) [1]–[3] , trinucleotide repeat associated diseases ( myotonic dystrophy , spinocerebellar atixia , Huntington's disease , etc . ) [4]–[7] , as well as some forms of cancer [8]–[11] . Though these are intriguing data , we know very little about the etiology of the increased frequency of diseases in the offspring of older fathers . Among the most likely contributing factors to this phenomenon are epigenetic alterations in the sperm that can be passed on to the offspring . These studies are in striking contrast to the previously held dogma that the mature sperm are responsible only for the safe delivery of the paternal DNA . Intriguingly , with increased investigation has come mounting evidence that the sperm epigenome is not only well suited to facilitate mature gamete function but is also competent to contribute to events in embryonic development . It has been established that even through the dramatic nuclear protein remodeling that occurs in the developing sperm , involving the replacement of histone proteins with protamines , some nucleosomes are retained [12] . Importantly , histones are retained at promoters of important genomic loci for development , suggesting that the sperm epigenome is poised to play a role in embryogenesis [12] . In addition , recent reports suggest that hypomethylated regions with high CpG density also appear to drive nucleosome retention [13] . Similarly , DNA methylation marks in the sperm have been identified that likely contribute to embryonic development as well [12] , [14] . These data strongly support the hypothesis that the sperm epigenome is not only well suited to facilitate mature sperm function , but that it also contributes to events beyond fertilization . Looking past fertilization and embryogenesis , sperm appear to contribute to events manifesting later in life . The remarkable claim that sperm , independent of gene mutation , may be capable of affecting phenotype in the offspring was initially proposed as a result of large retrospective epidemiological studies observing changes in the frequency of diseases in the offspring of fathers who were exposed to famine conditions in the early 19th century [15] , [16] . Recently , many studies utilizing animal models have discovered similar patterns that comport with the epidemiological data . Specifically , in male animals fed a low protein diet , offspring display altered cholesterol metabolism in hepatic tissue [17] . However , the etiology of this phenomenon is poorly understood . Despite this , there are multiple likely candidates that may drive these effects , such as DNA methylation . Methylation marks at cytosine residues , typically found at cytosine phosphate guanine dinucleotides ( CpGs ) , in the DNA are capable of regulatory control over gene activation or silencing . These roles are dependent on location relative to gene architecture ( promoter , exon , intron , etc . ) . Since these heritable marks are capable of driving changes that may affect phenotype , they represent a possible mechanism to explain the increased disease susceptibility in the offspring of older fathers . Additionally , in both sexes , aging alters DNA methylation marks in most somatic tissues throughout the body . In one of the first large studies to address the question of age-associated methylation alterations , Christensen et al . identified over 300 different CpG loci with age-associated methylation alterations in many tissues [18] . One recent study compared age-associated DNA methylation alterations in blood , brain , kidney and muscle tissue and identified both common and unique methylation alterations between different tissues [19] . Additionally , recent work suggests that DNA methylation can be used to predict the age of an organism based on tissue methylation profiles [20] . This study also supports previous reports which identify global hypomethylation as a hallmark of aging in most somatic tissues [21] . Because of its prevalence in other cell types , age-associated DNA methylation alteration is likely to occur in sperm as well . In further support of this idea is work demonstrating that frequently dividing cells typically have more striking methylation changes associated with age than do cells which divide less often [22] . In this study we have analyzed the age associated sperm DNA methylation alterations that are common among the individuals in our study population to determine the magnitude of sperm DNA methylation changes over time and whether specific regions are consistently altered with age . To assess global methylation in the samples in question we performed pyrosequencing analysis of long interspersed elements ( LINE ) , a commonly used tool for the analysis of global methylation in many tissues [23] , [24] . We identified significant global hypermethylation with age in sperm DNA as previous data from our lab suggests ( Figure 1 ) [25] . Specifically , there was significant hypermethylation with age based on a paired analysis ( p = 0 . 028 ) or by stratifying the samples by age alone and performing linear regression analysis ( p = 0 . 0062 ) . In addition to the global analysis , we performed a high resolution ( CpG level ) analysis of methylation alterations with age . To perform this we utilized Illumina's Infinium HumanMethylation 450K array . Each sample was hybridized and analyzed on an array and the results were compared to detect changes in methylation that are consistent with age . We utilized a sliding window analysis , coupled with regression analysis ( average methylation at identified window versus the age at collection ) as an additional filter ( any window whose regression p-value was >0 . 05 was excluded from downstream analysis ) , to compare changes that are common between paired samples . A Benjamini Hochberg corrected Wilcoxon Signed Rank Test FDR of < = 0 . 0001 and an absolute log2 ratio > = 0 . 2 ( effectively a change in methylation of approximately 10% or greater ) was used as our threshold of significance . Raw FDR values have been transformed for visualization in figures and reference in this text ( ( −10 log10 ( q-value FDR ) ) , such that a transformed FDR value of 13 = 0 . 05 , 20 = 0 . 01 , 25 = 0 . 003 , 30 = 0 . 001 , and 40 = 0 . 0001 . With this approach we have identified multiple age-associated intra-individual regional methylation alterations that consistently occur within the same genomic windows in most or all of the donors screened . Specifically , we identified a total of 139 regions that are significantly hypomethylated with age ( Log2 ratio ≤−0 . 2 ) and 8 regions that are significantly hypermethylated with age ( Log2 ratio ≥0 . 2; Table S1 ) . The average significant window is approximately 887 base pairs in length and contains an average of 5 CpGs with no fewer than 3 in any significant window . Of the 139 hypomethylated regions , 112 are associated with a gene ( at either the promoter or the gene body ) , and of the 8 hypermethylated regions 7 are gene associated . The 8 hypermethylated regions that were found did change in all donor samples , however they did not increase DNA methylation levels beyond 0 . 1 fraction methylation . In one case we identified 3 significantly hypomethylated windows within a single gene ( PTPRN2 ) . Thus there were a total of 110 genes with age-associated hypomethylation . A previous report analyzing multiple somatic tissues suggests that the magnitude of DNA methylation alterations that occurs with age is fairly subtle with an average percent change per year ( measured as slope ) at a single CpG of approximately 0 . 05% to 0 . 15% [19] . Our data , while still subtle , suggest that there may be a stronger effect of age on the methylation alterations in sperm compared with somatic cells . Briefly , in the four tissues screened by Day et al . ( blood , brain , kidney and muscle ) they identified a total of 8 individual CpGs with a methylation change per year of >0 . 4% and a single CpG with a yearly change of >0 . 5% . By comparison , our data have revealed a total of 26 genomic windows ( not just individual CpGs ) whose average fraction methylation change is >0 . 4% per year and 13 genomic windows with an average fraction methylation change per year of >0 . 5% ( Figure 2A–B ) . Specifically in hypermethylated regions , the average fraction methylation change was 0 . 304% per year ( ranging from 0 . 08% to 0 . 95% per year ) . In hypomethylated regions the average fraction methylation change was 0 . 279% per year ( ranging from 0 . 08% to 0 . 92% per year ) . Considering the entire reproductive lifespan of a male , it is not unreasonable to anticipate an average change of 10–12% at these significantly altered regions . Importantly , these alterations all occur in windows with an average initial fraction methylation of <0 . 6 at the first collection and the majority ( 67% of altered regions ) are also considered to have intermediate methylation based on conventional standards ( fraction DNA methylation levels between 0 . 2 and 0 . 8; Figure 2B ) . Despite the increased magnitude of age-associated alterations in sperm when compared to somatic cells these changes are still quite subtle when considering the possible biological impacts at the 119 regions of age-associated alteration that are found at genes ( gene bodies , promoters ) . Gene promoters were defined based on Illumina's array annotation , in general these fall within 1000 bps of the associated gene . The significant loci identified in our analyses are located at various genomic features . The majority of regions that undergo age-associated hypomethylation occurred at CpG shores , whereas hypermethylation events are more commonly associated with CpG islands , and these differences are significant in both cases ( p = 0 . 0015 and p = 0 . 0056 respectively; Figure 2C ) . It should be noted that while we did observe these significant changes there are slight differences in the baseline fraction methylation at islands and shores between regions with hypomethylation events and those with hypermethylation events ( at the highest an absolute fraction methylation change of 0 . 16 ) . We additionally analyzed the co-localization of windows of age associated methylation alterations with known regions of nucleosome retention in the mature sperm , as well as regions where specific histone modifications are found based on previous work from our laboratory [12] . We found that approximately 88% of regions that are hypomethylated with age are found within 1 kb of known nucleosome retention sites in the mature sperm ( Figure 2D ) . Interestingly , loci that are hypermethylated with age are far less frequently found in regions of histone retention , with only approximately 37 . 5% being associated with sites where nucleosomes are found , though there are only 8 regions of significance on which to base this analysis . This difference was significant based on a fisher's exact test ( p = 0 . 002 ) . Similarly , 23% of loci with age-associated hypomethylation are associated with H3K4 methylation and 45 . 3% are associated with H3K27 methylation . The same co-localization is very rare with hypermethylation events ( p = 0 . 0107 ) . Additionally , we analyzed chromosomal enrichment of these marks to determine if there are specific chromosomal regions that are more susceptible to age-related methylation alterations . We found a random distribution of significant age-associated methylation alterations throughout the entire genome with what appears to be enrichment at telomeric and sub-telomeric loci , however this apparent enrichment failed to reach significance ( Figure 3 ) . To confirm our array data we selected 21 regions found to be significant by our array analysis and subjected them to targeted bisulfite sequencing ( on the MiSeq platform ) to confirm that the CpGs tiled on the array reflected the entire CpG content within the windows of interest . Specifically , we amplified via PCR , bisulfite converted DNA from each donor ( young and aged collections ) . The PCR was designed to produce amplicons of approximately 300–500 bp that were located within 21 of the regions of significant methylation alteration we identified by array . Our depth of sequencing was quite robust with an average of 2 , 252 ( SE ±371 . 6 ) reads per amplicon in each sample . The minimum number of average reads for any one amplicon was 313 . In 20 of the 21 gene regions that were analyzed , the array and MiSeq data were similar in both direction and relative magnitude ( Figure 4A ) . In the one case that did not show a similar trend ( hypomethylation with age by array and no change by MiSeq ) the amplicon was outside the region of the two CpGs that drove the significance of the window . When comparing the methylation of the approximately 300 bp amplicon to the CpG tiled on the array in that same region only , and not the array CpGs over the entire 1000 bp window , the data are in agreement . Taken together , the sequencing run confirmed that our array data is a good representation of the methylation status at all CpGs in our regions of interest . To confirm that the sites identified on the array were not only altered in the samples we investigated , but that these loci are also altered with age in the sperm of non-selected individuals in the general population , we have performed an analysis on an independent cohort of individuals from two age groups: young , defined as <25 years of age ( n = 47 ) , and aged , defined as ≥45 years of age ( n = 19 ) . Average age in the young cohort was 20 . 46 years of age ( SE ±0 . 18 ) , and in the aged cohort 47 . 71 years of age ( SE ±0 . 77 ) . We performed a multiplex sequencing run on sperm DNA from these individuals to probe for 15 different regions of interest that were identified with the array data . Briefly , we PCR amplified 15 regions ( using bisulfite converted DNA ) from each individual ( 47 young , and 19 aged ) . The PCR was designed to produce amplicons of approximately 300–500 bp that were located within 15 regions of significant methylation alteration identified by array . Our depth of sequencing was , again , quite robust with approximately 3 , 645 ( SE ±853 . 4 ) reads per amplicon in each sample with a minimum average number of reads for any one amplicon of 263 . From these data we have confirmed that these genomic regions clearly undergo age-associated methylation alterations ( Figure 4B ) . Interestingly , the average magnitude of alteration is also much higher in our independent cohort than in our initial paired donor sample study ( approximately 2 . 2 times greater on average ) . This is particularly remarkable when considering that the average age difference in the independent cohort study was 27 . 2 years , effectively 2 . 3 times greater than the average age difference of 12 . 6 years seen in the paired donor analysis . This further supports our regression data in the paired donor study , which generally suggest a linear relationship of methylation alterations with age at most of the identified genomic loci . To address the question of the dynamics of sperm population changes associated with the approximately 0 . 281% change per year identified in this study we subjected our next generation sequencing data from the paired donor samples to a novel analysis where we compared the sperm population shifts between the young and aged samples . Because the MiSeq platform produces data for each single nucleotide sequence ( each representing the methylation status in a single sperm ) we are able to determine average methylation at each region for all of the amplicons analyzed . We identified 3 general patterns in methylation profile population shifts that resulted in the age–associated methylation alterations we identified . First , we identified regions whose methylation at an age <45 was strongly hypomethylated , and the methylation profile in individuals >45 years of age is virtually the same , though it is more strongly hypomethylated . In these cases the change is still strikingly significant , but the magnitude of fraction DNA methylation change is minimal . Second , we see a single population in samples collected at <45 years of age that is shifted toward more hypomethylation in samples collected at >45 years of age . Last , we identified a bimodal distribution in samples collected <45 years of age that , in samples >45 years of age , is stabilized into a single population ( Figure 5 ) . This could be indicative of at least two sperm subpopulations , which are biased to a single , more hypomethylated sperm population with age . In every case the results suggest that all of the alterations we detected with the array are the result of the entire sperm population being altered in similar subtle ways and not a result of a dramatic alteration in a small portion of the sperm population . The genes affected by the age associated methylation alterations ( those that have alterations that occur at their promoter , or gene body ) were analyzed by Pathway , GO and disease association analysis . The results indicate that no one GO term or Pathway is significantly altered in our gene group . Similarly , there were no significant diseases or disease classes associated with the genes identified in this study based on results of the disease association tool on DAVID . However the most significant disease hits ( those that were significant prior to multiple comparison correction ) have both been suggested to have increased incidence in the offspring of older fathers , namely myotonic dystrophy and schizophrenia [2] , [7] . To directly investigate the disease association ( s ) in our set of genes we searched the National Institute of Health's ( NIH ) genetic association database ( GAD ) . We investigated all 117 genes that were determined to have age associated methylation alterations ( 110 hypomethylated; 7 hypermethylated ) for their various disease associations . From these a total of 46 genes have been confirmed to be associated with either a phenotypic alteration or a disease based on GAD annotation . We identified 4 diseases that were most commonly associated with our set of genes ( those disease that are associated with at least 2 genes identified in our study; diabetes mellitus , hypertension , bipolar disorder and schizophrenia ) . To further investigate these associations , we analyzed the frequency of genes associated with these 4 diseases in our gene set and compared it to their frequency in all 11 , 306 genes known to be associated with either a phenotypic alteration or a disease . Only bipolar disorder appeared to be more frequently associated with our identified genes than the background set of genes , based on chi-squared analysis with multiple comparison correction ( Bonferonni ) of the 117 age associated genes identified in our analyses ( p = 0 . 012 ) . Interestingly , schizophrenia also appeared to trend toward increased frequency ( p = 0 . 07; figure 6 ) . However , it is important to note that these are not considered significant enrichments if considering correction for comparisons with all genes in the genome ( omitting the filter for a disease connection ) . The frequency of genetic association between our gene set and the background gene set was statistically similar for both hypertension and diabetes mellitus . To investigate the attributes of regions that we determined to be most susceptible to methylation alterations , we evaluated the co-localization of significantly altered loci in our study with regions of nucleosome retention in the mature sperm . It appears that hypomethylation events are most commonly associated with sites of nucleosome retention . It should be noted that our criteria for sites of nucleosome retention is simply that our sites of alteration occur within 1 kb of known retention sites and thus there may be a greater degree of complexity in the actual sites of methylation alteration than we have identified . The actual nature of methylation patterns at a higher resolution in these regions ( whether the affected regions are flanking or directly associated with histones ) is difficult to elucidate due to the nature of array tiling in many of the loci we identified . Interestingly , this same co-localization was not seen with hypermethylation events . Though co-localization patterns are significantly different between the hypomethylation and hypermethylation events , it should be noted that the sample size is quite small in the hypermethylation group ( 8 significant windows ) . It should also be noted that while the co-localization of histones and the hypomethylation events we observed in our study are significant , the methylation marks observed are likely established earlier in spermatogenesis and thus may not be affected by the nucleosome architecture in the fully matured sperm . In addition , the alterations identified in this study are not localized everywhere that histones are retained , thus nucleosome retention alone can't be the independent driving force of regional susceptibility to methylation alterations . It should be further noted that our approach was not targeted to observe changes in chromatin co-localization patterns and as such this represents a secondary analysis of these patterns with the use of a “promoter array . ” As a result of observing only a selected portion of the genome , there are clear biases that are introduced that should be taken into account when considering these findings . Recent literature suggests an interesting hypothesis of “selfish spermatogonial selection” that may have application in this study as well [29] . Briefly , the hypothesis states that some gene mutations that are causative of abnormalities in the offspring are beneficial to spermatogenesis and become enriched throughout the aging process in spermatogonial stem cells . Thus , sperm carrying these mutations become more frequent in the population to the detriment of the offspring . Similarly , it is possible that the age-associated methylation alterations we have identified may be in regions that are important to spermatogenesis and thus would be selected for . While the genes identified herein are not well known spermatogenesis hotspots , they may lie close to other genes that are important in development and thus may be subject to a looser chromatin state leaving these genes more susceptible to methylation perturbations . The hypomethylation events we identified could occur as a result of either active or passive demethylation . For example , regional transcription activity at loci important in spermatogenesis would likely be accompanied by a relaxed chromatin structure that could result in increased frequency of DNA damage over time . Established methylation marks located within this region could then be passively removed through repair mechanisms in the developing sperm . If the removal of this mark is either beneficial or has no effect on spermatogenesis it will persist , and over time similar marks could accumulate at nearby CpGs ultimately leading to the profile we identified in our study . It should also be noted that the accumulation of de novo mutations could lead to a similar profile . It is clear that the number of mutations in the sperm increase with age , and if these mutations involve deamination of cytosine residues the resulting sequence could appear as a loss of methylation with the technologies utilized herein . However , the mutation load , and specifically these C to T transitions , in sperm are stochastic in nature and thus cannot be the primary driving factor for the genomic hotspots of age-associated hypomethylation seen in virtually all of the individuals screened [30] . Alternatively , active enzymatic removal of methylation marks in the sperm might drive age-associated methylation changes . For this to be mechanistically plausible we would have to assume that hypomethylation in the windows we identified is always beneficial to spermatogenesis . While either of these mechanisms is plausible , it is likely that the effects we have identified involve some combination of both . The mechanics of hypermethylation events with age are more difficult to elucidate , as this , by definition , has to be an active targeted process involving methyltransferase enzymes . However , some evidence from this study indicates DNA sequence may be an important driver of age-related hypermethylation . Of the 7 windows that we identified with gene-associated hypermethylation with age , 4 are associated with the FAM86 family of genes that are categorized not by protein function or genomic location but sequence similarity . This strongly suggests that , at least in part , age associated hypermethylation events at specific loci are driven , either directly or indirectly , by DNA sequence . Interestingly , this family of genes ( FAM86 ) with unknown function has recently been categorized with a larger family of methyltransferase genes , though it remains unclear what the FAM86 target ( s ) is/are ( DNA , Histone , other proteins , etc . ) . It is important to note that in addition to these regional hypermethyaltion events , globally DNA methylation is markedly increased as well . The possible role of chromatin modifications ( histone tail modifications , etc ) in this process is also important to note , as what we have identified may be linked to regional histone methylation , acetylation , etc . Such histone modifications may reflect underlying transcriptional changes during spermatogenesis . Taken together , the mechanisms that drive age-related methylation alterations in the sperm remain elusive , but likely involve both active and passive methylation modification . It is important to consider two questions in determining the biological impacts of the identified methylation changes in this study . First , are the methylation changes described herein capable of transcriptional alterations ? Second , are these methylation changes capable of avoiding embryonic methylation reprogramming ? Regardless of the mechanism by which these methylation marks are altered in the sperm over time , it is striking that these changes occur with such consistency between individuals and have such a tight association with age that was seen in both the paired donor analysis and the independent cohort analysis . This is in stark contrast to the relative stability of the sperm methylome seen over time within each individual in the majority of the genome . One limitation of these findings , however , is the magnitude of alterations we have discovered . As described earlier the average fraction methylation alteration per year was approximately a change of 0 . 281% . Though this seems relatively small , when expanded to include the possible reasonable reproductive years in a male the change would be 10–12% . The increased magnitude of change with increasing age is strongly supported by our independent cohort study where an increase in the age difference between two groups was directly correlated with an increase in the magnitude of methylation alterations at virtually every locus screened in a relatively linear manner . Importantly , based on our analysis of complete nucleotide sequences from our sequencing data it appears that this decrease of 10–12% reflects changes to random CpGs within windows of susceptibility in all sperm , which would manifest in an individual sperm as a mosaically methylated region . The resultant 10–12% change in methylation within every individual sperm ( effectively 1 out of every 10 CpGs are demethylated ) suggests that every sperm carries similar , more subtle , alterations within these regions on average . It is important to note that because we only investigated a portion of the regions of interest in our sequencing run ( used for confirmation of array results ) and the amplicons we probed made up only a portion of the regions of interest , we can not make a definitive overarching statement about the dynamics of methylation profile population shifts in sperm as a result of age . Despite this , the consistency of population shifts in the regions we were able to observe suggests that other regions of interest would likely follow similar patterns . Regardless , the resultant age-associated epigenetic landscape alterations may contribute to disease susceptibility in the offspring despite the small degree of change though the increased risk to the offspring may be relatively small . Figure 7 illustrates the alterations seen at two representative loci from our analysis , Dopamine receptor D4 ( DRD4; ENSG00000170956 ) and tenascin XB ( TNXB; ENSG00000168477 ) . The heritability of such marks is more difficult to elucidate mainly because the current study does not directly address this question . However , this issue needs to be addressed as the identified age-associated methylation alterations in the mature sperm may be removed through the embryonic demethylation wave . Despite the fact that there is no direct evidence of methylation alteration heritability at the specific loci presented in this work , the observed age-associated changes at regions known to be of significance in diseases with increased incidence in the offspring of aged males is striking and warrants further study . The intriguing localization of these alterations suggests that the methylation profile in the mature sperm , at specific loci , either contributes to the increased incidence of associated abnormalities in the offspring or that they reflect ( are downstream of ) changes that are actually causative of the associated abnormalities in the offspring . Moreover , it has been previously proposed that epigenetic alterations are among the most likely candidates to transmit such transgenerational effects , and we have identified methylation alterations that appear capable of contributing to the various pathologies associated with advanced paternal age . Despite this , future work must still be performed to determine the real impact these marks have on transcription and thus phenotype and disease . Taken together , these subtle yet highly significant , age-associated alterations to the sperm methylation profile are intriguing because of their location and consistency , but more work is required to elucidate the biological impact of these marks . There are many genes identified in our study that , if biologically affected , may result in pathologies in the offspring . DRD4 is one of the most widely implicated genes in the pathology of both schizophrenia and bipolar disorder as well as many other neuropsychiatric disorders [31] , [32] . Interestingly , the entire DRD4 gene itself is hypomethylated with age ( Figure 7 ) . TNXB has also been suggested to be associated with schizophrenia based on multiple studies , though the data are controversial [33] , [34] , and virtually the entire 1st exon of TNXB is hypomethylated with age . Additionally , DMPK ( ENSG00000104936 ) , a gene identified in our study , is known to be associated with myotonic dystrophy , a disease for which advanced paternal age is a risk factor [7] . In fact , increases in trinucleotide repeats in DMPK are believed to be the cause of myotonic dystrophy type 1 . Importantly , previous data suggests that altered methylation marks may affect trinucleotide instability [35] . These examples represent only a portion of the genes that were identified in our study and support the hypothesis that age-associated DNA methylation alterations in sperm may play a role in the etiology of various diseases associated with advanced paternal age . There are two important findings in this study . First , that there are any age-associated alterations common among such a varied study population ( in terms of the age at collection ) is remarkable . Specifically , age-associated methylation alterations occur in the sperm regardless of whether the ages between collections are approximately 20 to 30 years of age or 50 to 60 years of age . Second , the increased frequency of genes associated with bipolar disorder and schizophrenia in our study when compared to all genes associated with disease provides intriguing insight into the increased susceptibility of these specific disorders in the offspring of older fathers . Though frequently hypothesized , this work comprises , to the best of our knowledge , the first direct evidence suggesting the plausibility of epigenetic alterations in the sperm of aged fathers influencing , or even causing , disease in the offspring . Because of the nature of the unique sample set we have utilized in this study future work is needed to directly address a number of questions . Are methylation alterations , similar to those seen in our study , causative of neuropsychiatric disease ? Can the methylation marks we observe in our study avoid embryonic demethylation ? Future targeted work is required to directly address these questions to enable us to determine the role that these altered methylation marks play in the increased incidence of various diseases seen in the offspring of older fathers . The Institutional Review Board at the University of Utah approved this study . Written informed consent was obtained from all participants for their tissues to be utilized for this work . Under an Institutional Review Board approved study our laboratory has accessed samples from 17 sperm donors ( of known fertility ) that were collected in the 1990's . These donors provided an additional semen sample in 2008 , enabling the evaluation of intra-individual changes to sperm DNA methylation with age . These samples are referred to as young ( 1990's collection ) and aged ( 2008 collection ) samples . The age difference between each collection varied between 9 and 19 years , and the age at first collection ( “young” sample ) was between 23 and 56 years of age . At every collection donors were required to strictly follow the University of Utah Andrology Laboratory collection instructions , which includes abstinence time of between 2 and 5 days . The whole ejaculate ( no sperm selection method was employed ) collected at each visit was frozen in a 1∶1 ratio with Test Yolk Buffer ( TYB; Irvine Scientific , Irvine , CA ) and stored in liquid nitrogen prior to DNA isolation . Samples were thawed and the DNA was extracted simultaneously to decrease batch effects . Sperm DNA was extracted with the use of a sperm-specific extraction protocol used routinely in our laboratory [36] . Briefly , sperm DNA was isolated by enzymatic and detergent-based lysis followed by treatment with RNase and finally DNA precipitation using isopropanol and salt , with subsequent DNA cleanup using ethanol . To ensure the absence of potential contamination from somatic cells the samples were visually inspected prior to DNA extraction . Additionally , we analyzed our sequencing results in an attempt to identify reads that did not match the methylation profile of sperm but instead reflected that of leukocytes . We also analyzed imprinted regions from our array data in an attempt to identify fraction methylation values that were inconsistent with previous reports of sperm DNA methylation patterns . Specifically , at a region of the IGF-2 locus that is tiled on the 450K array , it has been previously shown that sperm DNA is strongly hypermethlyated with a fraction methylation of approximately 0 . 8–0 . 85 and in leukocytes this same region is strongly demethylated with a fraction methylation of <0 . 1 [37] . Our array data indicate average methylation in every sample screened at these sites is approximately 0 . 844 . In summary , with neither approach did we identify any signal that indicated leukocyte or other somatic cell contamination . Each sample was subjected to pyrosequencing analysis of a portion of the LINE-1 repetitive element for the purpose of confirming previously determined global methylation changes with age . Briefly , isolated sperm DNA samples were submitted to EpigenDx ( Hopkinton , MA ) for pyrosequencing analysis . Qiagen's PyroMark LINE1 kit was used to determine methylation status at each CpG investigated with the assay . The experiment was performed based on manufacturer recommendations . The resultant values for each CpG are reported as fraction methylation , or the percent of methylated cytosines at that specifc CpG position . The average of these values was calculated for each individual ( young and aged ) , and the values were compared both by linear regression and by a paired t-test . Each of the paired samples for the 17 donors ( a total of 34 samples ) was subjected to array analysis using the Infinium HumanMethylation 450 Bead Chip micro-array ( Illumina , San Diego CA ) . Extracted sperm DNA was bisulfite converted with EZ-96 DNA Methylation-Gold kit ( Zymo Research , Irvine CA ) according to manufacturer's recommendations . Converted DNA was then hybridized to the array and analyzed according to Illumina protocols at the University of Utah genomics core facility . Once scanned and analyzed for methylation levels at each CpG a β-value was generated by applying the average methylated and unmethylated intensities at each CpG using the calculation: β-value = methylated/ ( methylated+unmethylated ) . The resultant β-value ranges from 0 to 1 and indicates the relative levels of methylation at each CpG with highly methylated sites scoring close to 1 and unmethylated sites scoring close to 0 . The raw data were subjected to normalization to ensure the removal of poorly performing probes from the downstream analysis ( probes with a QC p<0 . 05 ) . Batch effect correction and basic descriptive analyses of the microarray data were performed using Partek ( St . Louis MO ) . More in depth analysis was performed using the USeq platform with the application Methylation Array Scanner which identifies regions of altered methylation that are common among individuals utilizing a sliding window analysis . Briefly , paired data from each donor ( young and aged ) was subjected to a 1000 base pair sliding window analysis where regions of altered methylation with age that are common among donors were called by Wilcoxon Signed Rank Test . To diminish the influence of outliers in the data set , methylation for a specific window was reported as a pseudo-median and differences between the young and aged sample are reported as log 2 ratios . Two thresholds were applied to identify windows with significant differential methylation . A Benjamini Hochberg corrected Wilcoxon Signed Rank Test FDR of < = 0 . 0001 ( > = transformed FDR of 40 ) and an absolute log2 ratio > = 0 . 2 was used as our threshold for significance . Raw FDR values were transformed for visualization in figures and reference in this text ( ( −10 log10 ( q-value FDR ) ) , such that a transformed FDR value of 13 = 0 . 05 , 20 = 0 . 01 , 25 = 0 . 003 , 30 = 0 . 001 , and 40 = 0 . 0001 , etc . We selected this robust level of significance , as opposed to an FDR of > = 13 ( corrected p-value of 0 . 05 ) , to ensure that we selected only the most striking alterations that are consistently perturbed in most or all of the individuals screened . To confirm the significance of each of the called windows we subjected the mean β-value within the window for each donor ( young and aged samples ) to a paired t-test . Following this initial filter we additionally subjected each significant window to a regression analysis ( age at time of collection versus average methylation within significant windows ) to determine the relationship between age and mean methylation within each window . Regression analysis and paired t-tests were performed using STATA 11 software package . A p-value of <0 . 05 was considered significant for these analyses . We performed multiplex sequencing in a replication cohort as a confirmation that the alterations identified in the paired donors via array represent methylation alterations that are common in human sperm with age . First , each donor sample used in the array study was additionally subjected to targeted bisulfite sequencing at loci determined to be most consistently altered based on the window analysis . This step was designed to confirm the array results and to provide greater depth of coverage of the CpGs in the windows of interest . Primers for 21 loci were designed using MethPrimer ( Li Lab , UCSF ) . PCR was performed on samples following sperm DNA bisulfite conversion with EZ-96 DNA Methylation-Gold kit ( Zymo Research , Irvine CA ) . PCR products were purified with QIAquick PCR Purification Kit ( Qiagen , Valencia CA ) and were pooled for each sample . The pooled products were delivered to the Microarray and Genomic Analysis core facility at the University of Utah for library prep which included shearing of the DNA with a Covaris sonicator to generate products of approximately 300 base pairs , in preparation for 150 bp paired end sequencing , and the addition of sample-specific barcodes for all 34 samples . Multiplex sequencing was then performed on a single lane on the MiSeq platform ( Illumina , San Diego CA ) . Second , 19 sperm DNA samples from an independent , unselected cohort of general population donors who were ≥45 of ages were selected and compared to 47 sperm DNA samples from general population donors who were <25 years of age . These samples underwent the same preparation as described above for multiplex sequencing , though only 15 amplicons were targeted in this study of larger sample size . Average fraction methylation for each window was determined and was subjected to unpaired t tests between the young and aged groups . Bisulfite sequencing data was aligned against the human reference genome Hg19 using Novoalign . The aligned reads were processed using Novoalign Bisulfite Parser , BisStat and Parse Point Data Context for CG from the USeq package . The binned CpG graphs were generated using a modified version of the Allelic Methylation Detector from the USeq package . In short , all reads were queried for their number of CpGs . A consensus CpG number was then taken based on the highest number of CpGs per read and a minimum of 10% of all aligned reads ( approximately 100 reads per region ) must cover said number of CpGs . The consensus CpG number then served as the basis for the number of bins per region . Samples that were donated at an age of 45 years or older were coalesced in silico in the “aged donor group” . Conversely , samples younger than 45 years were grouped in the “young donor group” . All reads for the consensus CpG count were summed up based on their age group and then normalized to a 100 reads total . The graphs plotting normalized reads to methylation bins were then generated using the spline function from the R package . GO term Analysis was performed with Gene Ontology Enrichment Analysis and Visualization Tool ( GOrilla; cbl-gorilla . cs . technion . ac . il ) . Pathway and disease association analysis was performed on the Database of Annotation , Visualization , and Integrated Discovery ( DAVID; david . abcc . ncifcrf . gov ) v6 . 7 . Additional disease association analysis was performed directly on the National Institute of Health's Genetic Association Database ( GAD; geneticassociationdb . nih . gov ) . Fishers exact test was used to determine the differences in frequencies of genes associated with particular diseases between our significant gene group and a background group . This analysis was also used to detect the differences in frequencies of windows that were found in regions of histone retention in the hypomethylation group and the hypermethylation group . Additionally , regression analysis was utilized to determine relationships between age and methylation status at various loci . STATA software package was used to test for significance with a p<0 . 05 being considered a significant finding .
There is a striking trend of delayed parenthood in developed countries due to secular and socioeconomic pressures . As a result , physicians commonly consult with concerned patients inquiring about the impact of advanced age on their ability to conceive healthy offspring . The concern has more frequently surrounded the effects of advanced maternal age , but recent evidence suggests negative effects of advanced paternal age as well . Specifically , studies have demonstrated increased incidence of neuropsychiatric and other disorders in the offspring of older males . In this study we have investigated a commonly hypothesized mechanism for this effect , namely sperm DNA methylation alteration . Our data indicate that specific genomic regions of DNA methylation are commonly altered with age , suggesting that some regions of the sperm genome are more susceptible than others to age-related epigenetic changes . Importantly , a significant portion of these alterations occur at genes known to be associated with schizophrenia and bipolar disorder , both of which display increased incidence in the offspring of older fathers . These data will be important in driving future studies aimed at determining the impact that these methylation alterations may have on offspring health and will thus enable couples at advanced reproductive ages to be more informed of possible risks .
You are an expert at summarizing long articles. Proceed to summarize the following text: Many taxonomically diverse prokaryotes enzymatically modify their DNA by replacing a non-bridging oxygen with a sulfur atom at specific sequences . The biological implications of this DNA S-modification ( phosphorothioation ) were unknown . We observed that simultaneous expression of the dndA-E gene cluster from Streptomyces lividans 66 , which is responsible for the DNA S-modification , and the putative Streptomyces coelicolor A ( 3 ) 2 Type IV methyl-dependent restriction endonuclease ScoA3McrA ( Sco4631 ) leads to cell death in the same host . A His-tagged derivative of ScoA3McrA cleaved S-modified DNA and also Dcm-methylated DNA in vitro near the respective modification sites . Double-strand cleavage occurred 16–28 nucleotides away from the phosphorothioate links . DNase I footprinting demonstrated binding of ScoA3McrA to the Dcm methylation site , but no clear binding could be detected at the S-modified site under cleavage conditions . This is the first report of in vitro endonuclease activity of a McrA homologue and also the first demonstration of an enzyme that specifically cleaves S-modified DNA . The sequence of the DNA bases contains the genetic information that is copied with great accuracy and inherited by successive generations . DNA may also carry so called epigenetic modifications that are added by host enzymes after replication . These modifications are lost or changed after transfer of the DNA to a new host by conjugation , transformation or transfection . Epigenetic modifications can also change in response to changing environmental conditions , and they are generally important for eukaryotic gene regulation including carcinogenesis [1] . In bacteria , epigenetic modifications have more specialized roles including the protection of self DNA against restriction endonucleases ( REases ) which cleave foreign , differently modified DNA . Potential invaders , such as bacteriophages , have developed DNA modifications and other measures to overcome these restriction barriers , and bacteria have evolved to restrict even the modified foreign DNA . One such strategy is for bacteria to restrict methylated DNA [2] . There are many methyl-specific REases . Some cleave DNA at specific methylated recognition sequences . Others make contact with their target DNA at a specific methylated recognition sequence , and then move along the DNA before cleaving at an undefined site . These Type IV methyl-specific REases are very diverse in their amino acid ( aa ) sequences , and they may consist of one or several peptides [3] . There are about 1303 putative Type IV REases in REBASE ( http://rebase . neb . com ) , but only 3 have been biochemically characterized , whereas others are predicted based on bioinformatic analysis of DNA sequences . Quite unusually , some Type IV REases recognize methylated and also hydroxymethylated or glucosyl-hydroxymethylated DNA [4] , [5] . We have discovered a Type IV REase from a bacterium of the genus Streptomyces that cleaves methylated and phosphorothioated ( S-modified ) DNA that has a non-bridging oxygen of the phosphate group in the DNA backbone replaced by sulfur . The DNA S-modification is sequence specific , and a dnd ( DNA degradation ) gene cluster encoding four or five proteins is responsible for the S-modification [6] , [7] . Streptomycetes are filamentous soil bacteria that produce many chemically diverse antibiotics . The 8 . 7 Mb linear genome of Streptomyces coelicolor A3 ( 2 ) has been fully sequenced and annotated [8] . S . coelicolor contains at least four methyl-specific restriction endonucleases that restrict ( reduce or prevent ) the introduction of methylated DNA e . g . from Dam+ Dcm+ Hsd+ E . coli K-12 strains [9] . Therefore , DNA is generally passaged through a non-methylating dam dcm hsd E . coli host before introduction into S . coelicolor [10] , [11] . Alternatively , the less restricting Streptomyces lividans 66 has been used as a recipient for methylated DNA from E . coli K . The genes of S . lividans and S . coelicolor are very similar and highly syntenous except for several genomic islands ( GIs ) , some of which are mobile elements [12] , [13] . One of these methyl-specific endonucleases , ScoA3McrA ( Sco4631 ) [4] is the focus of this report . As shown schematically in Figure 1 , ScoA3McrA is located on the 17 . 1 kb mobile element Gi11 , which is also known as the conjugative and integrative plasmid SLP1 [14] . The Type IV REase EcoKMcrA restricts DNA methylated at the sequences C5mCGG and has been shown to bind to this sequence , but in vitro DNA cleavage has not been observed [15]–[17] . Sequence comparison and phylogenetic analysis showed that the ScoA3 and EcoK McrA proteins have very little aa sequence similarities apart from the region surrounding the conserved HNH motif ( Figure S7B ) . About one in 6000 phosphate groups of the backbone of S . lividans genomic DNA contain sulfur instead of a non-bridging oxygen at specific sequences [7] , [18] . The stereospecific S-modification ( phosphorothioation ) renders purified DNA susceptible to oxidative double-strand cleavage by Tris peracid which is generated at the anode during electrophoresis [19] . Five genes , dndA-E located on the apparently non-transmissible 93 kb SLG genomic island [13] , are involved in DNA S-modification . Evidence for DNA S-modification has been discovered in several Streptomyces species , and in phylogenetically diverse prokaryotes including several economically important species [13] , [20] . The DNA S-modification of S . lividans does not seem to be accompanied by a cognate restriction endonuclease that could defend the strain against the invasion of bacteriophages that lack this modification , and S . lividans has traditionally been used as a permissive host for the isolation of many Streptomyces phages [11] . In this report we show that S-modified DNA ( as well as methylated DNA ) is restricted in vivo by the S . coelicolor methyl-specific endonucleases ScoA3McrA . We also report site-specific in vitro cleavage of S-modified DNA by the purified enzyme . This is the first indication of a biological role of DNA S-modification , and also the first report of in vitro DNA cleavage by a McrA homologue . The dndA-E gene cluster of S . lividans is responsible for the DNA S-modification ( phosphorothioation ) of this strain [6] . We wanted to express this gene cluster in S . coelicolor which is closely related to S . lividans but lacks these particular genes . In order to generate a stable recombinant S . coelicolor strain , we cloned the dndA-E gene cluster into the φC31-derived integrative vector pSET152 which has an apramycin resistance marker for selection , and an origin of transfer for highly efficient conjugative transfer from E . coli ET12567/pUZ8002 ( dam dcm hsd tra+ ) to S . coelicolor M145 . The plasmid vector without cloned DNA ( pSET152 ) produced as expected c . 105 apramycin resistant S . coelicolor exconjugants per experiment ( one Petri dish ) . Unexpectedly , pSET152::dndA-E ( pHZ1904 ) produced only an average of nine apramycin resistant exconjugants per Petri dish ( Figure 2 , Panel 1 ) . These apramycin resistant pSET152::dndA-E exconjugants were expected to express the dnd genes and generate S-modified S . coelicolor DNA that is visibly degraded during agarose gel electrophoresis ( methods used in [21] ) . Only 49 out of 100 exconjugants showed the expected DNA degradation ( Dnd+ phenotype; Dnd stands for DNA degradation ) . The remaining 51 contained stable , S-free DNA ( Dnd− phenotype , Figure S1A ) . The dndA-E gene cluster of three Dnd− exconjugants was analysed and each contained a different mutation that was likely to abolish the S-modification activity ( Figure S1B ) . One of these plasmids , pHZ1904* contained the entire dndA-E gene cluster with a single base insertion ( frameshift mutation ) in dndE which is required for DNA S-modification ( Figure S1B ) . pHZ1904* was excised in circular form from S . coelicolor and introduced into non-methylating E . coli ET12567/pUZ8002 . In interspecific matings pHZ1904* produced about 3×104 apramycin resistant S . coelicolor M145 exconjugants , almost as many as pSET152 without cloned DNA ( Figure 2 , Panel 1 ) . We speculated that S-modification of S . coelicolor DNA by the intact dndA-E gene cluster was not tolerated because of a hypothetical S . coelicolor endonuclease that restricts S-modified DNA . The above 51 Dnd− exconjugants would therefore all contain a mutant , inactive dndA-E gene cluster , and the 49 S-modified ( Dnd+ ) exconjugants may have kept the dndA-E gene cluster intact but may have lost the hypothetical S . coelicolor sulfur-specific REase . We therefore set out to cure the integrated pSET152::dndA-E from one of the 49 apramycin resistant Dnd+ S . coelicolor exconjugants and expected that the resulting strain would be a mutant that allows the reintroduction of fresh pSET152::dndA-E at high frequency . Unfortunately , we could not detect spontaneously apramycin sensitive derivatives of S . coelicolor pSET152::dndA-E . Instead , we cloned the dndA-E cluster into the highly unstable autonomously replicating Streptomyces plasmid pJTU412 [22] to generate pJTU1651 . Introducing pJTU1651 into S . coelicolor M145 by conjugation from E . coli produced thiostrepton resistant Dnd+ exconjugants . One of these was randomly selected and plated on non-selective medium to allow loss of pJTU1651 . LG3 , one of the cured ( thiostrepton sensitive , Dnd− ) S . coelicolor gave c . 5×104 apramycin resistant exconjugants both with pHZ1904 and with pHZ1904* ( Figure 2 , Panel 2 ) . As expected , the pHZ1904 exconjugants were Dnd+ . These results proved that the process of introducing the dndA-E gene cluster into wild-type S . coelicolor selected for rare mutant derivatives that no longer restricted the establishment of this gene cluster . Several putative endonucleases genes had been identified in the S . coelicolor genome sequence [12] . The identification of the correct one was aided by the availability of the complete genome sequence of Streptomyces avermitilis which contains dndA-E homologues and produces S-modified DNA like S . lividans [23] . We were thus searching for a putative endonucleases gene of S . coelicolor that does not have a counterpart in S . avermitilis and found Sco4631 which is encoded by the genomic island Gi11 ( also known as SLP1 ) and known to be absent from S . lividans [23] . Sco4631 was shown to restrict methylated DNA in vivo in Streptomyces [9] and is listed in REBASE as ScoA3McrA because it has limited similarity ( 37% identity including a HNH endonucleases motif in a 91 aa overlap ) to EcoKMcrA from E . coli which restricts methylated and hydroxymethylated DNA in vivo , but has not been demonstrated to cleave DNA in vitro [4] . The following two experiments tested whether Sco4631 ( ScoA3McrA ) was indeed the gene that prevented the establishment of pHZ1904 in S . coelicolor: single crossover gene disruption using an internal fragment of sco4631 on a suicide vector plasmid ( thiostrepton resistance ) was used to produce S . coelicolor LG4 ( Figure S2 ) . Both pHZ1904 ( dndA-E+ ) and pHZ1904* ( dndE- ) were introduced by conjugation from E . coli ET12567/pUZ8002 with equal high frequency into strain LG4 ( c . 104 apramycin-resistant exconjugants per plate , Figure 2 , Panel 3 ) . In addition , sco4631 including the upstream promoter was cloned into the pSAM2-derived integrating vector pPM927 , resulting in pJTU1654 , and introduced into HXY16 , a S . lividans derivative that does not S-modify its DNA because it lacks the entire SLG genomic island including dndA-E . The resulting strain , LG5 , gave only c . 100 apramycin resistant exconjugants with pHZ1904 but c . 104 exconjugants with pHZ1904* ( Figure 2 , Panel 4 ) , while LG6 , a control strain containing pPM927 without the cloned sco4631 gene , accepted both pHZ1904 and 1904* with equal high frequency ( Figure 2 , Panel 5 ) . These results proved that Sco4631/ScoA3McrA without the need for any other gene on Gi11 ( SLP1 ) restricted the establishment of pHZ1904 which confers S-modification on its host . Since Sco4631 is encoded by Gi11 , which is also known as the 17 . 1 kb conjugative plasmid SLP1 , we wondered whether the above 49 Dnd+ pHZ1904 exconjugants might have lost the entire SLP1 sequence . PCR amplification of LG3 and ten similar exconjugants using outside flanking primers showed the same band . Sequencing of the PCR product of LG3 showed the entire Gi11 sequence was excised precisely , restoring the tRNATyr sequence into which SLP1 had originally inserted ( Figure S3A ) . This was again consistent with the hypothesis that Sco4631 was responsible for restricting the establishment of pHZ1904 in S . coelicolor , and it demonstrated that SLP1 can be lost spontaneously from S . coelicolor ( Figure S3A ) . This has not been observed before because SLP1 is a highly efficient conjugative plasmid that would immediately reinfect cured strains in the absence of the dndA-E genes . pJTU1654 ( pPM927 derivative expressing sco4631 , see above ) was introduced by conjugation into wild type S . lividans 1326 and into S . lividans HXY16 ( lacks SLG including dndA-E ) . A high frequency of 3×104 thiostrepton exconjugants were obtained per plate with S . lividans HXY16 , and 300-fold fewer exconjugants were obtained with wild type ( Dnd+ ) S . lividans 1326 . The control pPM927 without cloned DNA transformed both strains with equal frequency ( Figure S4 ) . Ten randomly selected , independent exconjugants were examined using PCR amplification and sequencing of the amplified product . All of them had suffered a precise deletion of the entire 93 kb S . lividans SLG genomic island that contains the dndA-E gene cluster ( Figure S3B ) . These results again support the hypothesis that sco4631 and the dnd gene cluster are unable to coexist in the same host . The coding sequence of sco4631 including the stop codon was cloned into the expression vector pET28a , generating pJTU1655 for producing amino-terminally His6-tagged Sco4631 . A similar plasmid , pSco4631H508A , was constructed that contains an inactive mutant protein because the first histidine residue of the conserved motif ( H508-N521-H529 ) was changed to alanine . The two plasmids were introduced into E . coli BL21 ( DE3 ) /pLysE and His6-Sco4631 and its mutant derivative were overexpressed for 5 h at 30°C . About 70% percent of the overexpressed protein was soluble in the supernatants and 30% was in the precipitates ( Figure S5 ) . The soluble proteins were purified using a Ni affinity column and stored at −20°C in Tris-Cl buffer pH 8 . 0 containing 50% glycerol . In the course of these experiments we noticed that the pET28a derivative containing the cloned sco4631 gene could not be transformed into Dam+ Dcm+ E . coli DH10B but the plasmid was stably maintained in the Dcm- E . coli BL21 ( DE3 ) /pLysE expression host and also in the non-methylating E . coli ET12567 ( see Table S1 ) . This gave us the idea to test the in vitro DNA cleaving activity using as a substrate dcm methylated ( Cm5CWGG ) EcoRV pre-linearized pOJ260 DNA isolated from E . coli DH10B . No DNA cleavage was observed using standard restriction NEB buffers 1–4 without and with BSA or ATP . Endonuclease activity was , however , observed when Mn2+ or Co2+ was included in the reaction buffer ( Figure 3 ) . Optimal cleavage with minimal unspecific star activity was achieved at 30°C for 5 min using 20 mM Tris-Cl pH 9 . 0 , 50 mM NaCl , 1 mM Mn2+ . Under optimal conditions , about 25% of 100 ng pOJ260 was cleaved and produced at least five bands of differing intensity , indicative of a partial digest ( Figure 4A ) . The precise position of eight cleavage sites was determined by blunt-end ligating the digested pOJ260 DNA to a DNA fragment containing the bla gene conferring ampicillin resistance ( see Materials and Methods ) . The cleavage/ligation sites were sequenced using PCR primers SeqF and SeqR reading away from the ends of the bla fragment . Each of eight cleavage sites was between 12 and 16 bp away from a C5mCWGG Dcm methylation site . pOJ260 has ten Dcm methylation sites and six of them had nearby cut sites ( Figure 4C ) . These results were consistent with our expectation that Sco4631 is a Type IV restriction endonuclease that cleaves near rather than precisely at its methylated DNA recognition site . ( Note that EcoKMcrA does not restrict Dcm-methylated DNA . ) The reaction buffer used in the above experiment resembles buffers that induce star activity ( reduction of sequence specificity ) in many Type II REases [24] , [25] . It might thus be that not all Dcm methylated sites are cleaved in vivo by Sco4631 . The fact that cutting near one of the sites was observed six times independently suggested that the DNA recognition sequence of Sco4631 may extend beyond the sequence C5mCWGG , or that Dcm methylation of the plasmid was incomplete as was observed by H . ZHOU ( in preparation ) . Also , the in vivo results of Gonzalez-Ceron et al . [9] showed that Sco4631 restricts DNA containing other DNA methylations . The above results were consistent with the in vivo observations suggesting that Sco4631 is a methyl-directed REase , but we could not be absolutely sure that E . coli DH10B had not added an unknown , additional modification to pOJ260 that was necessary for target selection by Sco4631 . To test whether indeed the Dcm methylation alone was sufficient for cleavage by Sco4631 , we synthesized two 164 bp double stranded DNA fragment that contained centrally the above preferred presumptive methylated site , and a 5′32P end label either at the top or the bottom strand so that nicking of both strands could be observed . The labelled DNA fragments were used to test for binding of Sco4631 using DNase I footprinting , and for detecting endonucleolytic activity . Figure 5A and 5B show a staggered cut in the position where four independent clones showed DNA cleavage ( Figure 4C , third sequence ) . Overexposed gels showed two additional , faint bands corresponding to single strand nicks on the bottom strand only on the right side of the Dcm methylation site as it is shown in Figure 5B and 5C . All these bands were absent in the controls using an unmethylated form of the oligonucleotide or heat treated Sco4631 . There may be a trivial explanation for the lack of faint bands , indicating secondary cleavage sites in the upper strand: most the DNA molecules that were cleaved at one of the secondary sites in the upper strand will also be cleaved at the primary site . Because of the 5′ end-labeling , secondary cleavage cannot be detected . DNase I footprinting revealed protection of 14 nucleotides centred around the Dcm methylation site C5mCTGG on the top strand ( light blue nucleotides in Figure 5C ) , and weaker , asymmetric protection of the bottom strand containing the sequence C5mCAGG . These results confirm that Sco4631 binds specifically to the Dcm methylated DNA and covers additional bases outside the C5mCWGG sequences . S . lividans DNA is S-modified ( phosphorothioated ) at specific positions . Only about one in 6000 DNA backbone phosphates contain sulfur in a non-bridging position . S-modification of S . lividans DNA seems to be incomplete , and certain sequences are preferentially S-modified . The Streptomyces multicopy plasmid pHZ209 contains such a preferentially modified site which was chosen for our study . S-modified pHZ209 was isolated from Dnd+ S . lividans 1326 and either cleaved oxidatively using Tris-peracid or using His-tagged Sco4631 protein and the same buffer that was used for the cleavage of Dcm methylated DNA . After digestion using EcoRV , which cuts pHZ209 once , three prominent bands and additional fainter bands were observed in both the Tris-peracid and the Sco4631-treated S-modified pHZ209 samples ( Figure 6A ) . The largest fragment represents 5 . 4 kb linearised pHZ209 generated from plasmids that were not cleaved by Tris-peracid or Sco4631 . The other two smaller bands were of the sizes expected if pHZ209 was cleaved at or near the preferential S-modification site . Controls with pHZ209 that was isolated from a dnd− host did not produce these two smaller bands . The precise Sco4631 cleavage sites were determined by blunt end cloning and sequencing . Tris-peracid cleaves the DNA backbone precisely at the site of the sulfur producing a 1 bp 5′ overhanging staggered cut [19] , [21] . The Sco4631-induced cuts were found on both sides between 16 and 28 nt away from the S-modification ( dotted line arrows in Figure 6B ) . This was consistent with the expectation that Sco4631 , like other Type IV REases , bound to the S-modification but cleaved some distance away from the cleavage site [26]–[28] . This endonucleolytic cleavage of DNA is , of course , consistent with our initial observations that Sco4631 and dndA-E are unable to coexist in the same host . Oxidative cleavage of S-modified DNA breaks specifically the phosphorothioate bonds resulting in a 1 bp 5′ staggered cut . The above sequence data ( Figure 6 ) suggested that Sco4631 cuts S-modified DNA rather imprecisely on either side near the sequence CGpsGCCG . The fact that cleavage occurred on both sides of the sequence suggests that Sco4631 may bind to both strands equally , i . e . the essential binding region may not extend beyond this palindromic sequence . We could not exclude the possibility that the DNA propagated in S . lividans 1326 contained additional modifications that influenced the results . For this reason , we synthesized a 118 bp double-stranded oligonucleotide containing one phosphorothioate on each strand at the preferred sequence of pHZ209 . Again , 5′32P label was used separately on both the top and bottom strand so that cuts in either strand could be observed . Oligonucleotides without S-modified bases were used as controls . His-tagged Sco4631-specific cleavage was observed in both the top and the bottom strand , and on both sides of the S-modification ( Figure 7 ) . ( Note that no cleavage occurred in the controls or at the site of S-modification which is marked by ** in Figure 7A and 7B . ) Multiple cleavage sites were observed ( solid arrows in Figure 7A and 7B ) , consistent with the data from the cloning and sequencing of individual cleavage sites ( dotted arrows ) . Surprisingly , no concentration dependent protection by Sco4631 of the S-modified DNA against DNase I was observed ( Figure S6 ) , possibly because the S-modified DNA was cleaved too efficiently . This result may not apply precisely to the majority of S-modified sites because this preferred site is a 6 bp palindrome rather than the more usual 4 bp core palindrome . Also , the S-modification in the synthetic oligonucleotides is racemic while the DndA-E proteins add the sulfur stereospecifically in the RP configuration [18] . These results , however , confirmed that His6-tagged Sco4631 could cleave S-modified without interference from hypothetical DNA modifications that might have been present on naturally S-modified pHZ209 . We demonstrated that the S . coelicolor protein Sco4631 is a Type IV REase that cleaves Dcm-methylated and also S-modified ( phosphorothioated ) DNA in vitro near the respective modification sites . DNA cleavage required a special reaction buffer containing Mn2+ or Co2+ at pH 9 . 0 . These conditions reduce the sequence specificity of some Type II REases [24] , [25] . Also the N-terminal His6 tag that was added to facilitate protein purification might have reduced or even altered the sequence specificity of the protein that was used for the in vitro studies . The fact that all the observed DNA cleavage events occurred very specifically near the respective modification sites suggests that neither the chosen buffer nor the His6 tag changed the enzyme specificity . Two other Type IV REases also have special buffer requirements for in vitro activity: GmrSD , encoded by an E . coli prophage , required Ca2+ and UTP for DNA cleavage [5] , McrBC required GTP and Mg2+ for DNA binding [29] . Recently , Chan et al . reported that HNH nucleases generally have unusual buffer requirements [30] . Only MspJI from Mycobacterium , a remote homologue of E . coli Mrr , cleaved methylated DNA in a standard REase buffer [31] . ( Please note that BseMII and BspLU11III have been reclassified as Type IIG REases [4] , [32] . The in vivo Dcm-methylated or S-modified DNAs might have contained unknown additional modifications that could have influenced the in vitro target selection . We excluded this possibility by repeating the cleavage experiments using long ( 164 and 118 bp ) double-stranded synthetic oligonucleotides that had the same sequence and DNA modifications as the preferred cleavage sites identified on in vivo methylated or S-modified DNA . The results confirmed the initial data , indicating that there had been no unknown in vivo modifications influencing our results . In addition , using the end-labelled modified oligonucleotides provided better information about the relative frequencies of DNA cleavage in different positions ( Figure 5 , Figure 7 ) . We have thus shown that a few Dcm-methylated sites and one preferentially S-modified DNA sequence were cleaved by Sco4631 . Gonzalez-Ceron et al . demonstrated , however , that also other sequence specific 5mC methylations lead to in vivo restriction in Streptomyces strains expressing Sco4631 [9] . We cannot exclude that Sco4631 can cleave DNA with methylated bases or S-modifications in many different possible target sequences . The observation that one of the six available Dcm sites in pOJ260 was cleaved four times while others were cleaved once or not at all could be due to statistical fluctuation , but it could also indicate that the uncut sites may have been undermethylated or that the DNA sequence around the methylation may influence the frequency of cleavage . It is also not possible to speculate about the importance of the surrounding DNA sequence for the enzymatic cleavage near S-modification sites because only a minority of the potential sites receive the sulfur , and some preferred sites are more strongly phosphorothioated than others [33] . Preferential S-modification alone , without differential cutting of sites depending on the surrounding DNA sequence , could have produced the major and minor bands in Figure 7A . Type IV REases generally have low sequence specificity , and the same enzyme may cleave e . g . methylated and hydroxyl-methylated DNA [17] , [27] , [34] . A good illustration for this is MspJI that cleaved CmCWGG , CmCGG , AGmCT , GGmCC , GmCGC and mCCGG [31] . Gonzalez-Ceron et al . observed that a S . lividans strain expressing cloned sco4631 restricted pSET152 DNA methylated by Dam or M . TaqI 400-fold and 20-fold , respectively , while Dcm methylated pSET152 was restricted only four fold [9] . It therefore seemed surprising at first that we could introduce pET28a::HisSco4631 into the Dam+Dcm− strain BL21 ( DE3 ) , but not into the Dam+Dcm+ strain DH10B . Tighter regulation of the lac promoter in the latter strain may have caused the difference rather than the methylation status . The purified His6-Sco4631 was produced in E . coli that did not contain any other Streptomyces genes . This proved that Sco4631 acted without accessory proteins as are required for endonucleases activity by the McrBC family of Type IV REases [5] , [29] . Sco4631 cleaved the Dcm-modified DNA preferentially to the left of the modification site on the sequence shown in Figure 5 , indicating that the sequence flanking the 5mC modification may influence where cleavage occurs . The S-modified DNA was cleaved more evenly on both sides of the S-modification ( Figure 7 ) , but there was no evidence for simultaneous cleavage on both sites . It was surprising that the distance between the DNA modification and the cleavage sites was different for Dcm and S-modified DNA . Maybe the enzyme remains locked to the Dcm site producing a near cut 12–16 bp away , but moves away from the S-modified site before cleaving less precisely c . 16–28 bp away . The above speculation would be consistent with the observation that DNase I protection footprints , which were made under conditions that allowed DNA cleavage , were detectable on both strands of the Dcm-methylated oligonucleotides , but they were absent from the S-modified oligonucleotides ( Figure S6 ) . Note that the protection on each strand was asymmetric with respect to the methylated C . The natural S . lividans DNA S-modification is chiral ( P-SR-configuration , [18] ) but the S-modification on the synthetic oligonucleotides was racemic and thus only one in four double-stranded oligonucleotides had the natural R-R conformation . It is unknown whether this may have affected protein binding or cleavage activity . The larger distance between the S-modification and the cleavage sites is , however , not an artefact of racemic S-modification because the larger distances were observed both with the naturally S-modified DNA and the synthetic S-modified oligonucleotide ( see Figure 7B dotted arrows and solid arrows , respectively , for comparison ) . Sco4631 had been identified by Gonzalez-Ceron et al . as a methyl-specific REase that has some limited ( 37% in a region of 91 aa ) amino acid sequence similarity to the 5mC and 5hmC-specific EcoKMcrA [35] , [36] . The two enzymes share a consensus HNH protein sequence motif that occurs in homing endonucleases and inteins , and in Group I and Group II introns [37] , [38] . In REBASE [4] Sco4631 is listed as ScoA3McrA . It should be noted , however , that EcoKMcrA does not restrict Dcm methylated DNA [16] . Because in vitro cleavage by Type IV REases has been elusive for many years , it was speculated that these enzymes may achieve restriction of modified DNA without DNA cleavage that could easily be reversed by re-ligation . Simple binding to the target DNA might prevent its replication or integration into the host genome [39] . By adding a third example of in vitro DNA cleavage by a Type IV REase we support the original idea that Type IV REases cleave DNA like all the other types of REases . S . coelicolor restricts Dam , Dcm , Hsd and other methylated DNA very effectively using at least four different methyl-specific REases including Sco4631 [9] , [40] . However , S . coelicolor only weakly restricts S-modified DNA from S . lividans [41] . Unexpectedly , the integrating vector pHZ1904 was severely restricted by S . coelicolor even though it did not have any known DNA modification because it had been propagated in the non-methylating E . coli host ET12567 that also did not support expression of the dndA-E DNA S-modification genes . Mutant derivatives of pHZ1904 were , however not restricted , excluding the possibility of an unknown sequence-specific ( as opposed to modification-specific ) restriction system acting in S . coelicolor . It seemed therefore likely that expression of the dndA-E gene cluster in S . coelicolor resulted in S-modification of the host DNA . Sco4631 would then cleave near the modified sites resulting in cell death . S . coelicolor mutants that tolerated S-modification by DndA-E represented about 50% of the rare pHZ1904 exconjugants . We speculated that one of the putative S . coelicolor endonucleases without counterpart in S . lividans might cleave S-modified DNA . Such a nuclease was indeed found to reside on the genomic island Gi11 [12] . Gi11 , also known as SLP1 is a 17 . 1 kb mobile element that can excise from the S . coelicolor genome and transfer by conjugation to S . lividans where it usually forms autonomously replicating plasmids [14] . These plasmids in S . lividans always lack a part of the original Gi11 sequence including sco4631 which would destroy the S-modified S . lividans DNA . Occasionally , the entire SLP1 sequence integrates into the S . lividans genome [42] . It is not known whether these integrated elements feature a mutant sco4631 or whether dndA-E is inactivated or deleted from these strains . It seems that Sco4631 does not effectively restrict the entry of S-modified DNA into S . coelicolor [43] , but it very effectively prevents the establishment of mobile elements that contain gene clusters like dndA-E that cause S-modification of its DNA . Such suicidal processes are frequently used by plasmids containing stable kil ( kill ) and unstable kor ( kill override ) genes resulting in cell death as a result of plasmid loss [44] . Also , restriction-modification ( R-M ) systems which are often on mobile elements [45] are stabilized in a bacterial population because the R-activity persists longer than the M-activity after the genes have been lost from a cell [46] . Fukuda et al . proposed that an important function of Type IV REases in general is the sacrificial killing of cells that have newly acquired a DNA modification system [39] . It was expected that strains containing a dnd gene cluster would not contain a Sco4631 homologue . This was true for 40 out of 41 fully sequenced strains containing a dnd gene cluster . Pseudomonas fluorescens Pf0-1 , however , contains both a complete dnd gene cluster and S-modified DNA , and it also contains two HNH proteins that are , however , very different from Sco4631 ( Figure S7A , S7B ) . It is not understood why bacteria need such defences against DNA modification systems . Dam methylation reduces the mutation rate by directing the mutHLS pathway to correct errors in the newly synthesized DNA strand , and DNA adenine methylation also has known regulatory roles in bacteria [47]–[49] , but the functions of Dcm methylation and phosphorothioation remain unknown . SLP1 in S . coelicolor and SLG in S . lividans are segregationally very stable genomic islands [13] , [50] . Spontaneous excision of each element in the absence of selective pressure could , however , be observed using sensitive PCR analysis ( Figure S3B ) [13] . Our experiments demonstrate that both SLP1 and SLG can be cleanly excised from the respective genomes by natural processes . The sequence-specific S-modification of DNA prevents DNA cleavage by some Type II REases whose specificity overlaps the S-modification site ( Liang J , personal communication ) , and Salmonella enterica serovar Cerro 87 which has S-modified DNA , also contains a restriction system that cleaves foreign DNA that lacks the cognate S-modification [51] . The fact that S . coelicolor has in Sco4631 an effective defence against invasion by these genes indicates that DNA S-modification may have important biological functions that remain to be discovered . An internal region of sco4631 was amplified by PCR using primers S31DF and S31DR , gel purified and inserted into the EcoRV site of pBluescript prior to transformation into pSET151 to give pJTU1653 , which was then transferred from ET12567/pUZ8002 into S . coelicolor M145 by two-parental mating [11] . A 2176 bp fragment containing intact sco4631 was amplified using KOD Plus ( TOYOBO ) and primers S31HEF ( XbaI linker ) and S31HER ( XbaI linker ) . The resulting fragment was cut by XbaI and inserted into pBluescript , and then the XbaI insertion was purified from this intermediate and cloned into the XbaI site of pPM927 to give pJTU1654 . pJTU1654 and pPM927 were transformed individually into ET12567/pUZ8002 for conjugation into S . lividans strains 1326 and HXY16 . sco4631 was amplified by PCR using KOD Plus and primers S31OEF and S31OER , inserted into pBluescript , excised as an NdeI–EcoRI fragment and ligated between cognate sites of pET28a , generating pJTU1655 . Site directed mutagenesis of pJTU1655 was carried out by using KOD-Plus-Mutagenesis Kit ( TOYOBO ) with primers H508A-F and H508A-R , resulting in pSco4631H508A . The expression constructs were then transformed into BL21 ( DE3 ) /pLysE respectively . 10 ml of the overnight culture of BL21 ( DE3 ) /pLysE pJTU1655 or BL21 ( DE3 ) /pLysE pSco4631H508A was inoculated into 1 L LB medium supplied with 50 µg/ml kanamycin and 34 µg/ml chloramphenicol . Then the culture was incubated at 37°C to OD600 = 0 . 4 , cooled to room temperature and 0 . 4 mM IPTG was added , followed by incubation for another 5 hours at 30°C . Then the cells were harvested and resuspended in 20 ml binding buffer ( 20 mM Tris-Cl , 20 mM imidazole and 150 mM NaCl pH 8 . 0 ) and lysed by sonication in an ice bath . After centrifugation ( 16000 g for 30 min at 4°C ) , the supernatant was applied to a HisTrap HP column ( GE Healthcare ) and purified using an ÄKTA FPLC ( GE Healthcare ) by eluting with imidazole linear gradient 20–500 mM . The product was desalted by a HiTrap Desalting column ( GE healthcare ) and stored in Tris-Cl buffer ( 50 mM , pH 8 . 0 ) with 50% glycerol at −20°C . Purified Sco4631 was visualized by Coomassie-stained 12% SDS-PAGE analysis and protein concentration determined using a Bradford Protein Assay Kit ( Bio-Rad ) . The protein purity was determined by Quantity One ( Bio-Rad ) from the gel , and the purities of Sco4631 and Sco4631 ( H508A ) are about 96 . 5% and 97 . 9% , respectively . Total S . lividans 1326 DNA prepared by the Kirby mix procedure was used as a substrate for the Sco4631 cleavage activity assay . Purified Sco4631 protein ( 500 ng ) and 100 ng DNA substrate were incubated at 30°C for 5 min in a total reaction volume of 20 µl . The reaction buffer contained 20 mM Tris-Cl ( pH 8 . 0 ) , 50 mM NaCl and each of seven divalent ions , Ca2+ , Co2+ , Cu2+ , Mg2+ , Mn2+ , Ni2+ or Zn2+ at a concentration of 1 nM to 10 mM . The reactions were stopped by adding loading buffer ( Takara ) containing 1% SDS , 50% glycerol and 0 . 05% bromophenol blue . The DNA after the reaction was examined by 0 . 75% agarose gel electrophoresis . Mn2+ or Co2+ ions at concentrations between 100 µM and 10 mM gave maximal DNA cleavage . The optimal pH was determined in a similar manner using equivalent buffers of pH 5 to pH 10 , supplemented with 1 mM Mn2+; pH 9 was optimal for Sco4631-mediated DNA cleavage . The plasmids pOJ260 and pHZ209 were isolated from DH10B and S . lividans 1326 , respectively , using the QIAGEN Plasmid Midi Kit ( Qiagen ) . One microgram pOJ260 DNA was incubated with Sco4631 under the optimized conditions described above . Linearised pOJ260 was gel purified and blunted using Klenow Fragment ( Fermentas ) and dNTPs at 37°C for 10 min in a total volume of 20 µl ( 1×reaction buffer , 0 . 05 mM dNTP mix , 5 Unit Klenow Fragment ) , followed by heating at 75°C for 10 min to stop the reaction . The blunted mix was ligated to a 1311 bp amplicon harboring a bla cassette and the ligation mix was transformed into E . coli DH10B . Plasmid DNA was then prepared from randomly selected ampicillin-resistant transformants for insert end-sequencing using the primers seqF and seqR . Similarly , pHZ209 was linearized using EcoRV and then incubated with Sco4631 . The resulting DNA fragments were gel purified , blunted using Klenow fragment , and ligated into the EcoRV site of pBluescript for transformation into E . coli DH10B . Plasmid DNA was then prepared from randomly selected transformants for insert end-sequencing using the primers T3 and T7 . A 164 bp PCR amplicon was generated using pOJ260 as a template , and the long primers MF and MR that contained a m5C residue on each strand within the Cm5C ( A/T ) GG Dcm-modification motif . An unmodified matching amplicon was also generated using primers UMF and UMR . Individual strands of the modified and unmodified amplicons were 5′ end-labeled by pre-tagging the appropriate primer with γ-32P using the following protocol . 10 pmol of primer was incubated with [γ-32P]-ATP ( Beijing Furui Co . Ltd ) using 10U of T4 polynucleotide kinase ( Promega ) at 37°C for 30 min , followed by 90°C for 2 min to inactivate the T4 polynucleotide kinase . PCR reactions were performed in a total volume of 50 µl containing 2 ng template DNA , 10 pmol of each primer , 1× PCR buffer , 5% DMSO , and 5U Taq polymerase ( Genescript ) . PCR products were purified using the QIAquick PCR Purification Kit ( Qiangen ) . An identical strategy was used to synthesize and strand-specifically label the phosphorothioate-modified and unmodified pHZ209-derived 118 bp amplicons . The phosphorothioate modification was introduced between the tandem guanine residues in the Dnd-motif ( GpsGCC ) by prior chemical synthesis of the long PCR primers SF and SR . The matching unmodified amplicon was generated using the primers USF and USR . The DNA substrates used in these analyses were as described above . For cleavage sites analysis , the reaction mixture containing 100 cps 32P-labeled DNA substrate ( 10 nM ) , 20 mM Tris-Cl ( pH 9 . 0 ) , 50 mM NaCl , 1 mM MnCl2 , 5% glycerol and 500 ng Sco4631 in a total volume of 20 µl was incubated at 30°C for 5 min . For the DNase I footprinting assay , 500 cps 32P-labeled DNA substrate ( 50 nM ) was added to 0–24 µg ( 18 µM ) of Sco4631 under the same buffer conditions as above , and the reaction mix was then incubated on ice for 5 min prior to adding 2 . 5 µl DNase I buffer and 0 . 3 U of DNase I ( Promega ) , and further incubation at 37°C for 1 min . The reaction was stopped by adding 100 µl stop solution ( 3 M ammonium acetate , 0 . 25 M EDTA , 1 mg/ml glycogen ) , and 50 µl phenol-chloroform . Samples were then denatured at 95°C for 2 min and loaded on an 8% polyacrylamide–urea gel . The DNA sequence ladder was generated using an fmol DNA Cycle Sequencing kit ( Promega ) . After electrophoresis , the gels were dried and exposed to a Kodak X-ray film . The nucleotide sequence of pOJ260 has been deposited in GenBank under Accession number GU270843 .
Bacteria frequently exchange genetic information among themselves . DNA from one species can be transferred efficiently to unrelated microbes . Bacteria have developed systems that restrict gene transfer . Many restriction systems recognize and destroy foreign DNA entering the cells , but there are also enzymes inducing suicide of cells that have been invaded by foreign genes that modify the host DNA . We describe a restriction endonuclease from an antibiotic-producing soil bacterium that cuts foreign methylated DNA and also foreign DNA containing sulfur . DNA sulfur modification occurs in diverse medically or industrially important microbes and has been shown to prevent cleavage of DNA . The most similar enzyme in the databases is the putative restriction endonuclease McrA from Escherichia coli which has not been observed to cleave DNA in a test tube . Our endonuclease showed no activity with magnesium , but it cleaved DNA in the presence of manganese ions . Therefore , we present two novelties: an unusual restriction endonuclease that cleaves sulfur-modified DNA and conditions that allow the study of the enzyme in a test tube .
You are an expert at summarizing long articles. Proceed to summarize the following text: Mouse early transposon insertions are responsible for ∼10% of spontaneous mutant phenotypes . We previously reported the phenotypes and genetic mapping of Polypodia , ( Ppd ) , a spontaneous , X-linked dominant mutation with profound effects on body plan morphogenesis . Our new data shows that mutant mice are not born in expected Mendelian ratios secondary to loss after E9 . 5 . In addition , we refined the Ppd genetic interval and discovered a novel ETnII-β early transposon insertion between the genes for Dusp9 and Pnck . The ETn inserted 1 . 6 kb downstream and antisense to Dusp9 and does not disrupt polyadenylation or splicing of either gene . Knock-in mice engineered to carry the ETn display Ppd characteristic ectopic caudal limb phenotypes , showing that the ETn insertion is the Ppd molecular lesion . Early transposons are actively expressed in the early blastocyst . To explore the consequences of the ETn on the genomic landscape at an early stage of development , we compared interval gene expression between wild-type and mutant ES cells . Mutant ES cell expression analysis revealed marked upregulation of Dusp9 mRNA and protein expression . Evaluation of the 5′ LTR CpG methylation state in adult mice revealed no correlation with the occurrence or severity of Ppd phenotypes at birth . Thus , the broad range of phenotypes observed in this mutant is secondary to a novel intergenic ETn insertion whose effects include dysregulation of nearby interval gene expression at early stages of development . The molecular causes of vertebrate malformations and the molecular basis of the variability in Mendelian syndromes are incompletely understood . While coding alterations have received a substantial amount of attention , the contribution of variation or mutation in intergenic regions , as well as the role of genetic background/modifiers , epigenetic and environmental factors , retrotransposons and transgenerational genetic effects , are receiving more attention particularly in relation to penetrance , expressivity and pleiotropy [1]–[8] . Spontaneous mobile element insertions in mice can be associated with alterations in body plan and morphogenesis [9] . There are many types of transposable elements; however , those active in the mouse are mostly IAP or Type II early transposons ( ETn ) [9] . Type II early transposons carry long terminal repeats ( LTR ) and are classified into MusD , ETnI and ETnII subtypes . IAP , MusD and ETnII insertions are responsible for a substantial fraction ( ∼10% ) of spontaneous new mutations in mice [9] . Most previously reported mutagenic ETn insertions occur in the sense orientation within genes , resulting in disruption of exons , polyadenylation and/or splicing . ETn elements are highly transcribed during pre-gastrulation and at later stages of morphogenesis in selected tissues [10–12] and while promoter activation of adjacent genes has been demonstrated for IAP elements , it has not been observed for ETn insertions [9] . Moreover , ETn regulatory sequences such as enhancers and repressors upon random insertion in new genomic environments could exert deleterious or beneficial effects on neighboring gene expression . The activity of retrotransposons varies depending on their state of methylation , which is controlled by host factors , and many transposable elements act as metastable epialleles [9 , 13 , 14] . Previously we reported the phenotypes and genetic mapping of Polypodia , ( Ppd ) , a dominant , X-linked mouse mutation exhibiting malformations in 20–25% of newborn mutation carriers [15] . Postnatally affected mice predominantly exhibit ventral , caudal limb duplications ( Figure 1 ) and a variety of other defects including bilaterally asymmetric anomalies , partially duplicated snouts and whiskers , mirror-image pelvic duplication ( dipygus ) , extra digit-like bony growths on abdominal skin , cystic kidneys , renal agenesis , duplicated external genitalia with normal internal genitalia , kinked , curly or knotted tails , forelimb postaxial polydactyly , radial aplasia , spina bifida , microphthalmia ( unilateral ) , supernumerary nipples , yet no malignancy , duplicated upper extremities , or extra spinal elements . We localized the mutation to a ∼10 Mb interval on the mouse X-chromosome between markers DXMIT74 and rs13483835 [15] . The striking body plan alterations offer an opportunity to understand in molecular terms how such disorganization of the vertebrate body plan can occur and how these principles might inform our understanding of similar birth defects in humans . In this paper , we 1 ) show that Ppd mutant embryos are not born at expected Mendelian ratios due to fetal loss , 2 ) describe the discovery of a novel , intergenic ETnII-β insertion in the refined genetic interval , 3 ) recreate the mutation using homologous recombination in ES cells and recapitulate Ppd phenotypes , 4 ) show that one effect of the Ppd ETn insertion is dysregulated adjacent gene transcription in mutant ES cells , and 5 ) show that the state of DNA methylation of the 5′ LTR is not correlated with Ppd phenotypic variability . Ppd arose on the CD-1 strain and mutants exhibit a variety of malformations as described above , although the ventral , caudal duplications with extra limbs are the most frequent and dramatic [15]; Figure 1 . We crossed Ppd hemizygous males and heterozygous females to the wild-type , inbred C3H/HeJ strain for over 10 generations and observed that ∼21% of mice born with Ppd interval genetic markers [15] showed abnormal phenotypes . We attempted crosses to produce a higher frequency of postnatal anomalies to facilitate later experimental studies by outcrossing Ppd mice ( male or female ) on the C3H background ( generation N8 ) to CAST/EiJ , CZECHII/EiJ , MSM/Ms , C3H/HeJ , C57BL/6J , DBA/2J , CD1 , and B6/D2 F1 hybrids . Offspring were evaluated at birth for any of the phenotypes observed in Ppd mutants and genomic DNA was collected and genotyped for the Ppd haplotype [15] . In this breeding scheme , inclusion of C57BL/6J genetic background did not change the frequency of observed postnatal malformations ( ∼21–22% ) in females or males bearing the Ppd genetic interval . Outcrossing for one generation to CAST/EiJ , CZECHII/EiJ and DBA/2J chromosomes resulted in the lowest percentage with birth anomalies ( ∼0–0 . 4% ) , whereas ∼11–14% of newborns of MSM/Ms , B6/D2 and CD1 outcrosses had anomalies at birth . This is not a formal measure of penetrance . It suggests , but does not prove , that genetic background could have a significant effect on the phenotypic outcome related to inheriting this mutation , but evidence to support that conclusion will require many generations on the individual strains as well as examination of both prenatal and postnatal phenotypes . We hypothesized that apparent variations in the frequency of postnatal malformations in mutants at birth might be influenced by embryonic lethality . To test this , we took advantage of a genetic cross for mapping purposes that produced Ppd heterozygous female mice with one wild-type CZECHII X-chromosome and one Ppd X chromosome ( C3H background ) and mated these females with wild-type C3H males . Offspring of this latter cross were genotyped for interval markers and sex as described [15] , which allowed us to determine the birth frequency of male and female offspring with the Ppd chromosome , which must come from the female . Table 1 shows the X-chromosome identity in offspring ( CZECHII/C3H refers to a female with CZECHII and C3H chromosomes; CZECHII/Y refers to a male with a CZECHII X-chromosome; Ppd/C3H refers to a female with Ppd and C3H X-chromosomes; Ppd/Y refers to a male with a Ppd X-chromosome ) . A 60% reduction of the Ppd haplotype was found in liveborn males and a 23% reduction was observed in liveborn females ( Fisher's Exact test , p<0 . 007 ) . A similar result was obtained in a cross involving only the C3H background ( 82% and 36% reductions , respectively; Table 2; p<0 . 055 ) . The data indicate that there are fewer Ppd mutants at birth than expected and males with Ppd are more likely than females to fail to be born . To determine if Ppd X-chromosomes are represented in offspring early in development as expected , we evaluated the genotypes and sex of conceptuses at E9 . 5 . Ppd males ( C3H background ) were crossed to CD-1 females , followed by a backcross of female Ppd offspring to CD-1 wild-type males . Evaluation of those offspring revealed expected numbers of Ppd X-chromosomes in conceptuses at E9 . 5 ( Table 3 ) . Thus , embryos must be dying between E9 . 5 and birth . Our preliminary data suggest that mutants occasionally display extensive early gastrulation abnormalities including overallocation of extraembryonic tissue at the expense of the epiblast and accumulation or piling up of cells in the primitive streak ( J . Innis , K . Downs , P . Wakenight , K . Millen , data not shown ) . Further work will be required to determine the basis of fetal loss in these mutants . We reported the location of Ppd in a 9 . 64 Mb genetic interval on the X- chromosome [15] . To narrow the interval , we crossed our Ppd mice on the C3H background to CZECHII/EiJ mice to exploit a greater number of polymorphic differences and improve crossover resolution . Using 2 visibly affected recombinant animals , we narrowed the interval to 1 . 85 Mb between DXMIT94 and rs13483824 . a at 72 . 02 Mb and 73 . 87 Mb , respectively ( GRCm38 ) . In addition , we test crossed the visibly unaffected critical recombinant F2 animals and looked for affected progeny , allowing us to refine our map based on the Ppd “carrier” haplotype . These efforts allowed us to locate Ppd in a ∼1 . 4 Mb interval between DXMIT119 and SNP rs13483824 . a ( data not shown ) . We previously reported a normal karyotype and no apparent submicroscopic gene dosage aberration by BAC array comparative genomic hybridization ( CGH ) [15] . To examine the X chromosome in more detail , we compared male Ppd DNA to wild-type male C3H DNA using an X-chromosome-specific NimbleGen array in a CGH experiment with average probe spacing every 500 base pairs . No variation was identified on the X-chromosome within the 1 . 4 Mb critical genetic interval ( data not shown ) . Thus , at this level of resolution Ppd is not due to a chromosomal deletion/duplication , leaving us to consider single gene smaller mutations , deletions or insertions . Our refined genetic mapping experiments on the X-chromosome defined a Ppd interval with over 30 annotated protein coding genes . To determine if Ppd was a mutation in one of these interval genes , we prioritized gene candidates based on known gene function and initiated a variant search with several methods . Southern analysis with non-repetitive , gene-centered DNA probes and Ppd genomic DNA disclosed altered restriction digest patterns with a Dusp9 gene probe ( Figure 2A ) . This alteration was not observed with this probe in other mouse strains ( Figure S1 ) . Using PCR primer walking and DNA sequencing of PCR products and clones spanning the entire insertion and flanking regions we identified a 5 . 5 kb insertion positioned 1 . 6 kb downstream of the 3′ end of the Dusp9 gene ( Figure 3 ) . No mutations of endogenous chromosomal material were observed in adjacent genomic regions . We demonstrated absence of this genomic alteration in representative background ( CD-1 ) male genomic DNA , as well as 21 different mouse strains using PCR ( Figure 2B ) . Similarly affected mutant mice were independently discovered by K . Millen and P . Wakenight in CD-1 animals at the University of Chicago . Blinded testing with a Ppd mutation-specific PCR assay utilizing unique primers to the adjacent X chromosome and the newly inserted sequences ( see Figure 3 , primers F5/R6; 248 base pair product ) , demonstrated the same insertion mutation in those affected mice ( data not shown ) . The DNA sequence of the inserted segment ( GenBank Accession: Mouse_ETnII-B_Polypodia_X_Chromosome_DNA KC512757 ) revealed it to be an early transposon type IIβ ( ETnII-β ) element . This conclusion is supported by 1 ) the sequences of the homologous 5′ and 3′ LTRs; 2 ) the presence of a putative Lys-tRNA binding site ( PBS ) 5′-TGGCGCCCGAACAGGGA-3′ , 3 ) the presence of a 6 bp direct duplication ( 5′-TCCTGT-3′ in the orientation shown in Figure 3 ) at the insertion junctions , 4 ) absence of coding sequences that would be more characteristic of MusD or IAP elements [16–18] , 5 ) absence of ETnI-specific sequences [19] , and 6 ) the presence of specific sequences found only in ETnII-β elements that cross an internal deletion ( ETnII-3636as = 5′-GTCACTTAATACCCCCTGACTAACAAATG-3′; [20 , 21] . The Ppd interval ETnII-β is highly related to several endogenous ETnII-β elements located on chromosome 5 ( AC163331 ) , chromosome 13 ( AC163684 ) and within the desmoglein locus , among others . As expected , the 317 bp LTRs of the newly identified ETn are identical and have 16 CpG dinucleotide sites . The Ppd interval ETn is located 1 . 6 kb downstream ( relative to Dusp9 transcription ) of the polyadenylation signal of Dusp9 , between two repetitive sequences ( SINE and LINE elements; Figure 3 ) at position ChrX: 73645160 ( GRCm38/mm10 ) . This insertion does not disrupt Dusp9 , Pnck , or any other known gene or noncoding RNA; examination of the EST databases shows no reported spliced or unspliced ESTs or isoforms beyond exon 4 of Dusp9 or of the last exon of Pnck . Sequencing of exons and exon/intron boundaries of Dusp9 and Pnck did not reveal any pathogenic sequence variants . The orientation of the ETn is antisense to Dusp9 gene transcription and the insertion site is located ∼10 . 8 kb from the 3′ end of the Pnck gene . Thus , the ETn insertion appeared to be a strong candidate for Ppd . While transposon insertions are well known mutagens , the intergenic position of the insertion was novel . To determine whether this novel intergenic ETnII-β insertion is Ppd , we sought to introduce this ETn into a wild-type genome to create an engineered ETn allele ( eETN ) . We first created a BAC library from male Ppd genomic DNA and then isolated a BAC clone spanning the genomic region including the ETn . We used BAC recombineering to construct a targeting vector for homologous recombination in mouse ES cells ( Figure 4 ) . DNA sequencing of 5′ and 3′ genomic targeting arms was employed to determine whether the ETn insertion was the only plausible candidate mutation in the targeting vector . Sequencing disclosed one common , non-coding SNP variant ( rs29038663; C>T; GRCm38/mm10 ) by comparison with the reference C57BL/6J sequence . Thus , the ETn insertion is the only candidate mutation within the targeting vector . We employed Bruce-4 . G9 ( a chromosomally stable sub-line generated at the University of Michigan Transgenic Animal Core Lab from Bruce4 ES cells ) [22] and UMB6J-D7 ( a pure BL/6 line generated here at the University of Michigan ) mouse ES cell lines to knock-in the ETn into the wild-type genome . Three hundred clones from each electroporation were picked and expanded . Southern blotting with Probe A ( see Figure 4 ) and Ppd ETn-specific locus PCR ( F5/R6 ) confirmed a high frequency of homologous recombination in both cell lines ( 27–50% ) . Five ES cell clones from each line were karyotyped and 5 cell lines ( 4 Bruce4 . G9 and 1 UMB6J-D7 ) from those clones were found to be euploid . All euploid lines were reexamined by Southern blotting ( Figure S2 ) and by Ppd-specific PCR ( not shown ) and were found to be correctly targeted . Blastocysts were injected with the Bruce-4 . G9 targeted ES cells , and chimeric males were produced . Germline transmission was successful in generating 10 female engineered ETn ( eETn ) heterozygotes ( Neo+/eETn+ ) ; none of these females exhibited an abnormal phenotype . We bred these females to β-actin FLPe males ( Jackson Lab stock #005703 ) , to excise the Neo cassette and demonstrated expected PCR products after excision ( Figure S3 ) . Figure 5A shows a Neo−/eETn+ progeny female with a caudal mass and ectopic legs . This observation confirmed our hypothesis that the ETn is the Ppd mutation . To determine if phenotypically unaffected Neo−/eETn+ mice could have offspring with Ppd phenotypes consistent with the original Ppd mutant , we bred Neo−/eETn+ carrier males to B6/D2 F1 hybrid or FVB females . Nine out of 69 ( 13% ) eETN+ offspring of B6/D2 mothers and 8 out of 31 ( 26% ) eETN+ offspring of FVB mothers , had caudal masses with ectopic limbs . These results demonstrate that germline transmission of the engineered allele from the male or female germline is associated with typical Ppd caudal malformations ( Figure 5B , C ) . Moreover , in this small cohort on mixed genetic backgrounds , the frequency of postnatal malformations and phenotypic variability in the engineered lines is similar to that of the original Ppd allele . These results confirm that the ETnII-β insertion is the Ppd mutation . Endogenous retroviral transpositions including ETnII-β insertions are the cause of ∼10% of spontaneous new mouse mutants [9 , 19] . Most , but not all , mutagenic ETn insertions occur within genes in the mouse and are sense-oriented [9 , 23] . Transcriptional interference with splicing or 3′ end formation , when ETn insertion occurs within genes due to the contribution of ETn splice sites and polyadenylation signals , is well documented and is the basis of most phenotypic effects of such insertions [9] . To begin to explore the mechanism by which the Ppd ETn insertion was interfering with development , we first examined the structure and expression of flanking genes Dusp9 and Pnck mRNAs in mutant embryos . Dusp9 encodes a MAP kinase tyrosine/serine/threonine phosphatase of which there are numerous family members [24 , 25] . Dusp9 is expressed in ES cells [26] , but it is not essential for ES cell viability , although BMP4 has recently been shown to activate Dusp9 transcription via SMAD1/5 , resulting in reduction of pERK in ES cells [27] . Expression also has been observed in the ectoplacental cone and chorion of the placenta as early as E7; at E8 . 5 Dusp9 is activated in the ventral foregut endoderm , which ultimately becomes the liver . It is also expressed in dorsal and ventral muscle groups of the forelimb and hindlimb at E9–E11; the face ( E9 ) , mandible and hypoglossal cord [24] . Dusp9 heterozygous and null mutants die prenatally by E10 . 5 due to failure of growth of the placental labyrinth [26] , and by tetraploid rescue mutants exhibit normal embryonic development [26] . Pnck encodes a pregnancy-upregulated , non-ubiquitously expressed calcium/calmodulin-dependent serine/threonine protein kinase [28] , and is known to be expressed in mammary glands , brain and during hippocampal dendritic growth . PNCK has also been shown to induce ligand-independent epidermal growth factor receptor degradation [29] . Therefore , we sought to test if the ETn alters Dusp9 or Pnck 3′ RNA structure by evaluating mRNA from E7–E9 . 5 whole mutant embryos compared to wild-type littermates by 3′ RACE . No major differences were detected in relative abundance or in 3′ RACE products of Dusp9 or Pnck RNA in mutant embryos at these developmental times ( Figure S4 ) . We hypothesized that the ETn may ectopically activate or interfere with the transcription of Dusp9 or Pnck , through modification of the chromatin environment or through enhancer provision , usage , or interference . This hypothesis seemed particularly relevant considering the burst of early transposon transcription that occurs during early stages of development from E3 . 5–E7 . 5 [10–12 , 30] . To test this hypothesis , we first examined the mRNA expression and structure of Dusp9 and Pnck in wild-type mouse embryonic stem cells . ES cells represent the inner cell mass at a developmental stage when early transposon transcription is high . Reverse-transcription PCR using oligo-dT primed synthesis followed by PCR using primers in different exons confirmed that Dusp9 and Pnck are normally expressed in wild-type ES cells ( data not shown ) . Due to the close location of the ETn to Dusp9 , we used mutant ES cells to evaluate Dusp9 splicing ( from exons 2–4 by RT-PCR ) and 3′ end formation as assessed by 3′ RACE . Neither were disrupted in mutant ES cells ( data not shown ) , consistent with the observations in mutant embryos . To determine if Dusp9 , Pnck or other X chromosome local interval gene transcription is dysregulated as a consequence of the ETn insertion , we examined steady-state mRNA from several independent mutant male ES cell lines using Affymetrix Mouse GeneChip 430 2 . 0 expression microarrays . We compared all 3 original Ppd ES lines with normal ES cell mRNA prepared from Bruce4 . G9 , ND-D3 and UMB6J-D7 lines . We focused our analysis to genes in 500 kb intervals on either side of the ETn insertion site on the mouse X chromosome . Within this 1 Mb interval are 35 RefSeq genes ( GRCm38/mm10 ) , for which 9 were not represented on the microarray used ( 2 microRNA genes; 4 X-linked lymphocyte regulated genes; and 3 newly added genes in mm10 , Haus7 , Naa10 and Tex28 not located close to the ETn insertion site ) . Both Dusp9 and Pnck were represented . Genes in this interval whose expression fulfilled quality measures ( see Materials and Methods ) , were increased or decreased at least 2 fold and exhibited a FDR≤0 . 05 , were Dusp9 ( all 3 probe sets , increased 3 . 12 , 2 . 74 and 2 . 6 fold ) and Slc6a8 ( only 1 of 2 probe sets , increased 2 . 34 fold and 1 . 07 fold ) . Pnck mRNA expression was not altered . Slc6a8 , which encodes a brain creatine transporter , is located telomeric to Pnck and was not examined further . We used Taqman real-time quantitative RT-PCR directed to Dusp9 , a MAP kinase phosphatase , to confirm the array result . Steady-state Dusp9 RNA expression was elevated in all ETn-bearing ES cells by 5–15 fold over wild-type cells ( Figure 6A , B ) . To determine if the elevated levels of Dusp9 steady-state mRNA are associated with higher levels of steady-state DUSP9 protein , we performed Western blots with protein extracts from mutant ES cell populations compared to 4 different wild-type ES cell lines ( Figure 6C ) . Western blots with DUSP9 antibody ( gift from Robin Dickinson; [24] ) revealed increased DUSP9 protein expression ( 7–14 fold ) , adjusted for β-actin , in all Ppd ES cell lines and all eETn ES cell lines . This was confirmed with an independent antibody ( data not shown ) . The specificity of both antibodies for DUSP9 was confirmed by testing the effects of pre-incubation with synthesized DUSP9 peptide versus control , nonspecific peptide ( Figure S5 ) . Thus , DUSP9 protein is over-expressed in Ppd mutant ES cells . We conclude that one consequence of ETn insertion is Dusp9 overexpression in pluripotent cellular representatives of the inner cell mass . Retrotransposon activity varies depending on the state of methylation of the locus [13 , 14] . CpG methylation increases from 5′ to 3′ within individual ETn LTRs [14] . We hypothesized that variable occurrence in the Ppd phenotype among ETn carriers or Ppd males at birth may be explained by variation in Ppd interval ETnII-β 5′ LTR methylation . To test this hypothesis , we used bisulfite sequencing of tail genomic DNA from affected versus unaffected Ppd ETn carrier ( female ) and male littermates . After bisulfite modification , we amplified 237 base pairs of the 317 bp 5′ LTR anchoring on adjacent X-chromosome specific genomic sequence , allowing us to interrogate seven 5′ LTR-specific CpG dinucleotides and 1 adjacent X chromosome genomic CpG dinucleotide immediately upstream of the transcription start sites mapped in ETnII-β elements [21] . Comparison showed that inter-individual differences in the occurrence of a Ppd phenotype at birth is not related to the methylation state of the 7 CpG dinucleotides in the 5′ portion of the 5′ LTR ( Table 4 ) in either females or males . We also examined the methylation of the ETn in Ppd ES cells; the ETn , as expected , was largely unmethylated at this stage of development . In addition , male Ppd animals , regardless of phenotype , exhibited a broader distribution of the degree of methylation of these 8 CpGs . To determine if variation in 5′ LTR methylation was observed between tissues within an affected animal , genomic DNA derived from normal tissue ( tail ) and from the caudal ectopic legs/mass from one adult Ppd female was subjected to bisulfite sequencing . No differences were observed in the degree or distribution of methylated CpG residues . These results suggest that if the methylation state of the ETn does affect the occurrence of postnatal phenotypes , it is not observable as a difference in 5′ LTR methylation in adult tissues . Using genetic mapping and homologous recombination in ES cells , we have shown that a novel ETnII-β insertion discovered to lie 1 . 6 kb downstream of the Dusp9 gene is the Ppd genetic lesion . ETnII-β elements often insert into exons and disrupt splicing and polyadenylation [9] , yet we find no evidence of an altered Dusp9 transcript structure . Instead , in mutant ES cells , one apparent effect of the ETn in this new genomic environment is increased Dusp9 mRNA and protein expression . ES cells represent the pluripotent inner cell mass at a developmental time point associated with increased ETn transcription and it is attractive to speculate that interference , by an as yet unknown mechanism , with appropriate transcriptional regulation of Dusp9 at this or other stages of development , or of other genes in this region of the X chromosome , gives rise to the phenotypic effects in the Ppd mutant . ETn elements have been hypothesized to exert mutational effects on gene expression at a distance , but few examples have been identified . Dactylaplasia [31] is due to MusD ( ancestral ETn ) element insertion within ( Dac2J ) or upstream ( Dac1J ) of the dactylin gene [32] , and the two mutant alleles are suppressed by an unlinked modifier , Mdac [31] . Limb defects in Dactylaplasia mice may result from Mdac-suppressible transcriptional interference with apical ectodermal ridge expression of Fgf8 [32–34] , a gene located more than 70 kb away from the MusD insertion sites . Interestingly , MusD expression in the AER is increased in mutant limbs suggesting that Fgf8 AER enhancers may be co-opted by an active MusD element in this mutant [34] . In addition , Mdac appears to dominantly modulate the MusD methylation state , which inversely correlates with the phenotype . Recently , another intergenic ETn insertion 12 . 5 kb upstream of Ptf1a was elucidated as the cause of the semidominant Danforth's short tail ( Sd ) mutation , and this insertion is associated with upregulation of embryonic expression of Ptf1a leading to caudal regression phenotypes [35–37] . The addition of our example confirms that such intergenic insertions , while rare , are capable of modifying gene expression , although in all cases reported so far , the mechanism remains to be determined . In contrast to Dac mutants , the methylation state of the Ppd 5′ LTR is not correlated to phenotype . These results are consistent with prior conclusions indicating that ETnII transcriptional activity is regulated by more than methylation state and genomic environment [21] . Although we did not examine the 3′ LTR , which is closest to the Dusp9 gene , histone modification and chromatin structure across the Dusp9/ETn interval could be altered by the ETn and would be exciting to examine in future studies , with consideration given to analysis of selected cell populations earlier in development . We have not proven that upregulation of Dusp9 or modification of any other interval gene expression is the cause of the malformations and/or fetal death . It is conceivable that ETn transcriptional effects ( negative or positive ) could also occur at later developmental phases in different tissues . ETn expression occurs in two phases [10–12] . In the first phase , ETnII transcription occurs during E3 . 5–E7 . 5 beginning in the inner cell mass and extending into the epiblast and extraembryonic ectoderm . The 2nd phase occurs between E8 . 5–E11 . 5 beginning with E8 . 5 neural tube ETnII expression outlining the rhombomeres [12] . This neural expression gradually decreases as mesodermal expression increases in the somites at E8 . 5 . At E9 . 5–10 . 5 , expression is observed in the olfactory placode and then becomes concentrated along the nasal pit and lateral nasal processes . Strong branchial arch ETnII expression was observed at E8 . 5–E11 . 5 . Finally , the forelimb and hindlimb buds exhibited strong expression at E9 . 5 and E10 . 5 , respectively . At E11 . 5 , ETnII expression was noted in the condensing ulna/radius . Since there are 300–400 copies of type II ETn/MusD elements in the mouse genome , expression analyses likely reflect the contribution of expression from multiple genomic locations . Interestingly , this multiphasic , multiple tissue expression pattern could , in part , be related to the varied organ effects of the ETn insertion in Ppd mutant mice . For example , the ETn could ectopically activate Dusp9 in ES cells in association with the early burst of ETn transcription normally observed at E3 . 5 . In this situation , proximity to Dusp9 creates an opportunity for Dusp9 dysregulation consequent to the insertion of a transcriptionally activated ETn nearby . Potential interference with Dusp9 or other interval genes in specific tissues at later times is a natural hypothesis to examine as the etiology for malformations . It is intriguing that normal Dusp9 expression occurs later in development in other regions of the embryo as described [12 , 24] ( including the olfactory placode and nasal pit , somites and limbs ) that overlaps tissue malformations observed in some Ppd mutants: double snouts , spina bifida , and ulnar aplasia , syndactyly or hypodactyly [15] . Ppd mice strikingly resemble the mouse mutants Disorganization [38–41] and Duplicitas posterior [42–44] , as well as conceptuses exposed to the teratogen all-trans retinoic acid ( RA ) at pre-gastrulation stages , E4 . 5–E5 . 5 [45–48]; [Innis et al . , unpublished] . Ducks , cows , deer and other animals have also been reported ( not shown ) with similar Ppd-like , dramatic caudal or other ectopic limb duplications , suggesting that common fundamental vertebrate developmental pathways are susceptible to spontaneous mutations or environmental teratogens . Humans with ectopic lower limbs with and without pelvic anomalies or dipygus , have been described extensively in the literature [49–56]; all cases occurred sporadically , not unlike the occurrence of Ppd . Duplicitas posterior mice had varying pelvic masses and accessory limbs identical to Ppd mutants [42–44] . This mutation , which was never identified , arose on the stock carrying Sd , Danforth's short tail , had a penetrance of 20% in liveborn mice , caused prenatal death in some , and showed significant strain variation in penetrance and phenotype . Embryologically , Danforth noticed a thickening at mouse gestational age E11 of the “ventral tissues at the posterior end of the embryo in a region including , and extending in front of , the usual site of the cloacal pit” . The cloaca was noticed to widen out laterally and form two cloacal membranes , often resulting in two urethrae . Generally the mice had only 1 rectum , but occasionally two were observed , as might be expected from cloacal thickening . Duplicated pelvic bones , kinked tails , agenesis or hypoplastic kidneys ( suggesting interference with mesonephric duct development ) , microphthalmia and other anomalies were noted . These are quite similar to the defects we described for Polypodia mice [15] . Danforth also identified some mutants with double spinal cord at the lumbar/thoracic region and variations in between , as well as neural tube defects . Subsequent studies found a duplicate neural tube without notochord in some E11–E12 . 5 mutant pelvic masses suggesting bifurcation or budding off from the primary neural tube secondary to duplication of organizer tissue or the primitive streak , but this was not formally examined [44] . We have not observed duplicated neural tubes in Ppd mutants , although we have seen split tails and some spinal dysraphism on a few occasions on the genetic backgrounds presented . Unfortunately , Duplicitas posterior mice no longer exist ( E . Center , personal communication ) . The mouse mutant Disorganization ( Ds ) causes a wide variety of malformations in the mouse compatible with an early postimplantation patterning disruption . This mutation maps to mouse chromosome 14 . Ds mice share many malformations [38–41] in common with those of Polypodia , yet there are differences . Ds mice do not exhibit prenatal lethality , either as heterozygotes or homozygotes [40] . It will be interesting to compare the molecular pathways affected in both mutants . Exogenous retinoic acid ( RA ) , given at E4 . 5–E5 . 5 ( blastocyst stage ) , produces a mouse Ppd phenocopy . Such mouse conceptuses develop caudal limb and lower body duplications [45–48]; [Innis et al . , unpublished] , duplicated genital buds , facial defects and exencephaly . RA-treated embryos also display facial anomalies , which were not described in detail [45] , although these were more frequently observed when RA exposure occurred on E6–E7 . In most affected embryos , normal hindlimb development , single tails , and ectopic , ventral , rudimentary or complete lower limbs or caudal structures with or without duplicated pelvic structures are produced . The susceptible gestational times ( E4 . 5–5 . 5 ) correspond to post-implantation stages before gastrulation . Thus , provision of RA at E4 . 5–5 . 5 to pregnant dams clearly reorganizes the mouse body plan , and since RA is cleared within 12 hours of administration [57 , 58] the effect of RA is immediately confined to cells at pre-gastrulation stages . We believe that Ppd , Ds , and retinoic acid exposure at E4/5–E5 . 5 impact similar developmental pathways leading to caudal duplications and other malformations . Sporadic mutants for which coding alterations are elusive may be secondary to similar spontaneous insertions . However , it remains to be determined how Ppd and these other models intersect within known developmental pathways and at what developmental timepoint ( s ) . Moreover , the principles that influence penetrance , expressivity and pleiotropy in Ppd phenotypes are certainly relevant to human disease . All mouse experiments were approved by the UM University Committee on the Use and Care of Animals , Protocol #07982 . Genetic crosses were carried out as described [15] . For narrowing the Ppd genetic interval , we genotyped visibly affected recombinant animals and utilized extended crosses ( offspring exceeding 80–100 animals for each ) of visibly unaffected CzechII/C3H F2 critical recombinants . Non-repetitive mouse genomic DNA segments were amplified by PCR and sequence verified to use as probes in Southern blots with ten micrograms of restriction enzyme digested mouse genomic DNA from wild-type and Ppd mutant mice . A 2212 bp Dusp9 probe , DUSP9 . 01 , corresponding to GRCm38 genomic coordinates ChrX:73641114–73643326 that includes Dusp9 gene sequences from the middle of intron 2 through most of the 3′ UTR of exon 4 , was amplified with primers 5′-GGGCACTTATCAGCCAAAGA-3′ and 5′-GGTGTGGACTGCAATGAATG-3′ . This DNA segment was labeled with 32P-dCTP and used according to standard Southern hybridization and washing protocols . ES cell genomic Southern blots were carried out as described [59] . X-chromosome specific primers used to amplify across the Ppd ETn as shown in Figure 2B were F1 ( 5′-AGCAAATGGTGGGACTGTGTAAT-3′ ) and R2 ( 5′-ACCCAGGACGATTGAAGATGTGC-3′ ) , which together generate a 1 . 278 kb product on wild-type DNA , but a 6 . 778 kb product including the ETn . Tail genomic DNA for genotyping was isolated by overnight proteinase K digestion , followed by extraction with phenol/chloroform/isoamyl alcohol and ethanol precipitation . Ppd mutation-specific PCR was performed using F5 ( X-chromosome specific ) and R6 ( ETn LTR ) primers that yielded a 248 bp Ppd-specific product in mutants . PCR success was assessed by including wild type forward and reverse primers in the same PCR that yielded a wild type product of 100 bp . Male Ppd mutant PCR yields only the 248 bp Ppd -specific product . F5 – 5′-TTACCAGGAGAAAGGACGCACTATGAG-3′ R6 – 5′-GCACCTTTCTACTGGACCAGAGATT-3′ WT Forward – 5′-TTGGGTCAAAGTTGAATGAAAATAGAAATAGC-3′ WT Reverse – 5′-CCCCGCCACTTCAGTGCTACC-3′ Thermocycling was carried out in 25 µL , 0 . 5 M betaine and 3 mM MgCl2 with an initial 2-min 97°C denaturation followed by 36 cycles of 97°C for 30 sec , 63°C for 30 sec and 72°C for 30 sec . The final extension was for 5 min at 72°C . Real-time RT-PCR was performed on an ABI Prism 7000 thermocycler ( Applied Biosystems , Foster City , CA . Gene-specific primers and probes were designed using Primer 3 program . Sequences for primers and probes for mouse Dusp9 , Pnck and β-actin are as follows: Mouse β-actin Forward Primer –AAGAGCTATGAGCTGCCTGA β-actin Reverse Primer – CAAGAAGGAAGGCTGGAAAAGAG Probe – 6FAMAACGAGCGGTTCCGATGCCCTGTAMRA Mouse Dusp9 Forward Primer – GGCATCCGCTATATCCTCAA Dusp9 Reverse Primer – GGGGATCTGCTTGTAGTGGA Probe – 6FAMCCCCAACCTTCCTAACCTCTTAMRA Mouse Pnck Forward Primer – CTCCCGGTTTTTCTTTCCTC Pnck Reverse Primer – ATGCATCACACCCAGTCTCA Probe – 6FAMTGGATCCTTGTCCTCCAGACTAMRA RNA was extracted using TRIzol reagent ( Invitrogen ) from at least three independent preparations of mouse ES cells , Ppd-ES cells and eETn ES cells . Each RNA sample ( 0 . 5 µg ) was tested in triplicate using TaqMan one-step RT-PCR master mix reagents from Applied Biosystems . Average cycle threshold ( CT ) was determined for each sample and normalized to β-actin . Relative gene expression ( using the formula 2−ΔΔCT ) was calculated using the comparative CT method , which assesses the difference in gene expression between the gene of interest ( Dusp9 ) and an internal standard gene ( β-actin ) for each sample to generate the ΔCT [59] . The difference of the ΔCT for each experimental cell line from the ΔCT the control cell line BRUCE4 . G9 is referred to as ΔΔCT . The average of the control sample ( BRUCE4 . G9 ) was set to 1 for each experiment , and the relative gene expression ( fold change ) for each experimental sample was compared with that . We obtained Ppd blastocysts by mating 24–28 day old pseudopregnant Ppd CD-1 ( >90% CD-1 ) females , recovering blastocysts at E3 . 5 by uterine flushing , and single-well plating on feeder cells . Following the identification of male cells carrying the Ppd ETn , we established mutant ES cell lines Ppd-D3 , Ppd-D5 , and Ppd-C4 . ES culture procedures were performed as described in [60] . Mouse ES cells were maintained on γ-irradiated mouse embryonic fibroblasts ( PTMN cells: pretreated , mouse embryonic fibroblasts , neomycin resistant ) in Dulbecco's modified Eagle's medium ( DMEM ) supplemented with 15% fetal calf serum ( Atlanta Biologicals ) , 1000 U/ml LIF ( Millipore ) , 4 mM L-glutamine , 1% non-essential amino acids , 0 . 1 mM β-mercaptoethanol , 1% sodium pyruvate , and 1% penicillin/streptomycin . For RNA/DNA/protein analysis , ES cells were grown on gelatin coated plates without feeder cells , passed twice sequentially to eliminate PTMN feeder cell contamination , in DMEM with 15% fetal calf serum and 1000 U/ml LIF . RNA was isolated using TRIzol from ES cells after passage twice sequentially on gelatin coated plates without feeder cells . Biotinylated cDNA was prepared from 50 ng total RNA according to the Nugen ovation V2 kit protocol ( NuGen , Inc . ) . Following labeling , 4 µg of cDNA was hybridized for 16 hours at 45°C on GeneChip Mouse 430 2 . 0 arrays . GeneChips were washed and stained in the Affymetrix Fluidics Station 450 and then scanned with an Affymetrix 3000 7G GeneChip Scanner . Data quality analysis revealed no degradation and robust in vitro translation . Standard error estimates for each gene were derived and then standardized across all arrays , all of which showed high quality samples . A robust multi-array average ( RMA ) modeling strategy [61] was used to convert the PM probe values into expression values for each gene . Since we compared three normal ES cells lines to three Ppd ES cell lines , we used weighted linear models [62] , pooling information from all probe sets , to stabilize the variance estimate . Weighting was accomplished by a gene-by-gene algorithm that downweights samples deemed less reproducible [63] . We removed probe sets across sample comparisons ( Male WT versus Male Ppd ) that had a variance of less than 0 . 1 and then selected genes with a fold-change greater than 2 and an adjusted p-value ( adjusted for multiple comparisons using false discovery rate , FDR ) of less than 0 . 05 [64] . We used the Affy , AffyPLM and limma packages of Bioconductor in the R statistical environment . To place the ETn into a wild-type mouse genome , we first created a BAC library ( in vector pIndigoBAC5 ) from Ppd male genomic DNA utilizing the services of Bio S&T ( Lachine , Quebec ) and isolated 2 BAC clones spanning the Ppd ETnII insertion site on the X chromosome and surrounding genes spanning over 170 kb . We selected one clone ( Ppd BAC Clone 2 ) with a 170 kb insert and used BAC recombineering to construct a targeting vector through the UC Davis Mouse Biology Program ( http://mouse . ucdavis . edu/ineed/vectors_constructs . php ) . The strategy of construction began with the BAC . Ppd BAC Clone 2 was electroporated into EL350 and selection with chloramphenicol was used to isolate colonies . A frt-flanked PGK-Neo was inserted into the BAC just upstream of the 5 . 5 kb ETn insert via BAC recombineering and clones were selected with kanamycin ( PGK-Neo confers kanamycin resistance in bacterial cells ) , and chloramphenicol . The region containing the ETn , frted PGK-Neo , and 5′ ( 5 kb ) and 3′ ( 10 kb ) arms of homology was retrieved into a high-copy plasmid followed by selection with kanamycin and ampicillin ( retrieval vector confers Ampr ) . A Gateway reaction was then used to swap in the DTA negative selection marker followed by selection with kanamycin , which replaced the retrieval vector portion , and removed the Ampr cassette . Finally , a separate electroporation to isolate the targeting vector with the insertion followed by kanamycin selection was performed . Sequencing of all junctions created by recombineering revealed the expected insert structure . Sequencing of the 5′ ( 5 kb ) and 3′ ( 10 kb ) endogenous mouse genomic DNA arms of the targeting vector revealed not only the ETn , but also one common non-coding SNP , rs29038663 , a C>T substitution at ChrX:73646920 , 1 , 767 base pairs telomeric ( closer to the Pnck gene ) to the ETn . We targeted Bruce-4 . G9 and UMB6J-D7 ( a pure BL/6 line ) ES cell lines . Three hundred ES cell clones from each electroporation were picked and expanded . Southern blotting and Ppd ETn-specific locus PCR revealed a very high frequency of homologous recombination in both cell lines ( 27–50% ) . Germline transmission was successful in generating female engineered ETn ( eETn ) heterozygotes ( Neo+/eETn+ ) . We bred these females to β-actin FLPe males ( Jackson Lab stock #005703 ) , to remove the Neo cassette and obtained germline Neo−/eETn+ mutant mice for phenotypic analysis . Ppd-CD-1 mutant female mice were kept for overnight mating with a CD-1 WT male . Conception was defined by the presence of a vaginal plug the following morning , and the age of embryos calculated from midnight . Pregnant Ppd-CD-1 female mice were euthanized by carbon dioxide asphyxiation at E7 . 5 . Embryos were immediately dissected from the uterus in cold PBS under a dissecting microscope , and a portion of the ectoplacental cone and yolk sac were used for DNA isolation . Briefly , 20 µL alkaline lysis reagent ( 25 mM NaOH/2 mM EDTA ) was added to the tissue samples , and the mixture was incubated at 95°C for 20 minutes followed by neutralization using 20 µL 40 mM Tris-HCl . Genomic DNA was then used for genotyping using sex and Ppd genotyping . RNA was extracted from the embryos using Trizol reagent ( Invitrogen ) according to the manufacturer's instructions . Embryo sex was determined as described [15] using XX-XY forward and reverse primers that produce a ∼300 bp single product in females and a doublet in males . Thermocycling was carried out in 25 µL containing 0 . 5 M betaine and 3 mM MgCl2 with an initial 2-min 97°C denaturation followed by 36 cycles of 97°C for 30 sec , 63°C for 30 sec and 72°C for 30 sec . The final extension was for 5 min at 72°C . Primers: XX-XY forward: CCGCTGCCAAATTCTTTGG; XX-XY reverse: TGAAGCTTTTGGCTTTGAG . Ppd genotyping was as described above . ES cells grown on tissue culture plates were washed with phosphate-buffered saline ( PBS ) and lysed in 0 . 4 ml of ice-cold RIPA lysis buffer ( 1% sodium deoxycholate , 0 . 1% SDS , 0 . 15 M NaCl , 0 . 01 M NaH2PO4 , 2 mM EDTA , 0 . 5 mM NaF ) containing 2 mM sodium orthovanadate and 1∶1000 dilution of protease inhibitor mixture III ( Calbiochem ) . Protein concentrations were determined using the DC protein assay reagents from Bio-Rad ( Hercules , CA ) . SDS-PAGE and Western blot analysis were performed . Cell lysates were mixed with a 1∶5 v/v ratio of 6× gel loading dye ( 0 . 35 M Tris-HCl pH 6 . 8 , 30% glycerol , 10% SDS , 0 . 6 M DTT , 0 . 012% bromophenol blue ) and boiled at 95°C for 5 min to denature proteins . Sample mixtures were then loaded on 4–20% polyacrylamide gradient gels and subjected to electrophoresis . Proteins were electrophoretically transferred to a polyvinylidene difluoride membrane ( Immobilon–P , Millipore Inc . , Bedford , MA ) and incubated in 1× Tris-buffered saline ( pH 7 . 4 ) , 0 . 1% Tween 20 with 5% bovine serum albumin for 1 h at room temperature . The blot was incubated with 1∶1000 dilution of primary antibody in blocking buffer overnight at 4°C . Three washes with 1× TBS with 0 . 1% Tween 20 were performed prior to incubation with a secondary antibody conjugated to horseradish peroxidase . The washes were repeated five times , and the membrane was incubated with SuperSignal West Pico chemiluminescent substrate ( Thermo Scientific , Rockford , IL ) for 5 min . The blot was then exposed to chemiluminescent-sensitive HyBlot CL autoradiography film ( Denville Scientific Inc . , Metuchen , NJ ) . Image analysis was performed using a public domain NIH Image program available on the internet at rsb . info . nih . gov/nih-image . Sources of antibodies used in this study were as follows . Sheep anti-mouse DUSP9 polyclonal antibody , raised against two DUSP9 peptides ( residues 237–261 and residues 429–451; [24] ) was a gift from Dr . Robin Dickinson , University of Dundee , UK . From Santa Cruz Biotechnology ( Santa Cruz , CA ) : MKP-4 rabbit polyclonal antibody raised against a single DUSP9 peptide corresponding to residues 231–270 . From Bio-Rad: HRP conjugated anti-sheep secondary antibody . From Thermo Scientific ( Rockford , IL ) : Peroxidase conjugated goat anti-rabbit IgG and peroxidase conjugated anti-mouse IgG . Mouse monoclonal β-actin antibody was from Sigma . Synthesized peptides ( DUSP9 peptide 231–274 and a PNCK 30 amino acid peptide ) used in specificity assays were produced in the UM Protein Structure Facility . Tail samples were taken from 14 day old mice . Genomic DNA from an adult animal was used for comparison of LTR methylation between tail or other organ versus caudal ectopic mass . DNA was prepared from the samples and PCR was performed to confirm the presence of the ETn insertion . Once confirmed , the DNA was purified and treated with bisulfite using established protocols in the Qiagen EpiTect Bisulfite Kit . The bisulfite treated DNA ( btDNA ) samples were subjected to PCR using the primers EpiF4 ( 5′- GGTAAAAGAAGAAATGTAGTTAAGATAGTT-3′ ) targeting the modified LTR , and EpiR5 ( 5′- AAACTCCCCAAAACAAAACACTATA -3′ ) targeting the modified X chromosome sequences ( ChrX:73645196–73645220 ) upstream of the 5′ LTR . One reaction contained , 15 . 6 µL ddH2O , 2 . 5 µL 10× JumpStart PCR Buffer , 0 . 5 µL dNTP's , 1 . 25 µL Primer F4 , 1 . 25 µL Primer R5 , 0 . 4 µL JumpStart Taq , and 2 . 5 µL of 5 M Betaine . Each reaction also contained ∼200 ng of btDNA . The PCR program used was: 97°C ( 2 min ) , 97°C ( 30 sec ) , 46°C ( 30 sec ) , 72°C ( 1 min ) , Step 2 ( 40× ) , 72°C ( 10 min ) , 4°C ( ∞ ) . A second round of PCR was set up identical to the first , except 2 µL from the first round of PCR was used as the template for the second round PCR . No purification was necessary between PCR rounds . PCR reaction products were separated by electrophoresis on a 2% agarose gel . The bands were extracted and purified using a Qiagen Gel Extraction Kit . The PCR products were TA-cloned into a pGEM-T easy vector . The ligation was then electroporated into DH5α cells and plated onto LB agar with carbenicillin . Individual colonies were selected and grown overnight . Plasmid DNA from individual colonies was extracted and individual clones were sequenced in the University of Michigan DNA Sequencing Core with T7 and SP6 primers . Bidirectional sequences were scanned for the targeted CpG dinucleotide as well as unmethylated cytosine modifications .
Mobile genetic elements , particularly early transposons ( ETn ) , cause malformations by inserting within genes leading to disruption of exons , splicing or polyadenylation . Few mutagenic early transposon insertions have been found outside genes and the effects of such insertions on surrounding gene regulation is poorly understood . We discovered a novel intergenic ETnII-β insertion in the mouse mutant Polypodia ( Ppd ) . We reproduced the mutant phenotype after engineering the mutation in wild-type cells with homologous recombination , proving that this early transposon insertion is Ppd . Mutant mice are not born in expected Mendelian ratios secondary to loss after E9 . 5 . Embryonic stem cells from mutant mice show upregulated transcription of an adjacent gene , Dusp9 . Thus , at an early and critical stage of development , dysregulated gene transcription is one consequence of the insertion mutation . DNA methylation of the ETn 5′ LTR is not correlated with phenotypic outcome in mutant mice . Polypodia is an example of an intergenic mobile element insertion in mice causing dramatic morphogenetic defects and fetal death .
You are an expert at summarizing long articles. Proceed to summarize the following text: Protein evolution is most commonly studied by analyzing related protein sequences and generating ancestral sequences through Bayesian and Maximum Likelihood methods , and/or by resurrecting ancestral proteins in the lab and performing ligand binding studies to determine function . Structural and dynamic evolution have largely been left out of molecular evolution studies . Here we incorporate both structure and dynamics to elucidate the molecular principles behind the divergence in the evolutionary path of the steroid receptor proteins . We determine the likely structure of three evolutionarily diverged ancestral steroid receptor proteins using the Zipping and Assembly Method with FRODA ( ZAMF ) . Our predictions are within ∼2 . 7 Å all-atom RMSD of the respective crystal structures of the ancestral steroid receptors . Beyond static structure prediction , a particular feature of ZAMF is that it generates protein dynamics information . We investigate the differences in conformational dynamics of diverged proteins by obtaining the most collective motion through essential dynamics . Strikingly , our analysis shows that evolutionarily diverged proteins of the same family do not share the same dynamic subspace , while those sharing the same function are simultaneously clustered together and distant from those , that have functionally diverged . Dynamic analysis also enables those mutations that most affect dynamics to be identified . It correctly predicts all mutations ( functional and permissive ) necessary to evolve new function and ∼60% of permissive mutations necessary to recover ancestral function . Proteins are effective and efficient machines that carry out a wide range of essential biochemical functions in the cell . Beyond being robust and efficient , the outstanding property of proteins is that they can evolve and they show a remarkable capacity to acquire new functions and structures . In fact , modern proteins have emerged from only a few common ancestors over millions to billions of years [1]–[3] . Moreover , the emergence of drug resistance and enzymes with the capacity to degrade new chemicals indicates the ongoing contemporary evolution of proteins [1]–[7] . Therefore , understanding the mechanism by which mutations lead to functional diversity is critical in many aspects from protein engineering to drug design and personalized medicine . Indeed , computational protein design through analysis of mutations has attained major breakthroughs , with profound biotechnological and biomedical implications: design of a new fold [8] , design of new biocatalysts and biosensors [9]–[11] , design of binding affinity [12] , [13] , and design of proteins to bind non-biological cofactors [14] . Moreover , there are computational bioinformatics-based tools based on evolutionary information aspects to identify mutations leading to functional loss or disease [15]–[17] . From a phylogenetics perspective , horizontal and vertical approaches have been used to analyze the set of mutations that lead to changes in protein function throughout evolution [18] . The horizontal approach compares modern day proteins at the tips of the evolutionary tree . It identifies the amino acid residue differences within the functionally divergent members of a protein family based on primary sequence and structural analyses and then characterizes the functional role of these residues by swapping them between these family members through site-directed mutagenesis in the laboratory to check for loss of function [19]–[21] . Although the horizontal method gives insight into mutations critical to function , it often fails to identify permissive mutations necessary to switch function between family members . Protein function has evolved as mutations throughout history , i . e . “vertically” , in the ancestral protein lineages . Therefore , it is important to incorporate the historical background which contains both neutral and key function-switching mutations when examining function-altering mutations [18] . The vertical approach determines the likely ancestral sequences at nodes along the evolutionary tree and compares modern day proteins to their ancestors . Recent advances in molecular phylogenetic methods make it possible to obtain ancestral sequences by protein sequence alignments in a phylogenetic framework using Bayesian and Maximum Likelihood methods [22] , [23] . DNA molecules are synthesized coding for the most probable ancestral sequences and the protein expressed , allowing for experimental characterization of the ancient protein . The vertical approach has been used to gain insight into the underlying principles of protein function and evolution in several proteins including opsins [24] , [25] , GFP-like protein [26] , [27] , and others [28]–[32] . More recently , a vertical analysis of two ancestral nuclear receptors has been coupled with X-ray structure determination in successfully elucidating the switching of function between divergent members [33] , [34] . Such studies highlight the importance of including ancient protein structures into evolutionary studies . Although coarse-grained and all-atom models have furthered our understanding of sequence/structure relationship in evolution , further study of the inherent structural dynamics is crucial to give a more complete understanding of protein evolution [35] . A small local structural change due to a single mutation can lead to a large difference in conformational dynamics , even at quite distant residues due to structural allostery [36]–[38] . Thus the one sequence-one structure-one function paradigm is being extended to a new view: an ensemble of different conformations in equilibrium that can evolve new function [1] , [39]–[41] . The importance of structural dynamics has been demonstrated by a recent experimental study which shows that mutations distant from a binding site can increase enzyme efficiency by changing the conformational dynamics [42] . The modulation of rigidity/flexibility of residues both near and distant from the active region ( s ) as related to promiscuous and specific binding has also been noted in tRNA synthetase complexes [43] , [44] . Here we have developed a method to predict structural and dynamic evolution of ancestral sequences by using a modified version of our protein structure prediction tool , Zipping and Assembly Method with FRODA ( ZAMF ) [45] . ZAMF combines two crucial features of ZAM [46] , and FRODA [47] , [48] : i ) FRODA is a constraint-based geometric simulation technique that speeds up the search for native like topologies by accounting only for geometric relationships between atoms instead of detailed energetics , ii ) Molecular dynamics identifies the low free energy structures and further refines these structures toward the actual native conformation . Thus , it is a two-step multi-scale computational method that performs fast and extensive conformational sampling . As an outcome , we not only predict protein structures but also obtain detailed conformational dynamics of the predicted structures . With modified ZAMF , we analyze the role of structural dynamics in the evolution of three ancestral steroid receptors ( AncCR , AncGR1 and AncGR2 ) , the ancestors of mineralocorticoid and glucocorticoid receptors ( MR and GR ) . MR and GR arose by duplication of a single ancestor ( AncCR ) deep in the vertebrate lineage and then diverged function . MR is activated by aldosterone to control electrolyte homeostasis , kidney and colon function and other processes [33] . It is also activated by cortisol , albeit to a lesser extent [18] . On the other hand , GR regulates the stress response and is activated only by cortisol [33] . The structural comparison of human MR and GR ( i . e . horizontal approach ) suggested the two mutations ( S106P and L111Q ) to be critical in ligand specificity , however , swapping these residues between human MR and human GR yielded receptors with no binding activity [49] . Conversely , by resurrecting key ancestral proteins ( AncCR , AncGR1 and AncGR2 ) in MR and GR evolution and determining the crystal structures , Thornton et al . were able to shed insight into how function diverges through time by using both functional and permissive ( compensatory ) mutations [33] , [34] . AncCR ( main ancestor ) , ∼470 million years old , is a promiscuous steroid receptor which is activated by aldosterone , cortisol , and deoxycortisol ligands . AncCR branched into the mineralocorticoid steroid receptors . AncGR1 ( ancestor of sharks ) is ∼440 million years old with 25 mutations from AncCR and also promiscuously binds to and functions with aldosterone , cortisol , and deoxycortisol . AncGR1 later evolved into the Elasmobranch glucocorticoid receptor protein . AncGR2 ( ancestor of humans and fish ) is ∼420 million years old with 36 mutations from AncGR1 and preferentially binds to cortisol alone . These two ancestral proteins , AncGR1 and AncGR2 , which diverge functionally , have highly similar experimental structures that have <1 Å RMSD between them . Among 36 mutations between AncGR1 and AncGR2 , two conserved mutations {S106P , L111Q} ( i . e . group X ) when introduced together are sufficient to increase cortisol specificity . However three more functionally critical conserved mutations {L29M , F98I , S212Δ} ( i . e . group Y ) are needed for the loss of aldosterone binding activity when they are introduced together with two other permissive ( i . e . compensatory ) mutations {N26T and Q105L} ( i . e . group Z ) . Thus , making the X , Y , Z mutations in AncGR1 enables AncGR1 to function as AncGR2 ( i . e . forward evolution ) [34] . To make AncGR2 function as AncGR1 ( backward evolution ) the X , Y , Z mutations are insufficient and render the protein inactive . A fourth set of permissive mutations ( W ) is required to reverse function in addition to the X , Y , and Z , sets . The W mutation set is {H84Q , Y91C , A107Y , G114Q , L197M} [33] . A mutation between AncCR and AncGR1 , Y27R , is also a necessary mutation to eventually alter function to cortisol specificity , though it was not experimentally considered as part of the X , Y , Z , or W mutation sets [34] . We ask here whether an analysis of the predicted 3-D structures and corresponding equilibrated dynamics can distinguish the functional divergence and function swapping mutations between AncCR , AncGR1 , and AncGR2 . By applying ZAMF , we obtain the 3-D structures within ∼2 . 7 Å all-atom RMSD of the experimental structures . More importantly , when we analyze their structure-encoded dynamics , we observe that changes in the dynamics indicate functional divergence: that the most collective fluctuation profiles of AncCR and AncGR1 ( i . e . the slowest mode ) are much closer and distinctively separated from the functionally divergent AncGR2 . Moreover , AncCR and AncGR1 have a more flexible binding pocket , suggesting the role of flexibility in their promiscuous binding specificity . On the other hand , the mutations of AncGR2 lead to a rigid binding pocket , which suggests that as the binding becomes cortisol specific , evolution acts to shape the binding pocket toward a specific ligand . Finally , using their mean square fluctuation profiles and cross correlation maps to analyze the change in dynamics at each residue position enables us to distinguish critical mutations needed for swapping the function . Overall , all these findings suggest that conformational epistasis may play an important role where new functions evolve through novel molecular interactions and an analysis of detailed dynamics might provide insight into the mechanisms behind these novel interactions . Many of the modern day homologs to ancestral proteins in the steroid receptor class of the nuclear receptor superfamily have high sequence similarity ( ∼40–50% ) , and , as prediction accuracy scales with sequence similarity [50]–[52] our secondary structures for the ancestral sequences are sufficiently accurate to provide native-like structures [45] . Indeed , predicted secondary structures are all correct within one residue to the experimentally determined ancestral cortisol receptor protein [34] . Using these secondary structures as input to the assembly and refinement stages of ZAMF , we determine the 3D structure of the AncCR from its experimentally determined structure to 2 . 5 Å all atom RMSD ( 2 . 2 Å backbone ) , AncGR1 from its experimentally determined structure to 2 . 9 Å all atom RMSD ( 2 . 6 Å backbone ) AncGR2 from its experimentally determined structure to 2 . 9 Å all atom RMSD ( 2 . 4 Å backbone ) ( Fig . 1 and Table S1 ) . To test the accuracy of these predictions , we first compare the structural differences between the experimental structures . The experimental structures are very similar , with an RMSD of 1 . 49 Å between AncCR and AncGR1 , 1 . 68 Å between AncCR and AndGR2 , and 1 . 70 Å between AncGR1 and AncGR2 . However alignment excludes the atoms of the mutational residues . We also ran a 4 ns REMD simulation of the experimentally determined AncCR and AncGR2 under the same conditions . The ensembles for AncCR and AncGR2 converges at ∼2 . 5 Å backbone RMSD from their respective experimentally determined structures ( Fig . S1 ) . The 2 . 5 Å RMSD indicates that our predicted structures are as accurate as our force field permits . Closer analysis reveals that helix h9 in the predicted structure of AncGR2 is slightly less stable than in the experimental structure REMD simulations . However , both simulations show a high degree of flexibility in the loop region between helices h9 and h10 and ends of helices h9 and h10 at this loop region . As these three proteins diverged in function and have >10% sequence mutation between each successive protein , we expect to see some differences in structure . Therefore , we first look at a mean square displacement ( MSD ) between the static structures of AncCR , AncGR1 and AncGR2 . The MSD versus residue profile gives an indication of which residues are mutating , as mutated residues pack into stereochemically unique conformations ( Fig . S2 ) . Fig . S2 reveals conformational shifts in helices h7 and h10 and in the β-sheet region , b1 . We attempt to determine which of the 36 mutated residues between AncGR1 and AncGR2 are critical for cortisol binding specificity through distinguishing residues having an MSD cutoff of >6 Å2 between the AncGR1 and AncGR2 predicted structures . The residues identified from X , Y , Z and W sets are Y91C , Q105L , and S212Δ , with no false positives . The S212Δ and Q105L mutations are permissive mutations to shift function to cortisol specificity whereas Y91C is a permissive mutation necessary for “reverse evolution” i . e . to return binding promiscuity to AncGR2 . Experimental work indicates that S212Δ removes a hydrogen bond and imparts greater mobility to the loop before the activation function ( AF ) helix , allowing it to hydrogen bond with helix h3 , while Q105L indirectly restores a hydrogen bond with the activation helix by allowing for tighter packing of helices h3 and h7 [34] . An analysis of hydrogen bonding patterns [53] shows the loss of the S212 hydrogen bond with V217 ( in the loop before the AF helix ) in the AncGR2 structure as compared to the AncCR/AncGR1 structures , agreeing with experimental results . Y91C is one of the W mutations required for reverse evolution of AncGR1 from AncGR2 and we find it forms a hydrogen bond with N86 in AncGR2 but does not in AncCR or AncGR1 . Interestingly , none of these mutations occur in the binding pocket itself . Therefore , an MSD analysis is not sensitive enough to find functionally critical mutations in the binding pocket , and only finds a few of the necessary mutations to diverge function . We investigate the role of structural dynamics in functional divergence observed among the three ancestral steroid proteins . The extensive conformational sampling of our method enables us to capture the dynamics along with the most native-like structure ( Fig . S4 ) . We obtain the most collective modes of these three ancestral structures ( i . e . slowest fluctuation profiles ) through principal component analysis of our restraint-free trajectories ( See Method ) . We then form an Mx3N matrix where the M columns are the eigenvectors weighted by their eigenvalues , with each M column being a 3 column super-element composed from the slowest modes of AncCR , AncGR1 and AncGR2 and N being the number of C-α atoms . We chose to analyze the top 10 slowest modes and therefore there are 30 columns . By performing a singular value decomposition on this matrix , we measure how the most collective motions of these three ancestral proteins are distributed in dynamic space . Interestingly , as shown in Fig . 2A , AncCR and AncGR1 are much closer and distinctively separated in dynamic space from the functionally divergent ancestor of the human glucocorticoid receptor , AncGR2 . Clustering in dynamics space is significant because it shows that these structurally similar but functionally unique proteins differ in functionally governing dynamics , as observed in previous studies [42] , [54]–[56] . Moreover , previous studies indicate that functionally critical mutations alter modes that characterize biologically functional motion , while random sequence variations typically have non-statistically significant impact on those modes [57] . These findings indeed suggest that the governing functional dynamics is encoded within the structure and that only critical mutations lead to a shift in collective motion and therefore in binding selectivity as well [55] , [58] . Fig . 2B presents the color coded ribbon diagrams of these three ancestral proteins with respect to their functionally related collective fluctuation ( obtained by PCA ) profiles within a spectrum of red to blue , where rigid regions are denoted by blue/green and flexible regions are denoted with red/orange . Experimentally determined function altering mutations are highlighted in the sphere representation . Strikingly , residues in and near the functional site ( i . e . binding site ) are much more flexible for the two promiscuous enzymes ( AncGR1 and AncCR ) whereas the human ancestor AncGR2 , which has affinity only to cortisol , has very rigid functional site residues . The new view of proteins states that , rather than a single structure with induced binding , proteins interconvert between bound and unbound conformations in the native ensemble . Thus , promiscuous binding proteins utilize greater flexibility to interconvert between a greater number of conformations in the native ensemble as compared to specific binding proteins . Therefore , our dynamic analysis agrees with the new view that while the promiscuous ancestors are more flexible around the functional site , the functional site rigidifies as Nature biases towards binding only a single ligand with greater affinity [1] . Upon confirmation that dynamics can indeed distinguish functional divergence , the next question is whether dynamics can indicate which residues in the protein are critical to diverging function . We investigate whether we can distinguish the mutations , including function altering and permissive ( i . e . compensatory ) , that cause AncCR/GR1 to shift function to specifically bind cortisol as AncGR2 does , and also those that reverse the function of AncGR2 to promiscuously bind in the same way as AncCR/AncGR1 . To identify the critical residues for swapping function , we analyze how the fluctuation profile changes over these three successive ancestral proteins . Thus , using their most collective fluctuation profile ( i . e the slowest mode obtained by PCA ) , we compute the net change in fluctuation from AncCR to AncGR1 and AncGR1 to AncGR2 and show them in a 2-D plot to distinguish the mutations that have a higher impact on the change in dynamics between AncGR2 and AncGR1 compared to those mutations affecting the change in dynamics between AncGR1 and AncCR ( Fig . 3 ) . The upper left region of the graph in Fig . 3 indicates mutations that most alter dynamics when comparing the function-altering mutation from AncGR1 ( binding promiscuity ) to AncGR2 ( binding specificity to cortisol ) whereas the lower right region of the plot indicates mutations that most alter dynamics when comparing AncCR and AncGR1 , which do not diverge functionally . The central region of the graph ( between the parallel cutoff lines ) contains those mutations that do not alter the dynamics in a significantly different manner between successive homologs . Interestingly , most of the function altering mutation sites such as 106 , 212 ( shown as 211 and 213 due to deletion ) and most of the W mutations ( mutations necessary for backward evolution , e . g . altering AncGR2 to become promiscuous ) are in the upper left region . Permissive mutations 27 , 29 , 105 , and the mutations in the activation function helix are in the lower right region of the plot . 111 , a critical mutation for changing the specificity to cortisol only , is also in the lower right region . However , experimental analysis showed that the 111 mutation alone does not alter function in any appreciable manner . Thus , we propose it is only after permissive mutations alter the dynamics at site 111 can the necessary critical mutation at site 111 have a function altering effect . Additionally , certain mutations such as 214 and 173 both show large dynamic transitions . Mutation 214 is associated with the loop region that contains the critical mutation S212Δ , and it is in at the edge of a loop region . It undergoes transitions between being at the end of the h10 helix to being in the loop . The change in dynamics can be associated with the S212Δ mutation to identify the loop as a critical region . The 173 mutation is in a region that was not able to be crystallized in the experimental AncCR structure . Though the REMD simulations were determined to have converged , there is a possibility of some influence near site 173 due to the loop having to be built into the structure prior to REMD simulation . However , we expect that the shift in dynamics at mutation 173 may be correlated with movement of helix h10 , and is therefore potentially significant . We also obtain the net absolute change in the successive Δr2 fluctuation profiles along the slowest mode using the formulation ∥ΔfluctuationAncCR-AncGR1|–|ΔfluctuationAncGR1-AncGR2∥ for mutated residues based the alignment of AncCR and AncGR2 ( Fig . 4A ) and predict those residues with a net |ΔΔfluctuation|>0 . 002 Å2 to be critical . The forward mutations required to shift function to cortisol specificity are N26T , L29M , F98I , Q105L , S106P , L111Q , and S212Δ , and all of these are captured as critical as they are above the cutoff . The reverse mutations required to shift function from cortisol specific to promiscuous binding are H84Q , Y91C , A107Y , G114Q , and L197M . With the chosen cutoff , the identified permissive mutations are H84Q , A107Y , and G114Q , with Y91C only slightly below the cutoff . Interestingly , A107Y is the only W mutation that by itself partially recovered the promiscuous binding function [33] and it shows a high |ΔΔfluctuation| in our plot . We also find eight other mutated residues above the cutoff . Three of those are false positives I65L , Q117K and M158I . Each of these mutations occurred between AncCR and AncGR1 , prior to a shift in function . Among mutations identified is Y27R , which is not explicitly in the X , Y , or Z set , yet it is highly conserved in the GR family and is an experimentally determined permissive mutation critical for GR function [34] . The three mutations at the activation function helix are also identified as critical . The other mutation above the cutoff is 211 , which is correlated with S212Δ . Overall , our dynamic method identifies all mutations that are necessary for the evolution of GR function . We also distinguish three of the five mutations necessary for reversal of evolution ( e . g . permissive mutations to AncGR2 which are necessary to recover the promiscuous binding of AncCR/AncGR1 ) . Interestingly , many of the identified critical mutations such as N26T , H84Q , Y91C , F98I , Q105L , and S212Δ , are not interacting with the ligand , but rather are distant from the binding pocket ( i . e . >5 Å from any atom in the ligand ) . Additionally , the high |ΔΔfluctuation| at the C-terminus is associated with the activation-function ( AF ) helix , which does not contain critical mutations but its dynamics is critical to function . We also investigate the pairwise cross correlations of AncGR1 and AncGR2 ( Fig . 4B ) . Interestingly , comparing the cross correlations reveals differences along the regions containing critical mutations . The cross-correlations between helix h5 ( containing the critical mutation H84Q ) and helix h7 ( containing the critical mutations: Q105L , S106P , A107Y , L111Q , G114Q ) become highly positively correlated in AncGR2 whereas there is no correlation in AncGR1 . Analysis of hydrogen bonds [53] in predicted structures showed that additional hydrogen bonds are found between the β-sheet b1 and helices h5 and h7 , indicating the observed increased correlation in AncGR2 is likely due to the repacking of helices h5 and h7 after mutation which incorporates/creates these new hydrogen bonds . Moreover , we also observe increased positive correlations between the AF-helix and helices h3 and h10 in AncGR2 . These regions contain multiple permissive mutations ( N26T , L29M , L197M , S212Δ ) and thus , the change in correlations relate to the change in the stability of the AF helix caused by these permissive mutations necessary to alter function [34] . Furthermore , in Fig . 4C we compare the cross correlations of the most critical mutation for swapping the function to GR ( X mutations ) and the permissive mutations necessary to reverse the function to MR ( W mutations ) between AncGR1 and AncGR2 . In AncGR2 these mutations are significantly more correlated than in AncGR1 . This indeed suggests that W mutations play a critical role for GR function from the dynamics-perspective and therefore , they also need to be reversed along with the X , Y , Z mutation to recover the MR function . To test the robustness of our method in other proteins we repeated our method for benign and disease associated mutations [59]–[61] in the human ferritin protein [62] ( Fig . S5 ) . We observe that , indeed , benign and disease associated mutations are individually clustered together while separated from each other in dynamics space . In summary , by comparative dynamics analysis among the three ancestral steroid hormone receptors we identify all functionally critical and permissive mutations necessary to evolve new function from the ancestral MR promiscuous binding proteins to the ancestral GR cortisol-specific binding proteins . We also identify 60% of the permissive mutations necessary to revert to ancestral function along with an additional functionally critical mutation . We observe significant loss of flexibility in key residues both near and distant from the binding pocket in the transition from promiscuous to specific binding . A loss in flexibilty agrees well with the new view of proteins being conformationally dynamic in which bound and unbound conformations are sampled within the native ensemble . Thus , proteins evolve not just through those mutations that alter function in the immediate sense , but also due to those mutations that are permissive and alter the dynamic space in which the protein exists , thereby giving the protein the potential to evolve new function . We previously used the Zipping and Assembly Method with FRODA [ZAMF] [45]–[48] , [63] on a set of test proteins to predict the 3D structure from their 1D amino acid sequence . Here , we slightly modify ZAMF for the prediction of ancestral protein structures , particularly the three ancestral steroid receptor proteins , the corticoid receptor [AncCR] , the glucocorticoid/corticoid receptor [AncGR1] , and the glucocorticoid receptor [AncGR2] [33] , [34] . Since structure is more conserved than sequence [64]–[66] , we incorporate structural data acquired from modern day homologues into our prediction method . The modified version of ZAMF as outlined in Fig . 5 includes several steps: ( i ) obtaining secondary structural motifs and common contacts based on modern homologs , ( ii ) generation of an unfolded ensemble , ( iii ) generation of compact-native like conformations using FRODA , and ( iv ) refinement by ZAMF . Overall , all these steps lead to an extensive search in conformational space , which comes with several advantages . First , we increased our prediction accuracy for native structures compared to the previous version of ZAMF . Second , we obtain converged dynamics trajectories through the refinement stage of ZAMF , which is used for dynamic evolution analysis of the ancient proteins . We summarize each step in our approach below . Convergence is critical and , as such , a sample window of 1 ns is slid along the trajectory at 0 . 5 ns intervals and Principal Component Analysis is done . The PCA is done by first aligning and centering each snapshot of the trajectory to remove the translations and rotations , generating a matrix Xn for each sampling window ( 1 ) where xn are 3N dimensional position vectors and the < > denote a time average for a specific sampling window . Then , the covariance matrix of that sampling window , Cn , n , is calculated by ( 2 ) From the covariance matrix , the matrix of eigenvectors ( Vn ) and the matrix of eigenvalues ( Λn ) are ( 3 ) The eigenvectors and eigenvalues are sorted in order of decreasing eigenvalue and only the top 30 are kept as , once converged , any higher order ( faster fluctuation/smaller positional deviations ) are not relevant in determining biologically relevant large scale motion of the protein [75] . The reduced set of principal components is then ( 4 ) The fluctuation profile along each mode is simply the Δr of each residue in that mode . By plotting these against each other , we confirm convergence when the Pearson correlation coefficient , Pij , of the trajectory for sampling window i ( Xi ) and sampling window j ( Xj ) is >0 . 8 ( 5 ) σi and σj are the standard deviations of their trajectories . If the run has not converged it is continued until convergence is confirmed over a 3 ns window ( Fig . S3 ) . Using the Saguaro high performance computer at Arizona State University , a 250 residue protein with 40 temperature replicas ( 1 logical core per replica ) finishes just under 300 ps/day . The most native like structures are assumed to be those that dominate the lowest temperature replica , while those in higher temperature replicas are dismissed . After confirming convergence , in order to obtain the dynamics difference between the most collective motions ( i . e . slowest frequency fluctuation profiles ) of these three ancestral structures we apply the Singular Value Decomposition ( SVD ) technique to the matrix of dynamics profiles , G ( i . e . the dynamics profile of each protein will be the column in the matrix , and each super-element , ik corresponds the X , Y , and Z fluctuations of the kth residue in the sequence of protein i ) . ( 6 ) G matrix includes most collective modes of ( i . e . global motion ) individual proteins that we obtained separately from REMD trajectories . With construction of the G matrix our goal is to cluster the proteins with similar global motion . Since global dynamics ( i . e . most spatially extensive collective mode ) is most related to the function , proteins with similar global dynamics should cluster together and execute similar function . In order to do clustering we perform an SVD on G matrix ( 7 ) The first through nth values in each column of W can be plotted against each other to visualize the dynamic space occupied by each protein .
Proteins are remarkable machines of the living systems that show diverse biochemical functions . Biochemical diversity has grown over time via molecular evolution . In order to understand how diversity arose , it is fundamental to understand how the earliest proteins evolved and served as templates for the present diverse proteome . The one sequence - one structure - one function paradigm is being extended to a new view: an ensemble of different conformations in equilibrium can evolve new function and the analysis of inherent structural dynamics is crucial to give a more complete understanding of protein evolution . Therefore , we aim to bring structural dynamics into protein evolution through our zipping and assembly method with FRODA . ( ZAMF ) . We apply ZAMF to simultaneously obtain structures and structural dynamics of three ancestral sequences of steroid receptor proteins . By comparative dynamics analysis among the three ancestral steroid hormone receptors: ( i ) we show that changes in the structural dynamics indicates functional divergence and ( ii ) we identify all functionally critical and most of the permissive mutations necessary to evolve new function . Overall , all these findings suggest that conformational dynamics may play an important role where new functions evolve through novel molecular interactions .
You are an expert at summarizing long articles. Proceed to summarize the following text: Typical bacterial strain differentiation methods are often challenged by high genetic similarity between strains . To address this problem , we introduce a novel in silico peptide fingerprinting method based on conventional wet-lab protocols that enables the identification of potential strain-specific peptides . These can be further investigated using in vitro approaches , laying a foundation for the development of biomarker detection and application-specific methods . This novel method aims at reducing large amounts of comparative peptide data to binary matrices while maintaining a high phylogenetic resolution . The underlying case study concerns the Bacillus cereus group , namely the differentiation of Bacillus thuringiensis , Bacillus anthracis and Bacillus cereus strains . Results show that trees based on cytoplasmic and extracellular peptidomes are only marginally in conflict with those based on whole proteomes , as inferred by the established Genome-BLAST Distance Phylogeny ( GBDP ) method . Hence , these results indicate that the two approaches can most likely be used complementarily even in other organismal groups . The obtained results confirm previous reports about the misclassification of many strains within the B . cereus group . Moreover , our method was able to separate the B . anthracis strains with high resolution , similarly to the GBDP results as benchmarked via Bayesian inference and both Maximum Likelihood and Maximum Parsimony . In addition to the presented phylogenomic applications , whole-peptide fingerprinting might also become a valuable complementary technique to digital DNA-DNA hybridization , notably for bacterial classification at the species and subspecies level in the future . The most common techniques for bacterial classification and identification are conventional DNA:DNA hybridization ( DDH ) [1] , comparison of 16S or 23S rRNA gene sequences or 16S–23S rRNA spacer regions [2] , multi-locus sequence typing ( MLST ) [3] and rep-PCR fingerprinting [4] , among others [5] . For decades , the technique of choice to identify and classify species has been DDH with a similarity value of 70% DDH as the species delimitation threshold [6] . In microbial taxonomy , DDH is mandatory whenever the 16S rRNA gene sequence similarity between two strains is above 97% for confirming that these do not belong to the same species . This threshold has recently been increased by proposing values of between 98 . 2 and 99 . 0% , depending on the phylum [7] . Conventional DDH has limitations , for instance , that it is only available in a few specialized molecular laboratories world-wide and it is particularly biased to experimental errors [8] . Due to this and because of the availability of whole-genome sequencing , this facilitated the development of bioinformatics alternatives to conventional DDH [9] . Here , the Genome-to-Genome Distance Calculator web service ( GGDC; freely available at http://ggdc . dsmz . de/ ) currently provides the highest in silico correlation to conventional DDH–without sharing the aforementioned drawbacks–which is a crucial requirement for any such in silico method to maintain consistency in prokaryotic species delineation [10] . The GGDC server incorporates the latest version [[10] of the Genome-BLAST Distance Phylogeny method ( GBDP ) —a highly optimized tool for the calculation of intergenomic distances—and estimates digital DNA-DNA hybridization values ( dDDH values ) from these distances under recommended settings [10] . Among other useful data , the dDDH values are reported along with confidence intervals , which are important for assessing the statistical uncertainty inherent to all model-based approaches [10] . In this way , GGDC can be reliably used for both species and subspecies delimitation [11] . The GBDP method incorporates several optimizations to avoid potentially biased results caused by elements such as paralogous genes or low-complexity regions . It is also robust against the use of incomplete genome sequences [10] and can be applied to both nucleotide and amino acid data . Finally , it includes a pseudo-bootstrapping procedure [10] for the calculation of replicate intergenomic distances , which can be further used in phylogenetic applications to assess branch support values as shown earlier [11–13] . Matrix Assisted Laser Desorption/Ionization Time Of Flight Mass Spectrometry ( MALDI-TOF MS ) has been applied as an alternative approach to identify and discriminate between species and strains [14–16] . This alternative is typically adopted when there is limited genetic variability within or across the species under study , and assumes the presence and detection of species/strain specific peptides through comparison of their mass-to-charge ratio . In this way this method supports species/strain differentiation . However , many of these differential peptides may not be detected due to their low abundance or other physicochemical properties , i . e . , those methods are limited in such a way that it only explores a subset of the total peptidic variability . To overcome this limitation , we have designed a novel in silico peptide fingerprinting methodology suitable for phylogeny inference . This methodology follows the same general principle of existing mass spectrometry approaches but it uses whole genome data and in silico protein digestion , i . e . , it does not involve any conventional experimentation . Furthermore , the analysis stands on the shoulders of well-established software tools , namely PSortB [17] , mzJava [18] , SPECLUST [19] and MrBayes [20] . The aim is to be able to generate a valid and manageable list of peptides that are potentially specific to each strain . This list could then be further investigated using in vitro approaches , such as LC-MS/MS , towards the identification of biomarkers , strain specific peptides and the development of application-specific detection methods . Our case study covers a subset of strains belonging to the Bacillus cereus group [21] . More precisely , the case study covers B . thuringiensis , B . anthracis and B . cereus ( senso stricto ) strains , which are known to share high genetic similarity [22] . Such strains are conventionally classified according to other features , such as their pathogenic potential or the presence of plasmids [23] . From a taxonomic point of view , separation of the three Bacillus species is still a subject of controversy among scientists . However , a recent large-scale whole-genome sequence-based study using GBDP elucidated the taxonomy within the B . cereus group and showed that B . thuringiensis , B . anthracis and B . cereus ( senso stricto ) species are indeed belonging to individual phylogenetic groups [12] . Other strains originally attributed to one of these three species , were either misclassified or belong to other novel species within the cluster . The results of the GBDP phylogenomic analysis serve as a good baseline , representative of what can currently be achieved with a state-of-the-art phylogenomic analysis as exemplified for the B . cereus group . Currently , a method to infer bacterial taxonomy in silico through the use of peptidomes is missing . The development of such a method is appealing as it would complement GBDP analysis . Additionally , establishing the comparison and identification of unique peptides on an exemplary microbial data set would aid in the separation of closely related strains . Moreover , in silico peptidome fingerprinting is able to reduce whole proteome data into smaller binary matrices , which is of advantage when handling larger bacterial datasets . The amount of data may be decreased using different peptidome subsets without losing phylogenetic signal . Main results are discussed in this manuscript . All the sequence data used in this study were retrieved from the BioProject collection of the National Center for Biotechnology Information ( NCBI ) , using their public FTP site ( ftp://ftp . ncbi . nih . gov/genomes/bacteria/ ) [24] . Our study focused on the complete genomes of Bacillus anthracis , Bacillus cereus and Bacillus thuringiensis whose BioProject accession numbers are listed in Table 1 . Genetic data was obtained from * . fna files , whereas proteomes for in silico digestion were obtained from * . faa archives . Bacillus subtilis subsp . natto BEST195 was selected as an outgroup . For efficiency and to increase the flexibility in the analyses , protein data were stored in an in-house database . Subcellular localization defines the putative localization of the protein in the cell . This information is relevant because , for instance , extracellular proteins are used by the bacterium to communicate with its environment and thereby could help in bacterial differentiation . The subcellular localizations of the proteins were predicted using the standalone version of the PSortB v3 . 0 tool , following the developer guidelines [17] . The subsets corresponding to chromosomal proteins and plasmids were stored in the in-house database . Bacterial proteomes were obtained for all the Bacillus strains used in this work . The open-source Java library mzJava from ExPASy ( http://mzjava . expasy . org ) supported protein digestion [18] . For the purposes of the present analysis , three proteases representing the major intestinal endoproteases were used: trypsin , chymotrypsin and pepsin ( low specificity model , pH>2 ) . Resulting peptides , denominated peptidomes , were also stored in the in-house database . Five different datasets were considered in our study: i ) whole proteomes using GBDP for calculating intergenomic distances ( GBDP ) , ii ) peptides with a length > 28 amino acids obtained from cytoplasmic proteins ( Cyto28-more ) , iii ) peptides with a length comprised between 51 and 60 amino acids obtained from cytoplasmic proteins included in the pI range 4 . 5–5 . 5 ( Cyto_PI_51–60 ) , iv ) peptides with a length higher than 60 amino acids obtained from cytoplasmic proteins included in the pI range 4 . 5–5 . 5 ( Cyto_PI_60-more ) , and v ) peptides obtained from extracellular proteins ( Extracellular ) . For the four last subsets , three different methodologies were used to infer phylogenies , Bayesian ( MB ) , Maximum Likelihood ( ML ) and Maximum Parsimony ( MP ) . The consensus peak set among all the strains was obtained in two steps . First , the list of the total peptides for each strain was subdivided based on peptide length for indexing purposes . Then , the molecular weight and isoelectric point of the selected peptides were calculated using an in-house customised tool adapted from the SIB Bioinformatics Resource Portal ( http://web . expasy . org/compute_pi/ ) . In the case of peptides obtained from extracellular proteomes , all peptides were kept for analysis . SPECLUST , a public web-based tool , was used to identify representative and reproducible peak masses that are present in a collection of spectral profiles [18] . This tool calculates the mass difference between two peaks taken from different peak lists and determines whether or not the two peaks are identical , taking into account some measurement uncertainty ( σ ) . In the present study , the measurement uncertainty was set empirically to 3 . 0 Da . In addition , the pairwise cut-off was set to 0 . 6 , i . e . , a peak was considered shared between two spectra if it was matched in the alignment of the spectra with a peak match score greater than 0 . 6 ( corresponding to a 0 . 5 Da mass difference ) . The consensus spectra matrix was translated to a binary matrix ( 0s and 1s , representing absence or presence of a given peptide mass respectively ) in NEXUS file format [25] . MrBayes , the model-based phylogenetic inference tool using Bayesian statistics , was utilised to generate a consensus tree [20] . The consensus binary file obtained from the previously generated SPECLUST consensus file was used as input . The phylogeny was inferred through the restriction data type implemented in MrBayes ( with state 0 or 1 representing the absence or presence of a consensus peptide throughout the strain peptidomes ) . For the purpose of our study , we assumed that the frequencies of these two possible states had a Dirichlet ( 1 . 00 , 1 . 00 ) prior parameter . Bayesian analysis was performed in two independent runs using four Markov chains and 1 , 000 , 000 generations . When necessary , the number of generations was incremented for chain convergence diagnosis . The potential scale-reduction factor , printed at the end of the analysis , was used as convergence diagnosis . A majority-rule consensus tree ( 50% ) was obtained after discarding the initial 25% of the trees ( burnin = 250 ) , where the log-likelihood values of the analysis ( log probability of the data given the parameter values ) are frequently not yet stabilized . Using this command , MrBayes plots the number of generations ( each corresponding to a phylogenetic tree ) versus its log probability . Usually , the first sampled trees show trends towards increasing or decreasing log-likelihood values , which results in inadequate sampling from the posterior probability distribution Maximum likelihood ( ML ) and maximum parsimony ( MP ) phylogenies were inferred using the DSMZ phylogenomics pipeline [11] . A multiple sequence alignment was created with MUSCLE [26] , and ML and MP trees were inferred from it with RAxML [27] and TNT [28] , respectively . For ML , rapid bootstrapping in conjunction with the autoMRE bootstopping criterion [29] and subsequent search for the best tree was used; for MP , 1000 bootstrapping replicates were used in conjunction with tree-bisection-and-reconnection branch swapping and ten random sequence addition replicates . A whole-genome phylogeny ( based on the proteome data ) was inferred using the latest version of the Genome-BLAST Distance Phylogeny ( GBDP ) method [11 , 30] . Here , pairwise proteome comparisons ( including pseudo-bootstrap replicates ) were done under the greedy-with-trimming algorithm and further recommended settings [13] . The tree was inferred using FastME v2 . 07 with TBR post-processing [31] . The species and subspecies clustering was conducted on the nucleotide data ( i ) with the help of the Genome-to-Genome Distance Calculator ( GGDC ) , ( ii ) established ( sub- ) species distance cut-offs [11 , 12] , and ( iii ) the OPTSIL clustering tool [32] , in analogy to a recent study [12] . The Interactive Tree Of Life ( iTOL ) web-based tool was utilised to visualize the phylogenetic trees [33] . Using the tree files generated previously , the annotation was performed , highlighting the BCG ( Bacillus Cereus Group ) notation as reported before by Li et al . [12] . Posterior probabilities or branch support values were included when equal or above 60% . The inferred trees were compared amongst themselves and with the pseudo-bootstrapped whole-proteome GBDP phylogeny [13] . The topological comparison was based on pairwise weighted Robinson-Foulds distances , which were calculated using the RaxML tool [27 , 34] . Visualisation was supported by the packages ggplot [35] and ggdendro [36] for the statistical language R [37] . As illustrated in Fig 3 , the GBDP proteome tree recovered all species with high support and showed insignificant subspecies conflicts . Most notably , this tree has an average branch support of 84 . 7% ( Table 2 ) and confirms previous results of a nucleotide-based GBDP analysis [12] . Moreover , the OPTSIL clustering method [32] yielded eight species clusters as well as ten subspecies clusters ( excluding the outgroup of B . subtilis ) . For instance , the cluster BCG01 contained some “B . cereus” and “B . thuringiensis” strains , which in fact belong to B . anthracis based on the dDDH estimates ( see Supplementary S3 File ) . In turn , cluster BCG03 ( B . cereus ) included two “B . thuringiensis” strains: “B . thuringiensis BMB171” and “B . thuringiensis serovar kurstaki HD73" . This is in accordance with a recent study on the taxonomic situation of the B . cereus group [12] . In summary , three major groups were identified: ( i ) BCG01 containing traditional and anomalously assigned strains of B . anthracis , ( ii ) a group encompassing the three related BCG03 ( B . cereus ) , BCG04 ( B . thuringiensis ) and BCG17 and , ( iii ) a group formed by BCG10 , BGC12 and BCG20 comprising three potential novel species [12] . Finally , “B . thuringiensis MC28” was classified into BCG09 , which has been proposed as a novel species [12] . The phylogenies of the peptidome datasets resulting from all possible combinations of the three human proteases were evaluated based on MB , ML and MP criteria ( see Supplementary S1 File ) . We also investigated proteins with different subcellular location as a possible way of reducing the amount of proteomic data input . In the case of extracellular proteins , all the resulting peptides were used in the analysis , but in the case of cytoplasmic peptidomes , the high number of peptides was further reduced by means of amino acid length and pI value filtering . Specifically , we considered three length bins , i . e . 28-more , 51–60 and 60-more amino acids , and those proteins with a pI between 4 . 5 and 5 . 5 , which corresponds to the pI exhibited by most of the housekeeping and metabolic enzymes , as deduced from as deduced from 2 dimensional electrophoresis experiments [38] . In addition , genes coding for many of these proteins , such the β-subunit of RNA polymerase ( rpoB ) , the β-subunit of ATP synthase F0F1 ( atpD ) , or the chaperonin GroEL ( groEL ) are frequently used in multilocus sequence typing approaches [39] . Interestingly , this pI range do not correspond with the normal cytoplasmatic pH in mesophilic organisms such as Escherichia coli or Bacillus subtilis , which is slightly alkaline ( 7 . 0–7 . 8 ) over an external pH ranges of 5 . 0–9 . 0 [40–44] was determined by means of a flow cytometry with the fluorescent probe 5 ( and 6- ) -carboxyfluorescein ester . As an example , we can say that the dataset including peptides with more than 60 amino acids comprised approximately 1 , 000 peptides per strain ( Suppl . S2 File ) , which contrasts with the 320 , 000–411 , 000 peptides obtained after proteome digestion for the different strains concerned in this study , and results in an obvious reduction of data input . So , the hereafter presented results relate to the extracellular peptide dataset ( Fig 4 ) , the cytoplasmic dataset containing peptides with 28 or more amino acids ( Fig 5 ) , and the cytoplasmic datasets containing peptides with 51–60 amino acids or more than 60 amino acids and pI values within the range 4 . 5–5 . 5 ( Figs 6 and 7 , respectively ) . Interestingly , other filtering criteria , such as charge to mass amino acid ratio , may be implemented as mean as reducing the proteomic input . The four peptide subsets were loaded in MrBayes and used to infer phylogenies . At the end of the Bayesian analysis , the average standard deviation of split frequencies after 1e06 generations suggested a good convergence of the analyses , as in all cases it was lower than 0 . 01 ( Cyto28-more: 0 . 004; Cyto_PI_51–60: 0 . 007; Cyto_PI_60-more: 0 . 004; Extracellular: 0 . 006 ) . Convergence of the analyses was confirmed by calculating the potential scale reduction factor ( PSRF ) of the total tree length ( TL ) and the stationary phase frequencies ( pi ) of the two possible states of our binary model ( 0 or 1 ) . In all cases the PSRF values converged to 1 . 000–1 . 001 at the end of the analysis , indicating a good phylogenetic tree sampling from the posterior distribution . A summary of the results of the phylogenetic inference is found in Table 2 . The ML analyses yielded and subsequently used “Uncorrected+GAMMA” as best model during the inference . Since the ML , MP and MB trees were very similar within each peptidome dataset in terms of weighted topological distance ( see below ) , only the MB-based trees are shown while discussing the different datasets . The remaining ML and MP trees are shown in Suppl . S1 File . Pairwise weighted Robinson-Foulds distances supported the assessment of topological differences among the five trees at the light of the four methods of analysis ( Fig 2 ) . More specifically , the differences observed between the trees inferred from whole proteomes ( GBDP analysis ) and Cyto28-more , cyto_PI_60-more cyto_PI_51–60 and Extracellular subsets ( applying the MB , ML and MP criteria ) . Significance of conflict between two trees was assumed when a bipartition implied by one tree was found incompatible with a bipartition implied by the other tree , with both receiving ≥95% support . Similarly , disagreement with the monophyly of a species or subspecies was only considered if the conflicting branches had ≥95% support . As an initial observation we can say that the MB [Cyto_PI_60-more] and [Cyto28-more] trees showed no significant conflict with the GBDP tree . However , there are some interesting discrepancies between several trees . For example , in contrast to ML and MP trees , the MB [Cyto28-more] tree ( Fig 5 ) showed significant conflict in terms of subspecies assignments within BCG01 ( B . anthracis ) cluster . Another example is the conflict between the MB [Extracellular] tree and some of the MB cytoplasmic trees regarding the placement of “B . thuringiensis serovar kurstaki HD73” . Specifically , in the [Extracellular] tree ( Fig 4 ) the “B . thuringiensis serovar kurstaki HD73” is placed next to the BCG04 ( B . thuringiensis ) cluster with high support while in the MB [Cyto_PI_60-more] ( Fig 6 ) and [Cyto28-more] ( Fig 5 ) trees it is part of the BCG03 ( B . cereus ) group . Likewise , the MB [Cyto_PI_51–60] tree ( Fig 6 ) significantly deviated from the GBDP proteome tree by placing B . anthracis H9401 as sister group of all other highly virulent B . anthracis strains instead of as sister group of B . anthracis CDC 684; and , by forming a well-supported group ( 96% ) comprising “B . thuringiensis MC28” , the cluster BCG04 ( B . thuringiensis ) and the cluster BCG17 ( B . thuringiensis ) . Noteworthy , these arrangements received no support in the ML and MP analyses of the [Cyto_PI_51–60] dataset . See Supplementary S1 File for details . The comparison of the peptidome-based phylogenetic trees allowed us to gain a better understanding about the information provided by the different sets of peptides . The four peptide subsets produced similar results regarding the identification of quite unrelated strains ( e . g . , B . subtilis subsp . natto BEST195 ) , and established a species grouping as close as the one suggested by Liu et al . using 224 genomes of strains belonging to the B . cereus group [12] . Classically , B . thuringiensis strains have been considered an insect pathogen , affecting mainly members of the orders Lepidoptera , Diptera and Coleoptera [23] . Spores from these strains include large crystal protein inclusions , which are cleaved by the insect mid-gut proteases producing the active toxin forms . The action of this toxin leads to the complete destruction of the intestinal epithelium . In turn , the BCG03 cluster corresponds to B . cereus , which is an opportunistic human pathogen and food-borne bacterium that causes two forms of poisoning , one characterised by diarrhea and abdominal pain , and the other involving nausea and vomiting [45 , 46] . Some “B . thuringiensis” strains also clustered in BCG03 , because they share certain genetic similarity with B . cereus ATCC 14579T , namely genetic regions such as a putative polysaccharide capsule cluster [47] . B . anthracis ( BCG01 cluster ) is the etiological agent of anthrax , a fatal disease for herbivores and mammals that is best known for its use as biological weapon [48] . Strains from this species can be classified according to different phenotypical tests . For instance , these strains are non-motile , penicillin-sensitive , and produce an extracellular capsule of poly-γ-D-glutamic acid [49] . Toxins responsible for anthrax symptoms and other virulence factors necessary for complete virulence are codified into two large plasmids , denominated pXO1 and pXO2 [50] . Two strains of “B . thuringiensis” also clustered within the BCG01: “B . thuringiensis Al Hakam” , and “B . thuringiensis serovar konkurian” . Indeed both strains have been shown to be more related to the B . anthracis cluster . The genome of these strains contain no homologues of the known B . thuringiensis insecticidal genes cry , cyt , or vip and , even if these ever existed , the plasmid ( s ) encoding for these genes may have been lost during in vitro culture [50 , 51] . Therefore , classification of these two strains as B . thuringiensis strains may not be correct , as previously reported in [12] . Other cluster identified in our analysis was BCG17 , a putative novel species . This contained “B . cereus G9842” together with other two “B . thuringiensis” strains . The G9842 strain was isolated from stool samples of an emetic outbreak that involved three individuals in Nebraska ( 1996 ) and the genome was sequenced by the J . Craig Venter Institute ( http://www . ncbi . nlm . nih . gov/bioproject/17733 ) . The isolate was characterised by MLST typing using the MLSTDB scheme as sequence type 56 ( http://pubmlst . org/bcereus/ ) . Interestingly , the sequence type 56 was quite unrelated to the major clade of pathogenic B . cereus isolates and was suggested as representative for a novel pathogenicity group within the B . cereus group [52] . Peptidome fingerprinting confirms the new affiliation to B . thuringiensis . The peptidome of strain G9842 , shared a high homology with the other “B . thuringiensis strains” , so it is plausible that these two isolates lost the plasmids containing the insecticide genes and acquired certain virulence factors , which allow them to act as pathogens in the human host . Finally , phylogenetic techniques consistently grouped “B . thuringiensis serovar finitimus” individually , and it has been proposed as representative for the novel species BCG20 [12] . This strain contains several cry genes encoding for crystal proteins and located in two plasmids [53] . The chromosome of this strain has been shown to be closer phylogenetically to B . anthracis Ames than to B . cereus ATCC 14579T [12 , 54] . Given the close distance of “B . thuringiensis serovar finitimus” to the other BCG groups containing “B . cereus” strains , such as BCG10 and BCG12 , we speculate that this strain may be a B . cereus strain that acquired the plasmids from a B . thuringiensis donor . Another important aspect of the evaluation of our peptidome similarity method is the computational complexity induced by each processing step and the resulting processing time eventually , although available computational power will be of course decisive . The running time of future in silico experiments can thus be extrapolated , especially that of significantly larger datasets . We computed the processing time of each of the main steps for the whole Bacillus dataset ( i . e . 32 genomes ) and for four subsets , representing a large dataset ( i . e . 24 genomes ) , a medium-large dataset ( i . e . 16 genomes ) , a medium-small dataset ( i . e . 8 genomes ) and a small dataset ( i . e . 4 genomes , which is the smallest possible dataset that one can use for phylogenetic inference ) . In particular , we randomly sampled without replacement four sets of 24 , 16 , 8 and 4 genomes , and calculated the average running time . Here , we present the average times , but details on the different runs can be found in S4 File . Table 3 summarises the running times taken by the steps of protein localization , which is performed by PsortB , and protein digestion , which is performed by ExPASy MzJava . Protein localization is the most time consuming task and , in particular , the processing of larger datasets may take several days . Although this may be considered somewhat time consuming , this step enables further filtering of the peptide dataset that , in turn , may reduce considerably the data matrices to be computed and speed up the subsequent steps of analysis . The running times of steps leading to the generation of the NEXUS files are negligible compared to those of previous steps ( Table 4 ) . For most of the sample sets both steps took less than 15 minutes to execute . A large running time ( > 2 hours ) was observed for the SPECLUST run over the whole dataset of cytoplasmic peptides with 28 or more amino acids , which comprises a total of 121 , 632 peptides . One of the potential applications of our pipeline is to accept , as input , experimental peptide mass profiles . If traced back , our application allows detection of differential peptide profiles , providing a robust tool to discriminate not only strain-specific peptides , but true intraspecies differences among a set of biological replicates or even microorganism-phenotype variations such as those occurring between biofilm and planktonic populations . In this regard , the negative effect of certain peptide families on bacteria through different mechanisms is well known [55 , 56] . In this regard , our pipeline will just provide a candidate peptide list , but experimental approaches such as MS/MS experiments will never detect peptides that are inhibiting own bacterial growth . Rather , such experimental approaches will validate the presence of those certain strain-specific peptides , either free or most probably encoded in a “carrier protein” . Generation of a potential strain-specific peptide list together with its experimental identification , may facilitate development of different approaches focused on the identification of given strain , such as a dairy starter or a probiotic that has to be traced through the human gut during clinical intervention studies . This can be accomplished , for instance , with the use of high-resolution mass spectrometers or antibody-based protocols targeting these specific peptides . Whereas our bioinformatic approach will reliably produce the same results , conventional methods might yield different results even if applied on the same organisms , due for instance to phenotype-variations or the use of transient input data . In addition , the big advantage of the in silico method is accuracy , reproducibility and speed , whereas the disadvantage is that it might not get the experimental peptidome as we simply consider all proteins encoded in a genome and not only those that are actively produced by the organism while being measured . Overall , results show that our phylogenetic method based on peptidome similarity , as opposed to genome-sequence homology , is complementary to the proteome-based GBDP analysis . Most notably , our peptidome-based phylogeny analysis supported already reported taxonomic discrepancies within the B . cereus group . Our peptidome-based method has the advantage of reducing larger amounts of proteomic data to small matrices ( by a factor of 320 ) without losing too much phylogenetic signal . Our pipeline can be also applied to other peptide datasets originated from viruses , eukaryotic species or even metaproteomes with the inclusion of few modifications regarding the prediction of the protein subcellular location . This could be of interest for developing more efficient applications aimed at managing very large bacterial datasets , such as those generated in epidemiologic studies .
Molecular based differentiation of bacterial species is important in phylogenetic studies , diagnostics and epidemiological surveillance , particularly where unusual phenotype makes the classical phenotypic identification of bacteria difficult . Typical bacterial differentiation methods are often challenged by a high genetic similarity among strains . For decades , the technique of choice to classify and identify bacteria was DNA-DNA hybridization ( DDH ) . The boosting of whole-genome sequencing technology facilitated the development of bioinformatics alternatives that could assist a much wider number of laboratories and are less biased to experimental errors . Currently , the Genome-to-Genome Distance Calculator web service , implementing the Genome-BLAST Distance Phylogeny ( GBDP ) method , provides the highest correlation to conventional DDH . Our methodology shows that whole peptide fingerprinting may complement the outputs of GBDP , i . e . experimental mass spectra may be used to cluster the bacteria , and more specifically it has been found useful for bacterial classification at the species and subspecies level . In addition , we present here how peptidome subsets obtained from in silico digestion of the peptidomes , is an efficient way to maintain the phylogenetic signal whilst reducing the total amount of data , making this methodology suitable for handling large data sets as in the case of epidemiologic studies .
You are an expert at summarizing long articles. Proceed to summarize the following text: Influenza A viruses ( IAVs ) inhibit host gene expression by a process known as host shutoff . Host shutoff limits host innate immune responses and may also redirect the translation apparatus to the production of viral proteins . Multiple IAV proteins regulate host shutoff , including PA-X , a ribonuclease that remains incompletely characterized . We report that PA-X selectively targets host RNA polymerase II ( Pol II ) transcribed mRNAs , while sparing products of Pol I and Pol III . Interestingly , we show that PA-X can also target Pol II-transcribed RNAs in the nucleus , including non-coding RNAs that are not destined to be translated , and reporter transcripts with RNA hairpin structures that block ribosome loading . Transcript degradation likely occurs in the nucleus , as PA-X is enriched in the nucleus and its nuclear localization correlates with reduction in target RNA levels . Complete degradation of host mRNAs following PA-X-mediated endonucleolytic cleavage is dependent on the host 5’->3’-exonuclease Xrn1 . IAV mRNAs are structurally similar to host mRNAs , but are synthesized and modified at the 3’ end by the action of the viral RNA-dependent RNA polymerase complex . Infection of cells with wild-type IAV or a recombinant PA-X-deficient virus revealed that IAV mRNAs resist PA-X-mediated degradation during infection . At the same time , loss of PA-X resulted in changes in the synthesis of select viral mRNAs and a decrease in viral protein accumulation . Collectively , these results significantly advance our understanding of IAV host shutoff , and suggest that the PA-X causes selective degradation of host mRNAs by discriminating some aspect of Pol II-dependent RNA biogenesis in the nucleus . Inhibition of host gene expression , termed “host shutoff” , is thought to enable viruses to simultaneously inhibit innate immune responses and provide preferential access for viral mRNAs to the cellular translation machinery . Influenza A virus ( IAV ) has long been known to carry out host shutoff , and multiple shutoff mechanisms have been reported for this virus , including translation blockade [1] , inhibition of polyadenylation and nuclear export of host pre-mRNAs by the IAV NS1 protein [2] , and degradation of the host RNA polymerase II complex [3] . Because some of these mechanisms are specific to certain IAV strains [4] , it has long been suspected that more universal IAV host shutoff mechanisms exist . The recent discovery of the highly conserved RNA endonuclease PA-X [5] has prompted re-examination of established models of IAV host shutoff . Viruses from several divergent families use virus-encoded RNA endonucleases to broadly degrade host mRNAs and reduce host protein production [5–9] . Although host shutoff ribonucleases ( RNases ) generally have broad specificity in vitro , several studies have shown unexpected selectivity for different types of host transcripts [10–15] . PA-X limits accumulation of host mRNAs and proteins in infected cells and suppresses host responses to infection [5 , 16–19] , but the mechanistic determinants of selectivity , cleavage and degradation are not yet known . IAV is a negative strand RNA virus with a genome consisting of eight segments . PA-X protein is encoded on IAV genome segment 3 , which also produces polymerase acidic protein ( PA ) , one of the three subunits of the viral RNA-dependent RNA polymerase ( RdRp ) . It is generated by ribosomal pausing on a rare CGU codon that results in a +1 frameshift and read-through of an alternative open reading frame ( ORF ) [5 , 20] . PA-X comprises the amino-terminal 191 amino acids of the polymerase subunit PA fused to a carboxy-terminal domain ( termed “X-ORF” ) of either 41 or 61 amino acids that result from the frameshift [5 , 21] . Consequently , PA-X lacks the carboxy-terminal domain of PA responsible for its recruitment into the RdRp complex . The shared PA/PA-X amino-terminal domain includes an RNA endonuclease domain that is required for PA-X shutoff activity [5] . Thus , PA-X has a function analogous to host-shutoff proteins from other viruses that trigger RNA degradation . These factors include SARS coronavirus nsp1 [9 , 22] , herpes simplex virus 1 ( HSV-1 ) vhs [6 , 23] and Kaposi’s sarcoma-associated herpesvirus ( KSHV ) SOX [8] . In general , these proteins are unrelated at a molecular level , although both PA-X and the SOX family of proteins belong to the PD- ( D/E ) XK nuclease superfamily [24–28] . All known host-shutoff RNases use a similar mechanism of action , causing endonucleolytic cleavage of the RNA and relying on host enzymes to complete RNA degradation [14] . Moreover , they are all selective for translatable RNA polymerase II ( Pol II ) transcripts , but spare non-coding RNAs ( ncRNAs ) synthesized by Pol I and Pol III [14] . This selectivity has been linked to the process of translation or loading into translation initiation complexes [14 , 22 , 29–31] . RdRp-transcribed IAV mRNAs share essential features with host mRNAs like a 5’ 7-methyl guanosine ( m7G ) cap and a 3’ poly-adenylate ( poly ( A ) ) tail . 5’ m7G caps are acquired by “cap-snatching” , whereby the PA subunit cleaves nascent host Pol II-transcribed RNAs at a position 10–14 nucleotides downstream from the 5’ cap [32] . The RdRp complex uses these fragments to prime viral mRNA synthesis . The poly ( A ) tail is generated by RdRp “stuttering” , allowing reiterative copying of a short poly-uridine sequence at the 5’ end of the template genome segment [33] . The acquisition of m7G caps and poly ( A ) tails allows efficient loading of ribosomes and translation of IAV mRNAs; however , the similarity between host and viral mRNAs raises the question of their susceptibility to PA-X-mediated degradation . Here , we demonstrate that PA-X selectively targets host RNA transcribed by the RNA Pol II complex for cleavage , and degradation is completed by the host 5’->3’ exonuclease Xrn1 . By contrast , host transcripts generated by other Pol complexes resist PA-X-mediated degradation . Interestingly , selective targeting is not linked to translation , as we observe that PA-X also degrades non-coding Pol II transcripts , and may instead be linked to distinct features of Pol II transcript biogenesis in the nucleus . Consistent with these observations , we show that PA-X is recruited to the nucleus via X-ORF interactions that involve highly conserved basic residues previously shown to be important for the shutoff function [34] . Accordingly , we find that IAV mRNAs generated by viral polymerase complexes are intrinsically resistant to PA-X-mediated degradation . In addition , we demonstrate that PA-X is required for efficient translation of viral mRNAs , as viral protein accumulation is significantly diminished in cells infected with PA-X deficient viruses . Taken together these findings suggest that IAV PA-X hijacks cellular RNA biogenesis processes to direct the degradation of host RNAs , and that the distinct biogenesis mechanism for viral mRNAs provides a convenient way to discriminate host and viral products . Thus , although PA-X shares some mechanistic properties with other host shutoff RNases , it also displays distinctive features that advance our understanding of host shutoff . Diverse RNA species are generated by three host DNA-dependent RNA polymerases; Pol II transcribes mRNAs and some non-coding RNAs ( ncRNAs ) , whereas Pol I transcribes the rRNA precursor 47S and Pol III transcribes a variety of short ncRNAs including the 5S rRNA . To determine whether PA-X could trigger the degradation of distinct host RNA species , we transfected HEK 293T cells with plasmid reporters encoding an RFP gene driven by a Pol II promoter and a GFP gene under the control of a Pol I- , Pol II- , or Pol III-driven promoter . We observed that ectopic expression of PA-X derived from the A/PuertoRico/8/34 H1N1 ( PR8 ) virus together with these reporters consistently inhibited the accumulation of Pol II-driven RFP and GFP transcripts , whereas Pol I and Pol III-driven GFP transcripts accumulated to high levels ( Fig 1A ) . These data suggest that PA-X activity is specific for Pol II transcripts . To determine whether PA-X could similarly selectively target endogenous Pol II-driven transcripts , we constructed HEK 293T- and A549-based cell lines that stably express PA-X in a doxycycline-inducible manner ( iPA-X cells ) . In comparison to an inducible RFP control , PA-X expression in the HEK 293T cell lines dramatically reduced endogenous mRNA levels for actin , EEF1A , GAPDH , GUSB , HIST1H3C , tubulin and RPS6 , but had a much smaller effect on RPS18 and POLR2A mRNAs ( Fig 1B ) . By contrast , consistent with our results with reporter constructs ( Fig 1A ) , PA-X did not decrease the levels of the Pol I transcript 47S or several Pol III transcripts ( 5S , 7SK and 7SL ) . Both the specificity for Pol II transcripts and the variable level of down-regulation of different mRNAs were also observed in the A549 lung carcinoma cell line ( Fig 1C ) . In addition , expression of the PA-X RNase domain catalytic mutant D108A [5 , 35] in A549 cells did not affect RNA levels ( Fig 1C ) . This result indicates that the mRNA down-regulation is likely due to increased degradation , as it requires an intact RNase domain . Multiple independently isolated HEK 293T and A549 cell lines generated similar results ( S1A and S1B Fig ) . Thus , PA-X expressed in isolation decreases the levels of a broad range of host mRNAs with varying efficiency , but Pol I- and Pol III-transcribed RNAs resist degradation , pointing at a mechanism of action similar to that of previously described host-shutoff RNases from other viruses [14] . We previously found that herpesvirus and coronavirus host shutoff endonucleases employ analogous mechanisms to trigger widespread RNA degradation , whereby endonuclease cleavage of target RNA is followed by processive 5’->3’ degradation by the host exonuclease Xrn1 [14] . To determine whether PA-X requires host 5’->3’ exonucleases to complete RNA degradation , we tested plasmid reporters that contain a pseudo-knot forming sequence from West Nile virus ( SLII [36] ) in either the GFP coding region or the 3’ UTR ( Fig 2A ) ; the presence of the SLII element leads to protection of the downstream RNA from digestion by 5’->3’ exonucleases [14 , 29] . Northern blotting using a probe against the 3’ untranslated region ( UTR ) of GFP showed that ectopic expression of PA-X reduced the levels of full-length GFP mRNA and that a SLII-protected species appeared , which indicated that host 5’->3’ exonucleases were required for full degradation of target mRNAs ( Fig 2B ) . To determine whether Xrn1 in particular is required for full PA-X-initiated RNA degradation , Xrn1 expression was silenced in HEK 293T cells by doxycycline-inducible shRNA expression ( Fig 2E ) . As expected from previous observations ( Fig 1A ) , ectopic PA-X expression reduced GFP mRNA levels in control cells , but induction of Xrn1 shRNA expression reversed this phenotype ( Fig 2C ) . Using a northern blotting approach with a probe specific for GFP mRNA , we found that Xrn1 knock-down in wild-type PA-X-expressing cells caused the appearance of a heterogeneous population of partially digested GFP mRNA products ( Fig 2D , lane 7 ) . This is consistent with a model in which PA-X cleavage occurs throughout the length of the RNA with no discrete target site , followed by full digestion by host 5’-3’ exonucleases . As expected , neither a decrease in full-length GFP RNA levels nor the GFP fragments were detected when the D108A catalytic mutant of PA-X was expressed ( Fig 2D , lanes 3 and 8 ) . The lack of an apparent discrete primary endonuclease cleavage site makes PA-X unique amongst host-shutoff endonucleases studied to date , all of which either reveal sequence-specific cleavage sites on mRNAs , or target the 5’ end of transcripts [14 , 15 , 22 , 29 , 37] . Indeed , analysis of KSHV SOX cleavage products in Xrn1-deficient cells reveals the accumulation of a specific degradation product , reflecting cleavage at a discrete location in the mRNA ( Fig 2D , lane 10 ) , consistent with published reports [29] . Moreover , SARS nsp1 , which is reported to cut RNAs close to the 5’ end [22] did not cause the appearance of partially digested GFP mRNA products ( Fig 2D , lane 9 ) . Together , these data indicate that PA-X degrades host mRNAs in concert with Xrn1 and perhaps other cellular exonucleases , and unlike other host-shutoff endonucleases , lacks obvious sequence or location specificity , consistent with existing in vitro data [38] . Many Pol II-transcribed RNAs are translated . Association with components of the translation apparatus has been shown or proposed to be a determinant of selective mRNA targeting by other host-shutoff RNases , such as HSV-1 vhs [30 , 31] and KSHV SOX [29] . Moreover , SARS nsp1 requires active translation of RNAs for degradation [22] . To investigate the relationship between translation and RNA targeting by PA-X , we employed a Pol II-driven reporter that is not translated ( S2A Fig ) due to the insertion of a hairpin close the 5’ cap ( hp-GFP ) . This reporter mRNA associates with the translation initiation machinery , but cannot be translated because the hairpin blocks ribosome association and/or scanning [39] . We observed that PA-X decreased the levels of the hp-GFP and a control GFP mRNAs to a similar extent , suggesting that targeting is independent of mRNA translation ( Fig 3A ) . Conversely we used a dual-construct T7 polymerase system to direct transcription of luciferase RNA by overexpressed T7 polymerase [40] , rather than cellular Pol II . The T7-synthesized luciferase RNA is actively translated due to the presence of an EMCV internal ribosome entry site ( S2B Fig ) , but luciferase mRNA levels are unaffected by PA-X ( Fig 3B ) . These results indicate that synthesis by RNA Pol II , rather than translatability of the RNA , is a major determinant for targeting by PA-X . In addition to mRNAs , Pol II also transcribes several ncRNAs , including the long ncRNAs MALAT1 and TP53TG1 and the precursor of the small nuclear RNA U2 . Based on our reporter data ( Fig 3A and 3B ) , we hypothesized that Pol II transcribed ncRNAs may also be targeted by PA-X . Consistent with this hypothesis , we found that levels of the MALAT1 and TP53TG1 ncRNAs were reduced in 293T ( Fig 3C , S2C Fig ) and A549 iPA-X cell lines ( Fig 3D , S2D Fig ) . The effect of PA-X on the levels of U2 was variable; however this small nuclear RNA was unaffected in A549 iPA-X cells and some 293T iPA-X clones ( Fig 3C and 3D , S2C and S2D Fig ) . Half-life measurements for the actin mRNA and MALAT1 ncRNA in the presence of wild-type and catalytically inactive PA-X confirmed that PA-X directly affects the stability of the different RNA species ( S2E Fig ) . These results indicated that PA-X targets at least some Pol II synthesized ncRNAs for degradation and that , unlike herpesviral host shutoff endonucleases and SARS nsp1 , association with the protein synthesis machinery is not a prerequisite for targeting by PA-X . To determine whether PA-X selectively targets Pol II-transcribed RNA in the context of IAV infection , A549 cells were infected with PR8 or a mutant PR8-PA ( fs ) virus that should not produce PA-X due to codon optimization that prevents ribosome pausing and frameshifting ( Fig 4A ) . As we previously reported [41] , at later times post-infection PR8 virus causes dramatic depletion of cytoplasmic polyadenylated RNA which drives the nuclear relocalization of poly ( A ) binding protein ( PABP ) . By contrast , in cells infected with the PR8-PA ( fs ) mutant virus , nuclear PABP relocalization is significantly delayed , and only becomes detectable at 12 hours post-infection ( hpi , Fig 4B and 4C ) . The fact that PABP relocalization , a known PA-X-dependent phenotype , is markedly reduced in cells infected with the PR8-PA ( fs ) virus confirms that this virus is PA-X deficient; however , we note that nuclear PABP relocalization was still observed at later times post-infection . Other host shutoff mechanisms and/or leaky expression of PA-X in PR8-PA ( fs ) virus-infected cells may cause late nuclear localization of PABP . For this reason , we selected the 12 hpi time point for the analyses of host transcript levels . Consistent with our observations from PA-X ectopic expression experiments ( Fig 1 , S1 Fig ) , actin and GAPDH mRNA levels were reduced in a PA-X-dependent manner ( Fig 4D ) in PR8 IAV infected cells . We also detected a decrease in the levels of tubulin , POLR2A , and , to a lesser extent , RPS6 and RPS18 , but in these cases the decrease was only partially dependent on PA-X function , and may be due in part to other host-shutoff mechanisms . Interestingly , the Pol II-transcribed ncRNA MALAT1 and the histone mRNA HIST1H3C were strongly down-regulated in both PR8 wt and PR8-PA ( fs ) infected cells ( Fig 4D ) , suggesting that these RNAs are subject to regulation by other viral proteins . In contrast to our results with Pol II transcripts , the levels of Pol I and Pol III-transcribed ncRNAs ( 47S and 7SK , respectively ) were not altered in PR8 or PR8-PA ( fs ) infected cells . Collectively , these data show that during an IAV infection , PA-X selectively targets Pol II-transcribed RNAs for degradation . When determining the titers of recombinant PR8-PA ( fs ) virus stocks generated in our laboratory , we consistently observed reduction in plaque size compared to the parental wild-type PR8 strain ( Fig 5A ) . This suggests that PA-X function is important for efficient multi-round replication of IAV in culture , consistent with previous reports [19] . However , to date the exact contribution of PA-X to virus fitness remains poorly understood . In order to compare viral RNA and protein production between the wild-type PR8 virus and the PA-X deficient PR8-PA ( fs ) strain , we infected A549 cells with the same number of virions and collected parallel samples for immunofluorescence staining , western blotting for viral proteins , and total RNA isolation . Immunofluorescence staining for the viral PA protein confirmed that in our experiments a similar number of cells were infected with either wild-type PR8 or the mutant PR8-PA ( fs ) virus ( Fig 5B ) . Similar PA protein accumulation was also detected in wild-type and mutant virus-infected cell lysates at multiple time points ( Fig 5C ) . However , the accumulation of the viral proteins M1 , NS1 , and especially M2 was significantly slower in cells infected with PR8-PA ( fs ) mutant virus compared to wild-type virus-infected cells ( Fig 5C and 5D ) , which may be the cause for the reduction in plaque size . The reduced viral protein accumulation in the absence of PA-X was unexpected; if PA-X were able to degrade viral RNAs , we would expect an increase in viral protein levels in PR8-PA ( fs ) infected cells . These findings suggest potential secondary consequences of PA-X-mediated RNA degradation on viral protein accumulation . Importantly , we saw comparable levels of most viral mRNAs and genomic vRNAs in PR8 and PR8-PA ( fs ) infected cells ( Fig 5E ) . Only M1 mRNA accumulated to significantly higher levels in PR8-PA ( fs ) infected cells at later time points , with roughly 1 . 6-times more M1 transcript at 12 and 15 hpi ( Fig 5E and 5F ) . M2 and NEP mRNAs , which are generated through splicing of M1 and NS1 transcripts respectively , were slightly reduced in PR8-PA ( fs ) infected cells ( Fig 5E and 5F ) . Metabolic pulse-labeling of nascent RNAs by Click-IT chemistry revealed that the changes in M1 and NEP total mRNA levels resulted from increased ( M1 ) or decreased ( NEP ) synthesis rates , with relative total mRNA levels at 12 hpi overall matching the altered rates of synthesis at 9 hpi ( Fig 5F and 5G ) . Taken together , these data show that unlike cellular mRNAs synthesized by host Pol II , RdRp-generated viral mRNAs are not subject to PA-X mediated degradation . In fact , viral RdRp-generated mRNAs are translated more efficiently in the presence of PA-X , which may be due to reduced competition with host mRNAs for access to translation machinery . The selectivity of PA-X for Pol II transcripts could be linked to the RNA polymerase directly , or to processing events that are specific to Pol II transcripts . In particular , termination of transcription by Pol II is normally followed by addition of a non-templated poly ( A ) tail at the 3’ end of the RNA , which is absent from Pol I and Pol III transcripts , and the canonical polyadenylation signal serves to direct termination , cleavage of the RNA and polyadenylation . An alternative stem loop termination signal is used for histone mRNAs [42] , whereas some ncRNAs have distinct 3’ end processing mechanisms [43 , 44] . To test whether PA-X targeting of host Pol II transcripts is coupled to canonical 3’-end processing , we used a set of constructs that had altered 3’ ends . The 3’ polyadenylation signal and the 3’ untranslated region ( UTR ) of the GFP reporter were replaced by a self-cleaving hammerhead ribozyme ( HR ) or the histone stem loop termination region ( hisSL ) ( Fig 6A ) [45] . The HR sequence obviated the need for the cleavage and polyadenylation machinery in truncating the RNA 3’ end . We found that PA-X was unable to degrade the GFP-HR construct ( Fig 6B and 6C ) , which was also not translated as previously reported ( Fig 6D ) . Addition of a 60 nt templated stretch of adenosines that mimicked a poly ( A ) tail ( GFP-A60-HR ) restored translation of the GFP-HR construct ( Fig 6D ) while GFP-A60-HR mRNA levels were still unaffected by PA-X ( Fig 6B and 6C ) , suggesting that the lack of cellular 3’ end processing of this RNA prevented targeting by PA-X . By contrast , replacement of the 3’ poly ( A ) signal with a histone stem loop termination region promoted down-regulation by PA-X ( Fig 6B and 6C ) , consistent with the fact that the histone HIST1H3C mRNA was also down-regulated by PA-X expression ( Fig 1B and 1C , S1A and S1B Fig ) . Northern blotting confirmed that the size of the GFP RNA species is consistent with the expected processing route ( Fig 6C ) . In addition , we found that similarly to PR8 PA-X , PA-X variants from two other human strains of IAV , a pre-pandemic 2006 H1N1 strain and a 2009 pandemic H1N1 strain could degrade endogenous actin mRNA and transfected poly ( A ) -tailed GFP mRNA , but not GFP-A60-HR mRNA ( Fig 6E ) . Previous examples of protection of specific RNAs from host shutoff RNases have focused on the presence of protective elements . This is the case for SARS CoV mRNAs , which are protected from nsp1-mediated degradation [46] or for the host IL-6 mRNA during KSHV infection , which is protected from SOX-mediated degradation through 3’ UTR elements [47 , 48] . However , our results strongly suggest that resistance of IAV mRNAs to PA-X is due to their differential biogenesis pathway , in particular the 3’ maturation mechanism . These results , together with our viral mRNA data ( Fig 5E and 5F ) , suggest that the process of polyadenylation or the presence of the poly ( A ) tail per se are not sufficient or necessary for PA-X targeting of mRNAs . Instead they hint at a more universal feature of the 3’ end processing of the Pol II transcribed RNAs that earmark target RNAs for PA-X mediated cleavage . Since mRNA processing and maturation are linked to nuclear export , we compared the PA-X mediated changes in reporter transcript levels between cytoplasmic and nuclear RNA fractions . Importantly , PA-X was able to target both the cytoplasmic and the nuclear pools of the susceptible reporter RNAs ( poly ( A ) -tailed GFP and GFP-hisL ) , while we failed to detect down-regulation of the HR bearing constructs in either fraction ( S3A Fig ) . This demonstrates that PA-X is able to function in the nucleus and target transcripts prior to their export in the cytoplasm . Although all other host shutoff RNases are proposed to work in the cytoplasm , our results link PA-X targeting to 3’ end processing ( Fig 6 ) and show degradation of reporter transcripts in the nucleus ( S3A Fig ) . This suggests that PA-X may degrade nascent RNAs shortly after transcription . To test this hypothesis , we measured changes in the levels of endogenous transcripts in the nuclear and the cytoplasmic RNA fractions upon PA-X induction in A549 iPA-X cells ( Fig 7A ) . Consistent with the reporter RNA data , both nuclear and cytoplasmic actin and GAPDH mRNAs were down-regulated ( Fig 7A ) . Next , we compared PA-X dependent decrease in the levels of unspliced pre-mRNAs and mature mRNAs for these genes , as well as the MALAT1 ncRNA , in the nuclear fraction . We found that unlike processed mRNAs and MALAT1 , pre-mRNAs were not down-regulated ( Fig 7B ) . Because most splicing is thought to occur cotranscriptionally [49] , unspliced RNA levels likely reflect nascent RNA levels . Thus , this result reaffirms our finding that a later step of processing specific to Pol II transcripts is crucial for targeting by PA-X . Previous studies have demonstrated that the C-terminal domain of PA-X created by the frameshift , the X-ORF , is important for PA-X shutoff activity [34 , 50] . In particular the recent study by Oishi et al . [34] highlighted the importance of 6 highly conserved basic residues within the first 15 amino acids of X-ORF for PA-X function . Since we demonstrated that PA-X can function in the nucleus , we set out to examine the subcellular localization of PA-X and the effect of the X-ORF on the nucleocytoplasmic distribution of this protein . To this end we constructed a series of GFP fusion constructs containing the full-length wild type ( PA-X-GFP ) or catalytic mutant PA-X protein ( PA-X ( D108A ) -GFP ) , the N-terminal nuclease domain of PA-X ( PA-N191-GFP ) , the X-ORF ( GFP-X61 ) or shortened version of the X-ORF that mimics variants of PA-X with a 41 amino acid tail ( GFP-X41 , Fig 7C and 7D ) . In addition , we created a GFP X-ORF fusion protein in which four out of the six functionally important basic residues were mutated to alanines ( GFP-X61 ( 4A ) , Fig 7C and 7D ) . We observed that GFP-tagged PA-X and all fusion proteins containing the first 41 amino acids of X-ORF , although found throughout the cell , concentrated in the nucleus ( Fig 7E , S4A Fig ) . By contrast , the subcellular distribution of PA-N191-GFP and GFP-X61 ( 4A ) was highly similar to that of GFP alone ( Fig 7E and 7F and S4A Fig ) . Although GFP is small enough to show some nuclear accumulation on its own , the nuclear accumulation of the X-ORF fusion proteins was much more robust , and analysis of cells that expressed lower levels of the fusion proteins showed almost exclusive nuclear accumulation of the protein ( Fig 7E and S4A Fig ) . PA-X-GFP was efficient at blocking expression of co-transfected luciferase reporter constructs despite the large tag ( S4B and S4C Fig ) . Moreover , consistent with previous reports , the lack of the X-ORF severely impaired host-shutoff activity ( S4B and S4C Fig , PA-N191-GFP ) . As expected , despite nuclear accumulation , the PA-X ( D108A ) -GFP mutant , GFP-X61 or GFP-X41 did not possess the shutoff activity due to lack of the functional nuclease domain ( S4B and S4C Fig ) , although a previous report has argued that overexpression of X-ORF peptides alone has effects on gene expression [51] . Finally , we compared the shutoff function of myc-tagged constructs of the wild type PA-X , a PA-X with the four K/R-to-A mutations in the X-ORF ( PA-X ( 4A ) ) , and the N-terminal nuclease domain alone ( Fig 7G and 7H ) using luciferase reporter assay . In this assay , PA-X no longer inhibited luciferase expression when the four basic residues required for the nuclear localization function of the X-ORF ( Fig 7F ) were mutated to alanine , similarly to the complete deletion of the X-ORF ( Fig 7G and 7H ) . Collectively these data reveal a striking correlation between nuclear accumulation of the PA-X-GFP fusion proteins and their shutoff activity . More specifically , it shows that X-ORF acts as a nuclear accumulation domain and that the conserved basic residues important for PA-X shutoff function participate in key molecular interactions governing X-ORF-mediated nuclear accumulation . Our study provides new mechanistic insights into the specificity of PA-X , the most recently identified viral host shutoff nuclease . We demonstrate here for the first time that PA-X is recruited to the nucleus , selectively targets a subset of host transcripts , and is not active against viral mRNAs . Moreover , we uncover a novel route of target discrimination by a viral RNase , which takes advantage of the divergent mRNA biogenesis mechanisms that generate viral and host transcripts . We demonstrate that PA-X shares some specificity features with other host shutoff nucleases , as it selectively targets products of cellular RNA Pol II , while sparing Pol I and Pol III transcripts , and requires host RNases to complete RNA degradation . Interestingly , the mechanism for PA-X targeting of Pol II transcripts is not related to the translatability of the mRNAs . Instead , PA-X targeting is directly linked to synthesis by RNA Pol II complex or early processing events unique to Pol II transcripts . Moreover , we find that PA-X likely degrades RNAs in the nucleus , because it accumulates in this compartment and affects both the nuclear and cytoplasmic fraction of its target RNAs . Both the host shutoff activity of PA-X and the nuclear localization function of the C-terminal X-ORF are dependent on the presence of a set of basic residues in the X-ORF , indicating a correlation between nuclear localization and RNA targeting . Thus , the mechanism of PA-X targeting may be different from other shutoff RNases because it is tightly linked to the mechanism of biogenesis of host and viral mRNAs in the nucleus . Like other host shutoff endonucleases SOX , vhs and nsp1 [22 , 29 , 37] , PA-X carries out an initial cleavage of its targets and then relies on host enzymes to complete degradation of RNA fragments . Nonetheless , there are key differences between PA-X and these previously described enzymes . PA-X cleavage is not directed by specific sequence elements , as previously reported for the gamma-herpesviral host-shutoff RNases [14 , 15 , 29] , or by proximity to the 5’ end of the message , like SARS CoV nsp1 [22] or alphaherpesvirus vhs [37] . Analysis of the GFP reporter upon Xrn1 knock-down suggests that PA-X cuts RNAs throughout the transcript ( Fig 2D ) . Whether this is true for endogenous mRNA targets remains to be determined . Moreover , whereas a tight relationship with translational machinery has been reported for some of the endonucleases ( vhs , nsp1 [22 , 30 , 31] ) , we find that translation is not a key determinant of PA-X targeting . Pol II-transcribed RNAs that are not translated , like the 5’ hairpin containing GFP reporter and the endogenous ncRNAs MALAT1 or TP53TG1 are down-regulated by PA-X ( Fig 3A , 3C and 3D , S2A , S2C–S2E Fig ) . By contrast , translated viral mRNAs ( Fig 5C–5F ) and the translated GFP-A60-HR and T7-polymerase driven luciferase reporter constructs ( Figs 3B , 6B–6E , S2B and S3A Figs ) are not affected . Although we cannot exclude that other processes connected to translation are important for RNA degradation , our data strongly indicate that translation per se is not required . Our data point instead to the processing of Pol II transcripts in the nucleus as the link between PA-X and its target . Importantly , our examination of endogenous and reporter Pol II transcripts that differ in their 3’ end processing suggests that this step in biogenesis may be recognised by PA-X to find its targets . In our experiments , GFP reporters bearing the canonical polyadenylation signal ( PAS ) or a histone stem loop at the 3’ end were efficiently inhibited by PA-X . By contrast , the same reporter with a templated poly ( A ) stretch followed by a self-cleaving hammerhead ribozyme sequence was not degraded by PA-X despite being translated ( Fig 6 ) . Replication-dependent histone mRNAs and polyadenylated mRNAs are processed very differently , but some proteins participate in both mechanisms including the scaffold protein symplekin and the cleavage and polyadenylation specificity factor ( CPSF ) complex subunit CPSF-73 [52 , 53] . Nuclear localisation and targeting of nuclear mRNA pools by PA-X demonstrated in our study also strongly hint at a mechanism that involves association with target mRNAs prior to their engagement in translation in the cytoplasm . The fact that in our experiments nuclear unspliced pre-mRNAs were largely unaffected by PA-X ( Fig 7B ) also suggests that the later stages of mRNA maturation are responsible for PA-X recruitment . A host protein involved in Pol II transcript 3’ end processing could in theory earmark the RNA for cleavage by PA-X , although our data on the nuclear MALAT1 ncRNA hints at a more complex model . The MALAT1 gene includes a canonical PAS , but the most abundant MALAT1 transcript is processed by RNase P at a location upstream of the PAS , and is therefore not polyadenylated [43] . It is possible that the presence of the PAS could still promote co-transcriptional recruitment of cellular factors necessary for PA-X targeting , regardless of whether it is used for 3’-end processing , because the PAS can direct transcription termination independently of 3’ cleavage and polyadenylation [54] . In general , determining whether PA-X physically interacts with transcription and RNA processing machinery will be key to understanding how PA-X targets Pol II-transcribed RNAs in the nucleus . A major challenge for future studies will be to disentangle the highly interconnected steps of RNA metabolism to better understand PA-X function . Our data also demonstrate a new cellular function for the C-terminal X-ORF , whose required function in host shutoff [34 , 50] is poorly understood . We found that the conserved 41-amino acid portion of the X-ORF is sufficient to cause nuclear accumulation of GFP fusion proteins and acts as a nuclear targeting sequence ( Fig 7C–7F and S4A Fig ) . Moreover , nuclear localization of PA-X is tightly linked to reporter shutoff efficiency ( Fig 7G and 7H ) . At present it remains to be determined whether the X-ORF contains a functional NLS or whether it causes nuclear accumulation through interactions with other NLS-containing proteins . The latter mechanism appears more likely , because the region responsible for the nuclear localization function of X-ORF lacks homology to well-characterized NLS consensus sequences ( Fig 7D ) . Also , because increased in vitro activity has been reported for PA-X vs . the N terminal RNase domain alone [38] , the X-ORF probably has additional roles in RNA degradation by PA-X . The fate of viral mRNAs and proteins during host shutoff has been a topic of intense study , as the expectation is that viral products should be protected from host shutoff to confer a selective advantage . For example , SARS CoV transcripts are protected from RNA degradation by nsp1 by a common 5’ leader sequence [46] . Interestingly , some viruses , like gamma-herpesviruses , have no mechanism for widespread protection of viral mRNAs [55] . Early studies of IAV host shutoff demonstrated that the virus selectively inhibits translation of host mRNAs , while IAV mRNAs appear resistant [1] . One of the well characterised host shutoff mechanisms employed by IAV is mediated by the NS1 protein from human IAV strains that binds and inactivates cellular CPSF30 , preventing polyadenylation and nuclear export of host pre-mRNAs [2] . IAV mRNAs are exempt from CPSF30 inactivation because they rely exclusively on RdRp for addition of the poly ( A ) tail . Remarkably , we discovered that mRNA 3’ end processing in the nucleus may serve as the basis for the protection of viral mRNAs from PA-X as well . Future studies will determine whether similarly to NS1 , PA-X is recruited to its target RNAs through direct interaction with one of the subunits of 3’ end processing machinery . We observed no major effects of PA-X on the levels of viral mRNAs and vRNAs comparing viral RNA levels between wild-type and PA-X deficient mutant viruses ( Fig 5E ) . The only exceptions are the M1 mRNA , which is transcriptionally up-regulated , albeit to a small degree , and the spliced M2 and NEP mRNAs , which are synthesized at slightly lower rates ( Fig 5E–5G ) . One recent study reported an increase in RdRp activity and levels of PA protein in PA-X-deficient viruses [17] . We note that we do not see an increase in PA levels in the PR8-PA ( fs ) mutant virus-infected cells ( Fig 5B–5D ) . In fact , several of the IAV PR8 proteins accumulated to lower levels in the absence of PA-X , including M1 , despite an increase in its mRNA levels . This suggests that , like HSV-1 vhs [56] , PA-X may also function to reduce competition for translational machinery between host and viral mRNAs . In turn , changes in viral protein accumulation could explain the transcriptional and splicing changes we detected , because viral proteins have roles in regulating viral gene expression . For example , NS1 is required for the production of correct levels of spliced M2 mRNA [57] , and we detect both changes in NS1 protein accumulation and in M2 mRNA and protein levels . The reduced accumulation of M2 in particular may be responsible for the small plaque phenotype that we have observed with PR8-PA ( fs ) virus , as mutations in M2 have been reported to give rise to a small plaque phenotype [58] . Previous studies using different strains of IAV have reached different conclusions on the role of PA-X in replication of the virus in tissue culture and virulence in animal models [5 , 16–19] , suggesting that the effect of PA-X on viral replication and disease is strain dependent . In this study we used the mouse adapted PR8 strain that possesses NS1 protein lacking the ability to bind CPSF30 and block host mRNA polyadenylation . Because PR8 lacks at least one of the other IAV host-shutoff mechanisms , PA-X could play a more important role in host shutoff and/or have stronger effect on viral fitness in our model . Additional studies examining the interplay of the PA-X and NS1-mediated host shutoff mechanisms will further advance our understanding of IAV host shutoff and its role in viral replication , pathogenicity , and species adaptation . pCR3 . 1-PA-X-myc and pCR3 . 1-PA-N191-myc were generated by inserting the PCR-amplified full length PA-X or the PA nuclease domain sequences from pCR3 . 1-PA-X plasmid [41] into the pCR3 . 1-myc vector [59] between KpnI and MluI sites . Subsequently , pCR3 . 1-PA-X ( D108A ) -myc and pCR3 . 1-PA-X ( 4A ) -myc vectors were generated by site-directed mutagenesis of the pCR3 . 1-PA-X-myc plasmid . In order to create the GFP-tagged constructs pCR3 . 1-PA-X-GFP , pCR3 . 1-PA-X ( D108A ) -GFP , and pCR3 . 1-PA-N191-GFP , the myc tag sequence flanked by MluI and XhoI sites was replaced with the PCR-amplified EGFP ORF . pCR3 . 1-EGFP control vector was created by inserting EGFP ORF between BamHI and XhoI sites of pCR3 . 1-myc plasmid . An AgeI restriction site was introduced by PCR immediately upstream of the EGFP ORF stop codon to enable insertion of the C-terminal X-ORF sequences . Subsequently , full-length ( X61 ) , shortened ( X41 ) , and mutant [X61 ( 4A ) ] X-ORF sequences were PCR-amplified from the pCR3 . 1-PA-X-myc and pCR3 . 1-PA-X ( 4A ) -myc templates without the inclusion of the myc tag and inserted between AgeI and XhoI sites to create pCR3 . 1-GFP-X61 , pCR3 . 1-GFP-X41 , and pCR3 . 1-GFP-X61 ( 4A ) vectors . pCR3 . 1–2006 H1N1 PA-X-myc and pCR3 . 1–2009 TN H1N1 PA-X-myc were generated by subcloning PA-X ( including the 5’ UTR ) from A/Hong Kong/218847/2006 ( H1N1 ) and A/Tennessee/1-560/2009 ( pandemic H1N1 ) IAV segment 3 constructs that were a kind gift from R . Webby ( St Jude’s Children Research Hospital , Memphis , TN ) into the SalI and MluI sites of pCR3 . 1-myc . A single nucleotide was deleted to obtain the PA-X coding sequence using overlap extension . pd2-eGFP-N1 was purchased from Clontech . pd2-eGFP-N1-CMVd1 ( used in Fig 1A ) was generated by deleting 457 bp from the CMV IE promoter in pd2-eGFP-N1 , in order to reduce the constitutive levels of GFP mRNA and protein . GFP-3’SLII , GFP-codingSLII , GFP-HR , GFP-A60-HR , GFP-hisSL and hp-GFP constructs are based on pd2-eGFP-N1 and were previously described [14 , 29 , 45] . Pol I GFP , Pol III GFP , pCDEF3-SOX , pCAGGS-nsp1 , and pCDNA3 . 1-DsRedExpress-DR were previously described [8 , 14 , 29] . pTRIPZ-shNS and pTRIPZ-Xrn1 were purchased from Thermoscientific ( shXrn1: clone V2THS_89028/RHS4696-99704634 , targeting sequence: TATGGTGAGATATACTATG ) . pTRIPZ-PA-X-myc was generated by replacing the RFP sequence in pTRIPZ-shNS with the PA-X-myc coding sequence using the AgeI and ClaI restriction sites . pTRIPZ-RFP-SV40-3’UTR was generated by substituting the shRNA sequence downstream of the RFP coding sequence in pTRIPZ-shNS with the SV40 3’ UTR from pd2-eGFP-N1 . The RFP sequence was then replaced with PA-X-myc or PA-X ( D108A ) -myc to generate pTRIPZ-PA-X-myc-SV40-3’UTR and pTRIPZ-PA-X ( D108A ) -myc-SV40-3’UTR using the AgeI and ClaI restriction sites . All PA-X constructs also include the viral 5’ UTR of PR8 IAV segment 3 . The T7 polymerase and T7-driven EMCV-luciferase constructs [40] were a kind gift from E . Heldwein ( Tufts University School of Medicine , Boston , MA ) . They were co-transfected in 293T iPA-X cells to obtain luciferase transcription by T7 polymerase . Luciferase RNA and luciferase activity were only detected when the T7 polymerase expression construct was co-transfected , confirming that the luciferase RNA is solely transcribed by T7 . pTRE2-Fluc and pTRE2-Rluc vectors were previously described [60] . pGL4 . 32 and pGL4 . 74 reporter plasmids were purchased from Promega . Sequence information for the vectors generated in this study is available upon request . Mouse embryonic fibroblasts ( gift from Dr . Kedersha , Brigham and Women’s Hospital , Boston , MA ) , HeLa Tet-Off ( Clontech ) , HEK 293A ( ATCC ) , HEK 293T ( ATCC ) , A549 ( ATCC ) , and their derivative 293T and A549 “iPA-X” cells were maintained in Dulbecco’s modified Eagle’s medium ( DMEM; high glucose , Life Technologies ) supplemented with 10% fetal bovine serum ( FBS , Hyclone ) at 37°C in 5% CO2 atmosphere . HEK 293T iPA-X cells were generated by lentiviral transduction using pTRIPZ-PA-X-myc . A549 iPA-X were generated by lentiviral transduction using pTRIPZ-PA-X-myc-SV40-3’UTR or pTRIPZ-PA-X ( D108A ) -myc-SV40-3’UTR . Clonal populations were selected and several lines were used for all experiments . PA-X expression was induced by addition of doxycycline ( Fisher , 0 . 2–1 μg/ml final concentration depending on the cell line ) for approximately 18 h prior to harvesting , with the exception of half-life experiments , in which doxycycline was added for 4 . 5 h before actinomycin D addition . HEK 293T shNS ( also referred to as “iRFP” cells ) and shXrn1 cells [15] were generated using pTRIPZ-shNS ( Thermo scientific ) and pTRIPZ-shXrn1 ( Thermo scientific ) respectively . To induce expression of the shRNAs , cells were treated with 1 μg/ml doxycycline for 4–5 days prior to harvesting . For experiments using reporter constructs , 800 ng of DNA was transfected in 12-well plate wells using polyethylenimine ( Fisher ) . For the experiment in Fig 6E , FugeneHD ( Promega ) was used for transfection , in order to achieve high transfection efficiency ( estimated at > 75% based on GFP fluorescence ) . RNA and proteins were harvested as detailed below 1 day after transfection . For measuring the levels of the Firefly and Renilla luciferase reporter expression the Dual-Luciferase Reporter Assay Kit ( Promega ) was used according to manufacturer protocol . The A/PuertoRico/8/34/ ( H1N1 ) ( PR8 ) and the recombinant mutant PR8-PA ( fs ) viruses are described in [41] . Virus stocks used for experiments were produced and titrated by plaque assays as described [41] . Two independent recombinant virus rescues were performed as described in [61] for both wild-type PR8 and PR8-PA ( fs ) and used in experimental infection replicates . The genomic RNA segment 3 of all virus stocks was verified by sequencing . For each infection , after 1-hour inoculation with virus dilutions , cells were washed with PBS and cultured in infection medium ( 0 . 5% bovine serum albumin ( BSA ) in DMEM ) and incubated at 37°C in 5% CO2 atmosphere . Cells grown on glass coverslips were fixed and immunostained according to the protocol in [62] using mouse monoclonal antibody to PABP1 ( sc-32318 , Santa Cruz Biotechnology ) ; goat polyclonal antibody to influenza virus ( ab20841 , Abcam ) , or rabbit antibody to PA ( GeneTex-125932 ) at manufacturer-recommended dilutions . Nuclei were stained with Hoechst dye ( Invitrogen ) . AlexaFluor-conjugated secondary antibodies ( Molecular Probes ) were used at 1:1 , 000 dilution . Images were captured using Zeiss Axioplan II microscope . For steady-state measurements of RNA levels in cells ectopically expressing PA-X protein , RNA was harvested 1 day after transfection or 18 h after PA-X induction in iPA-X cells . For half-life measurements , PA-X ( wt or D108A ) was induced in A549 iPA-X cells for 4 . 5 h , followed by addition of actinomycin D at 10 μg/ml final concentration . RNA was collected 0 , 2 , 4 , 6 h after actinomycin D addition , as well as prior to doxycycline treatment . RNA for Northern blotting was harvested and extracted using Trizol reagent ( Life Technologies ) following manufacturer’s protocol . To isolate cytoplasmic vs . nuclear fractions , cells were lysed in 0 . 1% NP-40 in Dulbecco’s phosphate buffered saline ( DPBS ) and the nuclear pellet was spun down . The supernatant was collected as the cytoplasmic fraction . The pellet was washed in 0 . 1% NP-40 in DPBS , then it was collected as the nuclear fraction . The RNA was extracted from both fractions using Trizol ( Life Technologies ) following manufacturer’s protocol . The RNA for northern blotting was run on a 1 . 8% agarose/formaldehyde gel and transferred by capillary action onto nitrocellulose membrane ( Bio-Rad ) using 10x SSC buffer . Northern blots were probed with probes against the SV40 3’ UTR present in pd2-eGFP-N1 [29] , the first 450 nt of the GFP coding sequence , the endogenous 18S rRNA [45] , 7SL and U2 ncRNAs in Church buffer . Probes were generated and radiolabeled using the Decaprime II kit ( Life Technologies ) . Blots were imaged using a Fujifilm scanner FLA-9000 . Quantification of the blots was carried out using ImageJ [63] . Figures show images representative of multiple biological replicates . Total cellular RNA for RT-qPCR in iPA-X and transfected cells was harvested and isolated using Zymo mini-prep kit ( Zymo Research ) following manufacturer’s protocol . cDNA was generated using iScript ( Bio-Rad ) from DNase-treated RNA . For analyses of mRNA and vRNA levels in A549 cells infected with IAV , total RNA was isolated at 6 , 9 , 12 , and 15 h post-infection using Qiagen RNeasy Plus kit . For cDNA synthesis , Thermo Maxima H Minus kit was used with gene-specific primer for 18S rRNA combined with either oligo ( dT ) ( for mRNA targets ) or influenza vRNA-specific Uni12 primer [64] . To minimize primer-less reverse transcription , after primer annealing reactions were carried out at 65°C according to manufacturers’ protocol . Quantitative PCR analysis was performed using iTaq Universal SYBR Green Supermix ( Bio-Rad ) and Ct values were analyzed using the BioRad CFX Connect Real-Time System qPCR and Bio-Rad CFX Manager 3 . 1 program analysis . S1 Table lists primers used for qPCR . Pre-mRNA measurements were carried out with primer sets that have been previously used in the literature and that are located within predicted introns [65] . All qPCR experiments shown are the average of three or more biological replicates . Within each biological replicate , RNA levels were assessed using the average of at least two technical replicates . One- and two-sample Student’s t-test was used to analyze values for significant differences . Metabolic labeling and isolation of nascent RNA in A549 cells infected with wild-type PR8 or mutant PR8-PA ( fs ) viruses was performed using Molecular Probes Click-iT Nascent RNA Capture Kit . At 8 hpi , 0 . 4 mM Click-iT nucleotide analogue 5-ethynyl uridine ( EU ) was added to the infection media for 1 hr at 37°C . At 9 hpi monolayers were washed twice with PBS and total RNA was isolated as described using Qiagen RNeasy Plus kit . Biotinylation and subsequent purification of EU-labelled RNA was performed according to the kit manufacturer protocol . cDNA synthesis and qPCR was performed on streptavidin beads as directed by the kit manufacturer protocol and described in methods section above . Total cellular protein was collected in protein lysis buffer ( 10 mM Tris pH 8 , 150 mM NaCl , 1% Triton x100 , and cOmplete EDTA-free protease inhibitor cocktail ( Roche ) ) unless specified otherwise in the text . Proteins were separated on SDS-PAGE gels and transferred onto PVDF membranes ( Millipore ) . Western blots were performed in PBST with 5% milk or TBST with 4% bovine serum albumin . The following antibodies were used: Xrn1 ( 1:200 , Santa Cruz-16598 ) , IAV NS1 ( 1:1 , 000 , gift from Kevin Coombs [66] , clone 8C7 ) M2 ( 1:1 , 000 , Abcam ab5416 ) , GFP ( 1:500 , Santa Cruz-8334 ) , β tubulin ( 1:200 , Santa Cruz-9104 ) , actin ( 1:4 , 000 , HRP-conjugated , Cell Signalling #5125 ) , IAV PA ( GeneTex-125932 ) , IAV ( 1:2 , 000 , Abcam ab20841 , recognizes NP , M1 , and ( weakly ) HA proteins of PR8 strain ) . Secondary antibodies were purchased from Southern BioTech ( rabbit , mouse ) or Santa Cruz ( goat ) and used at 1:3 , 000 to 1:5 , 000 dilution . Human genes: β-actin: ACTB/60; β-tubulin: TUBB/ 203068; EEF1A: EEF1A1/1915; Histone cluster 1 H3C: HIST1H3C/8352; POLR2A: POLR2A/5430; MALAT1: MALAT1/378938; TP53TG1: TP53TG1/11257; GAPDH: GAPDH/2597; GUSB: GUSB/2990; RPS6: RPS6/6194; RPS18: RPS18/6222; 7SL: RN7SL1/6029; 7SK: RN7SK/125050; U2: RNU2-1/6066 . IAV genes: PR8 PA-X: PA-X/13229134 .
All viruses depend on host components to convert viral mRNAs into proteins . Several viruses , including influenza A virus , encode factors that trigger RNA destruction . The influenza A virus factor that serves in this capacity is known as PA-X . PA-X limits accumulation of host mRNAs and proteins in infected cells and suppresses host responses to infection , but to date its precise mechanism of action remains obscure . Here we report that PA-X selectively targets cellular mRNAs , while sparing viral mRNAs , thereby compromising host gene expression and ensuring priority access of viral mRNAs to the protein synthesis machinery . We demonstrate that complete degradation of mRNAs cut by PA-X is dependent on the host factor Xrn1 and that PA-X likely works in the cell’s nuclei . Interestingly , PA-X targeting appears to be selective for products of host RNA polymerase II , and canonical mRNA processing is required for cleavage . Even though viral mRNAs are spared from PA-X-mediated degradation , PA-X-deficient viruses displayed defects in the synthesis of certain viral mRNAs and decreased viral protein accumulation . Thus , PA-X-mediated host shutoff influences the efficiency of viral gene expression . These studies significantly advance our understanding of this important viral host shutoff protein and may provide future opportunities to limit the pathogenesis of influenza A virus infection .
You are an expert at summarizing long articles. Proceed to summarize the following text: Atypical/Nor98 scrapie was first identified in 1998 in Norway . It is now considered as a worldwide disease of small ruminants and currently represents a significant part of the detected transmissible spongiform encephalopathies ( TSE ) cases in Europe . Atypical/Nor98 scrapie cases were reported in ARR/ARR sheep , which are highly resistant to BSE and other small ruminants TSE agents . The biology and pathogenesis of the Atypical/Nor98 scrapie agent in its natural host is still poorly understood . However , based on the absence of detectable abnormal PrP in peripheral tissues of affected individuals , human and animal exposure risk to this specific TSE agent has been considered low . In this study we demonstrate that infectivity can accumulate , even if no abnormal PrP is detectable , in lymphoid tissues , nerves , and muscles from natural and/or experimental Atypical/Nor98 scrapie cases . Evidence is provided that , in comparison to other TSE agents , samples containing Atypical/Nor98 scrapie infectivity could remain PrPSc negative . This feature will impact detection of Atypical/Nor98 scrapie cases in the field , and highlights the need to review current evaluations of the disease prevalence and potential transmissibility . Finally , an estimate is made of the infectivity loads accumulating in peripheral tissues in both Atypical/Nor98 and classical scrapie cases that currently enter the food chain . The results obtained indicate that dietary exposure risk to small ruminants TSE agents may be higher than commonly believed . Transmissible spongiform encephalopathies ( TSE ) , or prion diseases , are fatal neurodegenerative disorders occurring in sheep ( scrapie ) , cattle ( bovine spongiform encephalopathy - BSE ) , or humans ( Creutzfeldt-Jakob disease - CJD ) . The key event in TSE is the conversion of a normal cellular protein ( PrPc ) into an abnormal isoform ( PrPSc ) which accumulates in tissues from infected individuals [1] . PrPSc is currently considered to be the only TSE biochemical marker . According to the prion concept , abnormal PrP would be the causative agent of TSE [2] . Following the BSE crisis and the identification of its zoonotic properties [3] , [4] , the control of human and animal exposure to TSE agents has become a priority . A sanitary policy has been implemented based on both eradication of TSE in food producing animals and exclusion of known infectious materials from the food chain . In 1998 an Atypical/Nor98 Scrapie was identified in Norwegian sheep; the PrPSc signature was partially PK resistant and displayed a multi-band pattern as showed by Western Blot ( WB ) that contrasted with those normally observed in small ruminants TSE cases [5] . After 2001 and the implementation of active TSE surveillance plans , a number of similar cases were identified in most EU members states as well in other countries , like Canada , USA and New Zealand [6] . The transmissibility of Atypical/Nor98 agent has been demonstrated in both rodent models ( transgenic animals expressing the ovine Prnp gene ) [7] and sheep [8] , [9] . Currently Atypical/Nor98 Scrapie represents a significant part of the TSE cases identified in the EU small ruminant population , where its prevalence was estimated to range between 5 to 8 positive small ruminants per 10 , 000 tested per year [10] . Atypical/Nor98 scrapie cases have different biological features from those observed in other small ruminants TSE [5] , [6] . Sheep susceptibility to TSE is strongly controlled by polymorphisms on the gene ( Prnp ) encoding for PrP protein [11] , [12] . The homozygous and heterozygous ARR sheep are considered to be strongly resistant to both the classical scrapie [11] , [12] and the cattle BSE agents [13] . This resistance has been the basis of a large scale genetic selection policy aiming at the control of TSE diseases by increasing the frequency of the ARR allele in general population and restocking affected flocks with ARR animals . In Atypical/Nor98 scrapie the sheep genetic susceptibility is significantly different from what is observed in classical TSE forms , with homozygous and heterozygous ARR allele carriers being susceptible to the disease [14] , [15] , [16] , [17] , [18] . Information about the tissue distribution of Atypical/Nor98 scrapie agent in the host species is limited [5] , [19] , [20] , [21] but research findings indicate that no detectable abnormal PrP has been found in peripheral tissues and that the infectious agent could be restricted to the central nervous system . This key feature led to consider that dietary exposure risk to Atypical/Nor98 scrapie is low . The apparent limited spreading of Atypical/Nor98 scrapie in the organism of affected individuals is also an argument supporting the hypothesis that this agent has restricted abilities to spread into the environment or between individuals . PrPSc detection generally correlates with the presence of infectivity [1] , [22] but infectivity has been reported in the absence of detectable PK resistant PrP [23] . In this study , we investigate the potential presence of TSE infectivity in peripheral tissues ( lymphoid organs , striated muscles and nerves ) of Atypical/Nor98 scrapie from naturally and experimentally infected sheep . We then compared the relative infectivity level present in peripheral tissues with those estimated in similar tissues from classical scrapie affected sheep . Atypical/Nor98 scrapie field cases ( n = 7 ) collected in three different countries ( Portugal , Norway and France ) were investigated for the presence of PrPSc and infectivity in lymphoid tissues and central nervous system ( Table 1 ) . These sheep were of various Prnp genotypes including those associated with high susceptibility to Atypical/Nor98 scrapie ( homozygous or heterozygous A136F141R154Q171– AHQ ) or resistance ( homozygous and heterozygous ARR ) to classical scrapie or BSE [11] , [13] . Amongst the seven cases , five were identified by the active surveillance program either at rendering plant or slaughter house and two through the passive surveillance network ( clinical suspects ) . Three of these cases were identified in fallen stock animals collected in three independent flocks where an Atypical/Nor98 scrapie case had previously been identified ( Portugal ) . In each of these natural Atypical/Nor98 cases , PrPSc accumulation could be detected in different brain areas by WB and/or Immunohistochemistry ( IHC ) . Conversely , no abnormal PrP deposits were evidenced in any of the investigated lymphoid organs ( Table 1 ) . Bioassay of brain homogenates prepared from these seven sheep into transgenic mice that over-express the VRQ allele of ovine PrP ( tg338 ) [7] were positive for TSE . Surprisingly , despite the absence of detectable PrPSc , the lymphoid tissue homogenates from five out of the seven cases were positive for TSE in tg338 mice . The attack rate in mice challenged with lymphoid tissues was lower and the clinical onset was delayed compared to mice inoculated using CNS homogenate ( Table 1 ) . Brains collected in clinically affected mice inoculated either with lymphoid tissues or brain homogenates displayed a similar PrPSc WB pattern ( Figure 1: lanes 5 , 6 , 7 – Figure S2 ) , PrPSc deposits distribution and vacuolar lesion profile ( Figure 2A , 2C , 2E- – Figure S2 ) . All these features were identical to those previously reported in tg338 mice inoculated with a panel of Norwegian , French [7] and UK [24] Atypical/Nor98 scrapie isolates . These phenotypic features were clearly different from those associated to two distinct classical scrapie isolates ( Figure 1: lanes 1–4 and Figure 2B , 2F , 2H ) . Infectivity was demonstrated in lymphoid tissues from one out of the two investigated ARR/ARR Atypical/Nor98 scrapie cases . Samples collected in two experimental Atypical/Nor98 scrapie cases were also analyzed; these were from an AHQ/AHQ ( case 9 ) and an AFRQ/ARQ ( case 8 ) sheep which had been intra-cerebrally challenged with an AFRQ/AFRQ field Atypical/Nor98 scrapie isolate ( case 1 , Figure 1: lane 5 ) . These sheep had an incubation period of 964 and 2240 days respectively . In both animals' CNS , PrPSc displayed a WB banding pattern that was characteristic [14] of Atypical/Nor98 scrapie ( Figure 1: lane 6 – Figure S2 ) . In none of the investigated peripheral tissues PrPSc could be detected ( Table 2 ) . In the tg338 bioassay , infectivity was shown in some but not all tested lymphoid tissues , and in striated muscle and peripheral nerves from both cases ( Table 2 – Figure 1: lanes 8–9 – Figure S2 ) . Incubation periods were prolonged in comparison to brain homogenates and the attack rate was lower than 100% . The phenotypes of the propagated prions in tg338 mice ( WB banding pattern , vacuolar lesion profile and PET Blot PrPSc distribution in brain ) were identical to the one observed with natural Atypical/Nor98 scrapie isolates ( Figure 1: lanes 5 , 7 , 9- Figure 2C , 2E , 2G ) . A similar experiment was performed using peripheral and CNS tissues from natural or experimental classical scrapie cases at clinical stage of the disease . This study involved two distinct classical scrapie agents ( Langlade and PG127 ) , which can be distinguished on the basis of their lesion profile in tg338 mice ( Figure 2F ) . The inoculation of peripheral tissues homogenates from animals infected with those classical scrapie agents into tg338 resulted in a 100% attack rate transmission , but with prolonged incubation period by comparison to mice inoculated with CNS samples ( Table 2 ) . For both isolates , PrPSc WB banding pattern ( Figure 1: lanes 1–4 – Figure S2 ) , vacuolar lesion ( Figure 3F , 3H – Figure S2 ) observed in mice inoculated with CNS and peripheral tissues were identical . As previously described and conversely to Atypical/Nor98 scrapie , PrPSc could be detected in the investigated lymphoid organs , and striated muscle [25] , [26] , [27] , [28] from all the four classical scrapie affected animals involved in this study ( Table 2 ) . In order to determine the cause of our incapacity to detect abnormal PrP in the peripheral tissues of Atypical/Nor98 scrapie cases that contain infectivity , classical scrapie ( cases 10 and 12 ) and Atypical/Nor98 scrapie ( cases 1 , 8 , 9 ) brain homogenates dilution series were prepared and processed for a OIE registered PrPSc detection WB ( TeSeE WB Kit – BIORAD ) , a PrPSc ELISA detection assay ( TeSeE Sheep and Goat - BIORAD ) ( Figure 3 and Figure S1 ) and bioassays in tg338 mice ( Table 3 ) . According to the endpoint titration , the infectious titre in the Langlade and PG127 classical scrapie isolates were estimated to be respectively 106 . 8 ID50 IC tg338 per gram and 106 . 6 ID50 IC tg338 per gram ( Table 3- Figure 4A ) . All the titrated Atypical/Nor98 scrapie cases ( n = 5 , see Table 3 ) that we investigated displayed substantially higher infectious titres , ranging between 108 . 7 and 109 . 5 ID50 IC tg338/g ( Table 3-Figure 4 B ) . For the two classical scrapie isolates , WB detected PrPSc to a dilution of 103 ( positive detection on 25 µg of brain equivalent material ) which corresponded to 102 . 2 ( Langlade isolate ) and 102 ( PG127 isolate ) ID50 IC tg338 ( Figure 3 ) . For the AHQ/AHQ Atypical/Nor98 scrapie isolate ( case 9 ) , PrPSc WB detection limit was the 1/80 dilution ( 312 µg of brain starting material ) ( Figure 3A ) which corresponded to 106 IC ID50 in tg338 ( Figure 4E ) . Similar results were obtained with the two other atypical scrapie isolates ( cases 1 and 8 ) for which the detection limits of PrPSc assays were respectively equivalent to 105 . 6 and 105 . 5 ID50 IC tg338 ( Table 3 and Figure S1 ) . These results indicate that PrPSc detection assays currently used for field TSE testing could have a dramatically lower intrinsic sensitivity for identifying Atypical/Nor98 scrapie agent than classical scrapie agent . As previously described [29] , [30] , [31] , [32] , [33] , [34] , the end point infectivity titration data that was generated with the two different types of classical scrapie agents ( case 10 and case 12 ) and the Atypical/Nor98 scrapie cases ( cases 14 and 15 ) were used to fit the best logistic regression models correlating the incubation periods in tg338 mice with the infectious dose ( Figure 4 ) . These models were then applied to estimate the infectious content in the different tissues using the incubation periods in tg338 mice ( Tables 1 and 2 ) . By this approach , the infectious titre of three atypical scrapie samples ( cases 1: cerebral cortex - case 8: cerebellum – case 9: cerebral cortex ) were estimated respectively 108 . 7 , 108 . 7 , and 108 . 3 ID50 IC tg338/gram ( Tables 1 and 2 ) . The measured infectious titre in the same three samples , by endpoint titration in tg338 , were respectively 109 . 1 , 108 . 7 and 10 9 . 5 ID50 IC tg338/gram ( Table 3 ) . In the investigated Atypical/Nor98 scrapie cases , the infectious load in muscle and lymphoid tissues samples from sheep affected were close to the sensitivity of our bioassay ( 102 , 7 ID50 IC per gram of tissue ) ; ie about 106 fold lower than the infectivity level measured ( by endpoint titration ) or estimated ( on the basis of the incubation periods ) in the same amount of brain prepared from clinically affected sheep ( Tables 1 and 2 ) . In sheep affected by the two different classical scrapie agents the incubation period recorded in tg338 inoculated with lymphoid tissues and striated muscle were consistent with infectious titre about 10 fold lower than the one measured in brains from those terminally affected animals ( Table 2 ) . Bioassay endpoint titration is considered as the most accurate method for determining the TSE infectivity titre in tissues . Although regarded as less accurate , dose-response relationships have been used as a method for infectivity estimation when endpoint titration data are not available; in such an approach , the incubation period observed in the inoculated mice is used to estimate the infectious titre of the samples tested [29] , [30] , [31] , [32] , [33] , [34] . In this study , the dose-response approach was used to estimate the infectious titre in various peripheral tissues from Atypical scrapie/Nor98 and classical scrapie affected sheep . For mice inoculated with peripheral tissue homogenates , the standard curve established using reference CNS homogenates was used , but it was established that the lesion profile was identical in the mice inoculated with the peripheral tissue and the CNS . Although it could be hypothesized that the nature of the peripheral tissue inoculated would impact on the observed incubation length in mice ( matrix effect ) and consequently on the estimated infectious titre , Dickinson et al . [29] , [30] demonstrated that in conventional mice the dose-response obtained with the spleen and the brain from ME7 infected mice are similar . Together these elements indicate , that even if the peripheral tissues infectious titre reported are ‘estimates’ , they provide a good guide to the relative infectivity levels that are present in the Atypical scrapie/Nor98 and classical scrapie cases' brain and peripheral tissues . The presence of PrPSc and infectivity in small ruminant's peripheral tissues affected with natural classical scrapie or experimental BSE is well established [25] , [27] , [35] , [36] , [37] . It is generally considered that peripheral tissues like lymphoid tissues and striated muscle contain much lower levels of prion than CNS from terminally affected animals . This concept is the basis of the statutory measures aiming at limiting the entry of small ruminants TSE agents into the food chain . Tissues considered to be the most infectious ( named Specific Risk Material ) are systematically discarded from consumption , but tissues that would potentially contain only a low level of infectivity might enter the food chain due to the feasibility/practicality of removing them . In this study , the estimated infectivity level in skeletal muscle and lymphoid tissues from animals ( n = 4 ) affected with two different classical scrapie isolates did reach up to 1/10 ( weight/weight ) of the infectivity found in the CNS from terminally affected sheep . These values are higher than those expected from previous work . This could be explained by the fact that previously available data on prion quantities in peripheral tissues of small ruminants ( in particular those related to striated muscle ) relied on biochemical measurement of PrPSc amount [26] and the cell types accumulating PrPSc and the composition of these tissues may have impact on the PrPSc recovery yield . Also , if in some classical scrapie cases a 3–4 log10 infectivity difference was reported between CNS and some lymphoid tissues using bioassay in conventional mice , in other classical scrapie cases , the same study reported that infectivity in lymphoid tissue was only 1 to 10 fold lower than in CNS [27] . The classical scrapie cases that were investigated in this work cannot be assumed to be representative of all field diversity as only four animal cases of highly susceptible genotypes were used . However , the results indicate that exposure risk to such TSE agents through the unrestricted entry in the food chain of potentially infectious tissues would be significantly higher than previously thought . In most countries , the identification of Atypical/Nor98 scrapie was a consequence of the implementation of an active surveillance for TSE consisting in random testing for PrPSc presence in brainstem of a fraction of fallen or healthy culled small ruminants [10] . In Atypical/Nor98 scrapie cases , the sensitivity of PrPSc detection tests that are used for initial field screening or confirmation of TSE cases is debated . Several authors reported failure to detect PrPSc in some CNS areas like the obex area [5] , [6] , [20] from known affected animals or discrepancies in results when applying different diagnostic tests to a same sample [6] , [10] . The results obtained in this study by comparing the analytical sensitivity of biochemical PrPSc detection ( using an OIE registered WB method and a validated rapid screening test for TSE detection , in small ruminants ) and bioassay indicated that CNS samples that would contain up to 107 . 4 . –107 . 7 ID50/g of Atypical/Nor98 scrapie ( according to tg338 IC bioassay ) could remain negative for PrPSc detection . In field , Atypical/Nor98 scrapie cases ( Table 1 ) PrPSc positive WB was observed in CNS samples in which infectious titre was estimated ( on the basis of incubation period ) to be higher than 105 . 8 ID50/g IC in tg338 . Such discrepancies might reflect an individual variability of the PrPSc WB detection limits between atypical scrapie cases . It might alternatively be the consequence of a relative imprecision in estimating the titre of low infectious doses by the incubation period bioassay method . In contrast to Atypical/Nor98 scrapie cases , using two different classical agents the WB PrPSc detection sensitivity limit was about 102 ID50 IC in tg338 ( ie a tissue with a titre of 103 . 7 ID50/g IC in tg338 ) . These differences strongly support the contention that diagnostic assays based on PrPSc detection have lower performance for identifying Atypical/Nor98 scrapie cases than classical scrapie cases . It is consequently highly probable that a significant number of Atypical/Nor98 cases remain undetected by field testing , leading to an underestimation of Atypical/Nor98 scrapie prevalence in the small ruminant population . It is however not possible on the sole basis of this study to evaluate the importance of such underestimation . The under detection of Atypical/Nor98 scrapie in the field due to the sensitivity of the current PrPSc based approach would also impact on understanding of the biology of this TSE agent . While under natural conditions , classical scrapie is known to transmit between individuals , the analysis of data collected through the active TSE surveillance program seemed to indicate that Atypical/Nor98 scrapie could be poorly or not transmissible at all . This is based on the lack of statistical difference of the observed Atypical/Nor98 frequencies between the general population and the flocks where a positive case had been identified [38] , [39] . The lower ability to detect Atypical Scrapie incubating animals using the PrPSc based methodologies means that this conclusion should be considered with caution . Atypical/Nor98 cases are identified in older animals in comparison to classical scrapie [6] , [40] . The lack of PrPSc detection in peripheral tissues of reported cases suggested that Atypical/Nor98 scrapie agent could be restricted to CNS . This is supportive of the hypothesis that Atypical/Nor98 scrapie could be a spontaneous disorder of PrP folding and metabolism occurring in aged animals without external cause [6] , [38] . However , this hypothesis is questioned by the evidence reported here that a negative PrPSc testing result could be observed in animals harbouring high infectious titre in their brain and that the infectious agent can be present in peripheral tissues of Atypical/Nor98 scrapie incubating sheep . TSE are considered to be transmitted following oral exposure; initial uptake is followed by a peripheral replication phase which is generally associated with a dissemination of the agent in the lymphoid system and the deposition of large amounts of PrPSc . This peripheral replication phase is later followed by the entry of the infectious agent into the CNS through the autonomic nervous system [25] , [27] , [35] , [36] . However , in several situations , like BSE in cattle [41] , [42] , [43] or classical scrapie in ARR heterozygote sheep [44] , [45] , the involvement of secondary lymphoid system is marginal , which does not preclude central neuro-invasion through the autonomic nervous system [46] . It could be proposed that Atypical Scrapie/Nor98 might occur following oral exposure to a TSE agent , which would spread marginally in lymphoid tissues before neuro-invasion . The slow propagation of Atypical Scrapie/Nor98 in its host ( long incubation period ) and the impaired detection sensitivity level of PrPSc based assays would explain the apparent old age of detected cases . The results presented here are insufficient to rule out the hypothesis of a spontaneous/non contagious disorder or to consider this alternative scenario as a plausible hypothesis . Indeed , the presence of Atypical scrapie/Nor98 infectivity in peripheral tissues could be alternatively due to the centripetal spreading of the agent from the CNS . However , our findings point out that further clarifications on Atypical/Nor98 scrapie agent biology are needed before accepting that this TSE is a spontaneous and non contagious disorder of small ruminants . Assessing Atypical/Nor98 scrapie transmissibility through oral route in natural host and presence in placenta and in colostrum/milk ( which are considered as major sources for TSE transmission between small ruminants ) [28] , [32] will provide crucial data . The presence of infectivity in peripheral tissues that enter the food chain clearly indicates that the risk of dietary exposure to Atypical/Nor98 scrapie cannot be disregarded . However , according to our observations , in comparison to the brain , the infectious titres in the peripheral tissues were five log10 lower in Atypical/Nor98 scrapie than in classical scrapie . Therefore , the reduction of the relative exposure risk following SRM removal ( CNS , head , spleen and ileum ) is probably significantly higher in Atypical/Nor98 scrapie cases than in classical scrapie cases . However , considering the currently estimated prevalence of Atypical/Nor98 scrapie in healthy slaughtered EU population [10] , it is probable that atypical scrapie infectivity enters in the food chain despite the prevention measures in force . Finally , the capacity of Atypical/Nor98 scrapie agent ( and more generally of small ruminants TSE agents ) to cross species barrier that naturally limits the transmission risk is insufficiently documented . Recently , the transmission of an Atypical/Nor98 scrapie isolate was reported into transgenic mice over-expressing the porcine PrP [47] . Such results cannot directly be extrapolated to natural exposure conditions and natural hosts . However , they underline the urgent need for further investigations on the potential capacity of Atypical/Nor98 scrapie to propagate in other species than small ruminants . All animal experiments were performed in compliance with our institutional and national guidelines , in accordance with the European Community Council Directive 86/609/EEC . The experimental protocols were approved by the INRA Toulouse/ENVT and by the Norwegian ethics committees . The natural classical scrapie case ( case 10 ) included in this experiment was a Romanov sheep born and bred in the Langlade flock where a natural scrapie epidemic has been occurring at a high incidence since 1993 [11] . Natural atypical scrapie cases were identified though active or passive surveillance programs in France , Norway and Portugal ( Table 1 ) . The Portuguese cases were identified in three independent flocks where an atypical case had already been identified in the past ( additional cases ) . In all cases , PrP genotype was obtained by sequencing the Exon 3 of the Prnp gene as previously described [14] . In each case , the polymorphisms at codons 136 ( A/V ) , 154 ( H/R ) and 171 ( R/Q ) , which have been demonstrated to strongly influence the susceptibility to TSE in sheep are indicated [48] . Additionally the presence of a phenylalanine at codon 141 ( F/L ) , which has been shown to impact on the susceptibility to atypical/Nor98 scrapie , was indicated [14] , [49] . Two sheep ( one 12 months old AFRQ/ARQ ( case 8 ) and one 14 months old AHQ/AHQ ( case 9 ) ) selected in a field flock were IC challenged with French AFRQ/AFRQ Atypical Scrapie ( case 1 ) ( Table 2 ) . The animals were euthanized when showing clear clinical signs at respectively 2224 and 964 days post inoculation . TSE-free Poll-Dorset sheep ( VLA- Weybridge- UK ) were used for intracerebral inoculation with Langlade isolate ( case 10 , inoculum derived from a VRQ/VRQ natural isolate ) , or PG127 isolate ( inoculum derived from a VRQ/VRQ experimental case ) . Animals were killed when displaying evident clinical signs at respectively 380 days and 160 days post inoculation . Oral challenge was performed in 6–10 months old TSE-free New Zealand cheviot sheep . Animals were dosed with 5 g equivalent of brain material ( 1% brain homogenate in glucose ) derived from an experimentally VRQ/VRQ affected sheep ( PG127 isolate ) . Animals were culled at clinical stage of the disease ( 200 days post inoculation ) . All tissues were collected using disposable equipment ( forceps and scalpels ) . The different field and experimental cases were sampled on different dates and/or places . Different instrument sets and containers were used for collecting , transporting and storing each sample . Finally , in all cases , peripheral tissues were collected before CNS to further reduce the risk of cross contamination . In natural Atypical/Nor98 cases , the nature of the tissues collected under TSE sterile conditions might have varied according to the country and date of collection . In all cases , CNS and at least one lymph node were available . In both Atypical/Nor98 and classical scrapie experimental cases , a large panel of tissues ( including Central Nervous System , Peripheral Nervous System , digestive tract wall , muscle ) was collected under TSE sterile conditions . From the available samples , tissues homogenates ( 20% stock material ) were prepared in Norway ( Norwegian cases ) or in France ( French natural and experimental cases and Portuguese cases ) . The list of processed samples is given in Tables 1 and 2 . In each case disposable equipment was used to manipulate the tissues . 20% tissues homogenates were prepared using single use grinding microtubes ( Precess 48 - BioRad ) and filtered through a 25 gauge needle ( single use syringe ) . The tissue homogenates were then aliquoted ( in 2 ml and 5 ml tubes ) and stored at −80°C . Peripheral tissues homogenates and CNS homogenates were prepared separately . This method was performed as previously described [50] . PrPSc IHC detection was first performed using 8G8 antibody raised against human recombinant PrP protein and specifically recognising the 95–108 amino acid sequence ( SQWNKP ) of the PrP protein . For each sample a negative serum control was included , in which the primary antibody was either omitted or replaced by purified mouse IgG2a serum . An OIE registered Western blot kit ( TeSeE Western Blot , BioRad ) was used following the manufacturer's recommendations . For each sample , 250 µl of 10% brain homogenate were submitted to PrPSc extraction . The obtained pellet was denaturated in Laemmli's buffer ( 15 µl ) before being loaded neat or diluted ( Figure 4 ) on a 12% acrylamide gel , and submitted to electrophoresis and blotting . Immunodetection was performed using SHa31 which recognizes the 145–152 sequence of PrP ( YEDRYYRE ) . Peroxidase activity was revealed using ECL substrate ( Pierce ) [26] . A commercially available TSE detection test ( TeSeE Sheep and Goat - BioRad ) was used according to manufacturer's recommendations . In summary , five hundred µL of the 20% homogenate were incubated for 10 min at 37°C with 500 µL of buffer A containing proteinase K . PrPsc was recovered as a pellet after addition of 500 µL of buffer B and centrifugation for 5 min at 20 000 g at room temperature . Supernatant was discarded and tubes dried . Finally , the pellet was denatured in buffer C ( 5 min at 100°C ) and 1:6 diluted in R6 reagent before distribution into the wells [28] , [51] . PET blots were performed using a method previously described [52] , [53] . Immunodetection was carried out using SHa31 monoclonal antibody ( 4 µg/mL ) , followed by application of an alkaline phosphatase labeled secondary antibody ( Dako reference D0314 – 1/500 diluted ) . Enzymatic activity was revealed using NBT/BCIP substrate chromogen . Bioassay experiments were carried out in ovine VRQ PrP transgenic mice ( tg338 ) , which are considered to be highly efficient for the detection of sheep scrapie infectivity [54] . At least six mice were intra-cerebrally inoculated with each sample ( 20 µL ) . Prior to inoculation , homogenates were diluted ( final concentration 10% or 12 . 5% ) in 5% glucose sterile solution . Each homogenate was then tested for bacteria presence ( blood gelose overnight 37°C culture ) and non sterile homogenates were submitted to a heat treatment ( 60°C – 10 min ) . Heat treated samples are identified in Table 1 . The impact of such heat treatment on atypical scrapie infectivity is currently unknown . Portuguese and French cases' inoculations were carried out in UMR INRA ENVT 1225 ( Toulouse , France ) facilities while Norwegian cases were inoculated at the NVI ( Oslo , Norway ) . Peripheral tissues and CNS homogenates were inoculated on different days in order to avoid any risk of cross contamination . In some cases , tissues autolysis resulted in the death of some animals inoculated which explain the low number of mice for some isolates . Mice were monitored daily until the occurrence clinical signs of TSE . Mice were culled when they started to show locomotor disorders and any impairment in their capacity to feed . CNS samples were individually collected . A part of the brain ( cerebral cortex ) was frozen for PrPSc Western blot testing ( TeSeE WB kit- BioRad ) and the other part of the brain was formalin fixed for vacuolar brain lesion profiling [55] and PrPSc PET-Blotting . Five different isolates were endpoint titrated in tg338 mice , by inoculating intra-cerebrally ( 20 µl ) successive 1/10 dilutions of CNS homogenate in groups of tg338 mice ( 6 or 12 mice ) . The material used for the titration was 10% brain homogenate except for the Langlade isolate ( 12 . 5% homogenate ) . Two classical scrapie inocula used for sheep inoculation ( Langlade: case 10 , posterior brain stem - PG127: case 12 , posterior brain stem ) were titrated in UMR INRA ENVT 1225 . The titration of the Langlade material was already published in a previous study [32] . Two confirmed atypical scrapie isolates ( one from an ARQ/ARQ Norwegian sheep: case 14 , cerebellum and one from a French ARR/ARR sheep: case 15 , cerebellum ) were titrated in INRA Jouy-en-Josas . These isolates correspond to two atypical cases originally described in the Le Dur et al . study in which they were respectively identified as Lindos and DS8 [7] . Three additional atypical scrapie isolates ( AFRQ/AFRQ: case 1 , cerebral cortex- AHQ/AHQ: case 9 , cerebral cortex– AFRQ/ARQ: case 8 , cerebellum ) were titrated in UMR INRA ENVT 1225 . The infectious titre ( Infectious Dose 50 ) of the brain homogenates was determined by the Spearman-Kärber's method [56] . For each isolate , the incubation periods recorded in individual tg338 and the number of ID50 inoculated to each mice ( number of ID50 per 20 µL of the inoculated homogenate ) ( derived from Table 3 ) were plotted on a graph . On the basis of this data a four parameter logistic regression function was computed ( Sigmaplot ) . This function was then used to estimate the infectious titre ( number of Infectious Dose 50 ) contained in tissue samples on the basis of the incubation period observed in tg338 mice [29] , [30] , [31] , [32] , [33] , [34] . CNS homogenate dilutions series from three different Atypical/Nor98 scrapie cases ( case 1: cerebral cortex – case 8: cerebellum – case 9: cerebral cortex ) were prepared by successive dilutions in negative brain homogenate . The prepared dilutions were: 1/2 , 1/5 , 1/10 , 1/20 , 1/40 , 1/80 , 1/100 , 1/200 , 1/400 , 1/800 , 10−3 , 10−4 , 10−5 , 10−6 , 10−7 , 10−8 . The dilutions series were tested for PrPSc using TeSeE Sheep and Goat ELISA test and the WB as previously described in the text . The neat sample and 10−5 to 10−7 dilutions from the same series were inoculated in groups of 6 tg338 in order to assess the infectious titre ( see paragraph: Reference Central Nervous System samples endpoint titration ) . Dilutions series of CNS homogenates were prepared from the Langlade scrapie ( case 10: posterior brainstem ) and PG127 ( case 12: posterior brainstem ) homogenates that were endpoint titrated in tg338 mice ( Table 3 ) . For these dilutions an aliquot of the 20% stock homogenate ( stored at −80°C ) was used as starting material . The dilution series ( neat , 10−1 , 10−2 , 10−3 , 10−4 , 10−5 , 10−6 , 10−7 ) were then tested by WB ( TeSeE WB kit – BioRad ) .
Following the bovine spongiform encephalopathy ( BSE ) crisis and the identification of its zoonotic properties , a sanitary policy has been implemented based on both eradication of transmissible spongiform encephalopathies ( TSE ) in food-producing animals and exclusion of known infectious materials from the food chain . Atypical/Nor98 scrapie is a prion disease of small ruminants identified worldwide . Currently it represents a significant part of the TSE cases detected in Europe . The restricted tissue distribution of Atypical/Nor98 scrapie agent in its natural host and the low detected prevalence of secondary cases in affected flocks meant that it is believed to be a poorly transmissible disease . This has led to the view that Atypical/Nor98 scrapie is a spontaneous disorder for which human and animal exposure risk remains low . In this study we demonstrate that in affected individuals , Atypical/Nor98 scrapie agent can disseminate in lymphoid tissues , nerves , and muscles , challenging the idea that it is a brain-restricted infectious agent . Evidence for the deficiencies in the current methods applied for monitoring Atypical/Nor98 scrapie is provided that would indicate an underestimation in the prevalence in the general population and in the affected flocks . These elements challenge the hypothesis on the biology of this recently identified TSE agent .
You are an expert at summarizing long articles. Proceed to summarize the following text: Neural populations encode information about their stimulus in a collective fashion , by joint activity patterns of spiking and silence . A full account of this mapping from stimulus to neural activity is given by the conditional probability distribution over neural codewords given the sensory input . For large populations , direct sampling of these distributions is impossible , and so we must rely on constructing appropriate models . We show here that in a population of 100 retinal ganglion cells in the salamander retina responding to temporal white-noise stimuli , dependencies between cells play an important encoding role . We introduce the stimulus-dependent maximum entropy ( SDME ) model—a minimal extension of the canonical linear-nonlinear model of a single neuron , to a pairwise-coupled neural population . We find that the SDME model gives a more accurate account of single cell responses and in particular significantly outperforms uncoupled models in reproducing the distributions of population codewords emitted in response to a stimulus . We show how the SDME model , in conjunction with static maximum entropy models of population vocabulary , can be used to estimate information-theoretic quantities like average surprise and information transmission in a neural population . Neurons represent and transmit information using temporal sequences of short stereotyped bursts of electrical activity , or spikes [1] . Much of what we know about this encoding has been learned by studying the mapping between stimuli and responses at the level of single neurons , and building detailed models of what stimulus features drive a single neuron to spike [2]–[4] . In most of the nervous system , however , information is represented by joint activity patterns of spiking and silence over populations of cells . In a sensory context , these patterns can be thought of as codewords that convey information about external stimuli to the central nervous system . One of the challenges of neuroscience is to understand the neural codebook—a map from the stimuli to the neural codewords—a task made difficult by the fact that neurons respond to the stimulus neither deterministically nor independently . The structure of correlations among the neurons determines the organization of the code , that is , how different stimuli are represented by the population activity [5]–[8] . These correlations also determine what the brain , having no access to the stimulus apart from the spikes coming from the sensory periphery , can learn about the outside world [9]–[11] . The source of these correlations , which arise either from the correlated external stimuli to the neurons , from “shared” local input from other neurons , or from “private” independent noise , has been heavily debated [12]–[15] . In many neural systems , the correlation between pairs of ( even nearby or functionally similar ) neurons was found to be weak [16]–[18] . Similarly , the redundancy between pairs in terms of the information they convey about their stimuli was also typically weak [19]–[21] . The low correlations and redundancies between pairs of neurons therefore led to the suggestion that neurons in larger populations might encode information independently [22] , which was echoed by theoretical ideas of maximally efficient neural codes [23]–[25] . Recent studies of the neural code in large populations have , however , revealed that while the typical pairwise correlations may be weak , larger populations of neurons can nevertheless be strongly correlated as a whole [18] , [26]–[33] . Maximum entropy models of neural populations have shown that such strong network correlations can be the result of collective effects of pairwise dependencies between cells , and , in some cases , of sparse high-order dependencies [18] , [34]–[36] . Most of these studies have characterized the strength of network effects and spiking synchrony at the level of the total vocabulary of the population , i . e . the distribution of codewords averaged over all the stimuli . It is not immediately clear how these findings affect stimulus encoding , where one needs to distinguish the impact of correlated stimuli that the cells receive ( “stimulus correlations” ) , from the impact of co-variance of the cells conditional on the stimulus ( “noise correlations” ) . For small populations of neurons , it has been shown that taking into account correlations for decoding or reconstructing the stimulus can be beneficial compared to the case where correlations are neglected ( e . g . [35] , [37]–[40] ) . Similarly , generalized linear models highlighted the importance of dependencies between cells in accounting for correlations between pairs and triplets of retinal ganglion cell responses [41] . Here we present a new encoding model that allows us to study in fine detail the codebook of a large neural population . We define the codewords to be the joint activity patterns of the population in time windows whose duration reflects the typical width of the cross-correlation of spiking between pairs of neurons . Importantly , this model gives a joint probability distribution over the activity patterns of the whole population for a given stimulus , while capturing both the stimulus and noise correlations . This new model belongs to a class of maximum entropy models with strong links to statistical physics [27] , [42]–[53] and is directly related to maximum entropy models of neural vocabulary [18] , [27]–[32] , allowing us to estimate the entropy and its derivative quantities for the neural code . In sum , the maximum entropy framework enables us to progress towards our goal of focusing attention on the level of joint patterns of activity , rather than capturing low-level statistics ( e . g . , the individual firing rates ) of the neural code alone . We start by showing that linear-nonlinear ( LN ) models of retinal ganglion cells responding to spatially unstructured stimuli capture a significant part of the single neuron response , but still miss much of the detail; in particular , we show that they fail to capture the correlation structure of firing among the cells . We next present our new stimulus-dependent maximum entropy ( SDME ) model , which is a hybrid between linear-nonlinear models for single cells and the pairwise maximum entropy models . Applied to groups of neurons recorded simultaneously , we find that SDME models outperform the LN models for the stimulus-response mapping of single cells and , crucially , give a significantly better account of the distribution of codewords in the neural population . Using repeated presentations of the same movie , we estimated the average response of each of the cells across repeats , , or the peri-stimulus time histogram ( PSTH ) . Following Refs . [4] , [55] , we fitted a linear-nonlinear model for each of the cells in the experiment , so that the resulting model for the population as a whole is a set of uncoupled , conditionally independent LN neurons that we denote together as a ‘S1’ model ( the reason for this notation will be explained later ) . The predicted rate of every neuron is then , where is a linear filter matched for the -th cell , is its point-wise nonlinear function , and is the stimulus fragment from time until ( here we used , making a vector of light intensities with 40 components ) . Linear filters were reconstructed using reverse correlation ( spike-triggered average ) , and nonlinearities were obtained by histograming into adaptively-sized bins and obtaining by inverting using Bayes' rule . These LN models captured most of structure of the PSTH , yet as the example cell in Fig . 2a shows , they often misestimated the exact firing rates of the neuron , or sometimes even missed parts of the neural response altogether . For the Gaussian FFF , the normalized ( Pearson ) correlation between the measured and predicted PSTH , , was ( mean std across 100 cells ) . The performance gap of the canonical LN models in predicting single neuron responses suggests that either the single-neuron models need to be improved to account for the observed behavior , or that interactions between neurons play an important encoding role and need to be included . Clearly , the firing rate prediction performance can be improved for single neurons by models with higher-dimensional stimulus sensitivity ( e . g . [55] , [56] ) or dynamical aspects of spiking behavior ( e . g . [57] , [58] ) . However , previous work ( and results below ) demonstrated that even conditionally-independent models which by construction perfectly reproduce the firing rate behavior of single cells , often fail to capture the measured correlation structure of firing between pairs of cells , as well as higher-order statistical structure [18] . We therefore sought a model of the neural code that would be able to reproduce the correlation structure of population codes . We asked whether a model that combined the LN ( receptive-field based ) aspect of single cells with the interactions between cells , could give a better account of the neural stimulus-response mapping . Importantly , the new model should capture not only the firing rate of single cells but also accurately predict the full distribution of the joint activity patterns across the whole population . Because the joint distributions of activity are high-dimensional ( e . g . , the distribution over codewords across the duration of the experiment , , has components ) , this is a very demanding benchmark for any model . We propose the simplest extension to the conditionally-independent set of LN models for each cell in the recorded population , by including pairwise couplings between cells , so that the spiking of cell can increase or decrease the probability of spiking for cell [59] , [60] . Importantly , in contrast to previous models , we introduce this coupling so that the resulting model is a maximum-entropy model for , the conditional distribution over population activity patterns given the stimulus . We recall that the maximum entropy models give the most parsimonious probabilistic description of the joint activity patterns , which perfectly reproduces a chosen set of measured statistics over these patterns , without making any additional assumptions [61] . Specifically , we construct a model that relies only on the measured overall correlations between pairs of neurons , which can be reliably estimated from experimental data ( see Methods ) . We find that ( i ) the pairwise correlations between cells in response to the Gaussian FFF movie are typically weak but significantly different from zero ( Fig . 1c , consistent with previous reports [18] , [27] , [32] ) ; ( ii ) the correlation in neural activities shows a fast decay with distance despite the infinite correlation length of the stimulus , but the decay does not reach zero correlation even at relatively large distances ( Fig . 1d ) . This salient structure , along with any other potential statistical correlation at the pairwise order , is characterized by the covariance matrix of activities , , where the averages are taken across time and repeats . We start by introducing the least structured ( maximum entropy ) distribution of the population responses to stimuli , by treating each time point along the stimulus separately; since every moment of time maps uniquely into one stimulus , we start by building the model of the response given time . We thus find that reproduces exactly the observed average firing rate for each time bin in the stimulus and for each neuron , , as well as the overall covariance matrix between all pairs of cells ( c . f . [62] ) . Thus , we seek that maximizes : ( 1 ) where the subscript to brackets denotes whether the averaging is done over the maximum entropy distribution ( ) , or over the recorded data; Lagrange multipliers ensure that the distributions are normalized . This is an optimization problem for parameters and , which has a unique solution since the entropy is convex . The functional form of the solution to this optimization problem is well-known and in our case it can be written as ( 2 ) where the individual time-dependent parameters for each of the cells , , and the stimulus-independent pairwise interaction terms , are set to match the measured firing rates and the pairwise correlations ; is a normalization factor or partition function for each time bin , given by . The pairwise time-dependent maximum entropy ( pairwise TDME or T2 ) model in Eq . ( 2 ) is equivalent to an Ising model from physics , where the single-cell parameters are time-dependent local fields acting on each of the neurons ( spins ) , and static ( stimulus-independent ) infinite-range interaction terms couple each pair of spins . In the limit where interactions go to zero , , the model in Eq . ( 2 ) becomes the full conditionally-independent model , itself a first-order time-dependent maximum entropy model that reproduces exactly the firing rate of every neuron , : ( 3 ) In this case the probability distribution factorizes , and the solution for and becomes trivially computable from the firing rates , . For time bins that are short enough to contain 0 or 1 spike ( as we have assumed throughout ) , is given by . Consistent with our previous notation , we denote this full conditionally-independent model as T1 . Time-dependent maximum entropy models are powerful , since they make no assumption about how the stimulus drives the response; they often serve as useful benchmarks for other models ( especially the T1 model ) . On the other hand , these models require repeated stimulus presentations to fit , involve a number of parameters that grows linearly with the duration of the stimulus , do not generalize to new stimuli , and do not provide an explicit map from the stimuli to the responses . We therefore present a more particular form of the model of Eq . ( 2 ) that , ( i ) , would give an explicit description of stimulus-dependent distribution of population patterns; ( ii ) , would generalize to new stimuli; ( iii ) , could be directly compared to the uncoupled LN models; and ( iv ) , would not require repeats of the same stimulus to fit . Specifically , rather than having an arbitrary time-dependent parameter for every neuron for each time bin , , we assume that this dependence takes place through the stimulus projection alone , i . e . . This is analogous to an LN model , where the neural firing depends on the value of the stimulus projection onto the linear filter . This choice is made for simplicity; this model can be generalized to , e . g . , neurons that depend on two linear projections of the stimulus , by making depend jointly on , although such models would be progressively more difficult to infer from data . Concretely , we estimated the linear filter for each cell using reverse correlation , and convolved the filter with the stimulus sequence , , to get the “generator signal” . We then looked for the maximum entropy probability distribution , by requiring that the average firing rate of every cell given the generator signal is the same in the data and under the model , i . e . ( see Methods ) ; as before , we also required the model to reproduce the overall covariance between all pairs of cells , . This yields a pairwise stimulus-dependent maximum entropy ( pairwise SDME or S2 ) model , which takes the following form: ( 4 ) The parameters of this model are: couplings , parameters , and a linear filter for each cell; these parameters define the energy function of the model . We used a Monte Carlo based gradient descent learning procedure to find the model parameters numerically ( see Methods; note that the problem is still convex with a single solution for the parameter values ) . By construction , the S2 model exactly reproduces the covariance of activities , , between all pairs of cells , and also the LN model properties of every cell: an arbitrary nonlinear function can be encoded by properly choosing how parameters depend on the linear projections of the stimulus , . We can construct a maximum entropy model with ( no constraints on the pairwise correlations ) . The result is a set of uncoupled ( conditionally independent ) LN models: ( 5 ) Fig . 3 shows all the models in a systematic way: the pairwise time-dependent maximum entropy ( T2 ) model of Eq . ( 2 ) is an extension of conditionally independent ( T1 ) model that additionally reproduces the measured pairwise correlations between cells . In a directly analogous way , the pairwise stimulus-dependent maximum entropy ( S2 ) model of Eq . ( 4 ) is an extension to the set of uncoupled LN models ( S1 ) , Eq . ( 5 ) , that additionally reproduces the measured pairwise correlations between cells . Because ( Eq . 4 ) agrees with ( Eq . 5 ) exactly in all constrained single-neuron statistics , any improvement in prediction of the S2 model , be it in the firing rate or the codeword distributions , can be directly ascribed to the effect of the interaction terms , . An alternative approach to describing the joint response of large populations of neurons to external stimuli has been presented in Ref . [41] . The Generalized Linear Model ( GLM ) gives a generative model from which one can sample simulated responses to new stimuli , relying on activity history and temporal dependencies between cells , but assuming conditional independence within any given time bin . We compare the advantages of the two approaches in the Discussion below , but briefly emphasize here that a key difference is that GLM does not present an explicit probability distribution over codewords ( that are defined for temporal bins significantly longer than those of the GLMs ) , which is central for the analysis of the neural code we present below . To assess the accuracy of different stimulus-dependent models , and , in particular , of the contribution of the interactions between cells , we fitted and quantified the performance of the uncoupled LN models ( S1 ) and the pairwise SDME model ( S2 ) . At the level of single neurons , we found that the S2 model predicted the firing rates better than the S1 model ( see e . g . Fig . 2a ) , with the normalized correlation coefficient between the true and predicted firing rate , reaching ( mean std across 100 cells ) , as shown in Fig . 2b . The differences between the S2 and the S1 models become more striking at the level of the activity patterns of the whole population . Figs . 4a , b show the complex structure of the population activity patterns across all 626 repeats at a particular moment in time . During times when the population is active , it generates a wide diversity of patterns in response to the same stimulus; even with hundreds of repeats , these distributions cannot be empirically sampled . Nevertheless , the large number of repeats suffices to identify and estimate reliable low-order marginals of these distributions , in particular , the correlations between the pairs of neurons at various points in time . The wide range of magnitudes of these reliably estimated correlations shows that a number of neuronal pairs are far from conditionally independent . As shown in Fig . 4c , the S2 model captures a significant fraction of this correlation structure on a timebin-by-timebin basis ( on test data ) ; clearly , the S1 model fails at this task . We found that S2 is orders of magnitude better in predicting the population neural responses to stimuli . This is quantified in Fig . 4d , which compares S1 and S2 through the log-likelihood ratio , , for the population activity patterns under the two models . These differences are large in particular for those stimuli that elicit a strong response , that is , precisely where the response consists of synchronous spiking and the structure of the codewords can be nontrivial . Fig . 5 summarizes these results by showing the average log-likelihood ratio over all testing repeats , emphasizing that the difference between the models becomes particularly apparent for groups of more than 20 cells . We next examined how well various models of the neural codebook , , explain the total vocabulary , that is , the distribution of neural codewords observed across the whole duration of the experiment , . Despite the nominally large space of possible codewords—much larger than the total number of samples in the experiment ( ) —the sparsity of spikes and the correlations between neurons restrict the vocabulary to a much smaller set of patterns . Some of these occur many times during our stimulus presentation , allowing us to estimate their empirical probability , , directly from the experiment , and compare it to the model prediction [35] . The most prominent example of such frequently observed codewords is the silent pattern , , which is seen of the time . Fig . 6 shows the likelihood ratio of the model probability and empirical probability for various codewords observed in the test part of the experiment , as a function of the rate at which these codewords appear . Here we used an additional model for comparison , i . e . , the full conditionally-independent model ( T1 ) , where every cell is described in terms of time-dependent firing rate . The S2 model in Fig . 6a strongly outperforms the S1 model in Fig . 6b , and has a slightly better performance than the T1 model ( Fig . 6c ) , despite the fact that the latter is determined by parameters , the firing rates of every cell in every time bin . Quantitatively , the per-codeword log-likelihood of the test data under S1 model is 5 . 30 , under T1 model 4 . 34 , under S2 model 4 . 12 , under empirically sampled distribution on the training set 4 . 02 , while the lower bound on the log-likelihood ( obtained when the “model” are the true empirical frequencies on the test set ) is 2 . 98 ( see Methods ) . On average , S2 predicts the probabilities of the patterns of activity with minimal bias , and with a standard deviation of of about 1; the S1 model in comparison is biased and has a spread that is more than twice as large . Even more striking is the fact that S1 assigns very low probabilities to some codewords such that they were never generated during our Monte Carlo sampling ( and are therefore not even shown in scatterplots of Fig . 6 ) , although they were frequently observed in the experiment . This discrepancy is quantified by enumerating the most probable patterns in the data and in the model ( by sampling , see Methods ) , and measuring the size of the intersection of the two sets of patterns . In other words , we ask if the model is even able to access all the patterns that one is likely to record in the experiment . As shown in the bottom of Fig . 6 , S2 does well on this task , with 419 codewords in the intersection of the most likely patterns in the data and the model . This is a much better performance than the S1 model , and a little better than for the T1 model ( which has many more parameters ) . We emphasize that all these comparisons were done on test data only , so that the models had to generalize over the large diversity of patterns where some of the patterns seen in the training set might never occur on the testing set and vice versa ( see Fig . 4a , b ) . The S2 model was constructed to capture exactly the total pairwise correlation in neuronal spiking , . With repeated stimulus , this total correlation can be broken down into the signal and noise components . The signal correlations , , are inferred by applying the same formula as for the total correlation , but on the spiking raster where the repeated trial indices have been randomly and independently permuted for each time bin . This removes any correlation due to interactions between spikes on simultaneously recorded trials , and only leaves the correlations induced by the response being locked to the stimulus . The noise correlation , , is then defined as the difference between the total and the signal components , . We calculated the noise correlations between all pairs in our neuron dataset . By their definition , the conditionally independent models cannot reproduce , which are always zero for those models . To assess the performance of the S2 model , we drew samples from our model distribution using a Monte Carlo simulation and compared the noise correlations in the simulated rasters to the true noise correlations . The model prediction is tightly correlated with the measured values , as shown in Fig . 7 . We observe a systematic deviation of , most likely because the assumed dependence on the stimulus through one linear filter per neuron is insufficient to capture the complete dependence on stimulus , thereby underestimating the full structure of stimulus correlation and inducing an excess in the noise correlation . Despite this , the degree of correspondence in noise correlations observed in Fig . 7 is telling us that the S2 model has clearly captured a large amount of noise covariance structure in neural firing at the network level . How should we interpret the inferred parameters of the S2 model ? LN models have a clear mechanistic interpretation in terms of the cell's receptive field and the nonlinear spiking mechanism . Here , similarly , the stimulus dependent part of the model for each cell , , is a nonlinear function of a filtered version of the stimulus ; in the absence of neuron-to-neuron couplings , the nonlinearity of every neuron would correspond to , where , according to Eq . ( 5 ) . The dependence of on the stimulus projection is similar across the recorded cells as shown in Fig . 8a; as expected , higher overlaps with the linear filter induce higher probability of spiking . The pairwise interaction terms in the S2 model , , are symmetric , static , and stimulus independent by construction . As such , they represent only functional and not physical ( i . e . synaptic ) connections between the cells . Fig . 8b shows the pairwise interaction map for 100 cells; the histogram of their values ( in Fig . 8c ) reflects that they can be of both signs , but the distribution has a stronger positive tail , i . e . a number of cell pairs tend to spike together or be silent together with a probability that is higher than expected from their respective LN models . We can compare these interactions to the interactions of a static ( non-stimulus-dependent ) pairwise maximum entropy model for the population vocabulary [18] , [28]: ( 6 ) In this model for the total distribution of codewords , there is no stimulus dependence , and the parameters and are chosen so that the distribution is as random as possible , while reproducing exactly the measured mean firing rate of every neuron , and every pairwise correlation , , across the whole duration of the experiment . Interestingly , we find that the pairwise interaction terms in the S2 model of Eq . ( 4 ) are closely related to the interactions in the static pairwise maximum entropy model of Eq . ( 6 ) : S2 interactions , , tend to be smaller in magnitude , but have an equal sign and relative ordering , as the static ME interactions , . Some degree of correspondence is expected: an interaction between neurons and in the static ME model captures the combined effect of the stimulus and noise correlations , while in the corresponding S2 interaction , ( most of ) the stimulus correlation has been factored out into the correlated dynamics of the inputs to the neurons and , i . e . and . The surprisingly high degree of correspondence , however , indicates that even the interactions learned from static maximum entropy models can account for , up to a scaling factor , the pairwise neuron dependencies that are not due to the correlated stimulus inputs . Figs . 4a , b show the richness of activity patterns produced in response to repeats of the same stimulus . While these patterns must encode the same information , it is not clear how this could be established by looking at the patterns alone ( without prior knowledge that they were generated in response to the same stimulus ) , because of the high dimensionality of the pattern space . Is there a way to simplify this response space ? We suggest one such approach here , motivated by the analogy to Ising models in statistical physics and the related similarities with the Hopfield networks [27] , [32] , [62] , [63] . At every instant in time , the probability of any activity pattern in the S2 model is fully specified by the distribution with an exponential form given by Eq . ( 4 ) . In analogy to statistical physics , the exponent is the ( negative ) energy of the state . This energy function defines an instantaneous “energy landscape” over the space of all possible activity patterns . Minima in this landscape can be viewed as metastable patterns or attractors , and all activity patterns can be assigned to their respective attractors by descending on the energy landscape until the closest local minimum is reached , much like in the Hopfield network . In this way , the space of patterns is partitioned , at each point in time , into a number of domains centered on the metastable states . How useful is this representation of the response space ? Using the S2 model fit on training repeats , we examined neural responses in every time bin across all testing repeats . We assigned each response pattern from testing data to its corresponding metastable state . Fig . 9a shows , as a function of time , all identified metastable states , their energies ( i . e . the negative log probability of that state ) , and the number of repeats on which a pattern belonging to that state was emitted . This analysis still paints a rich , but already much simplified picture of the retinal responses , where many patterns are grouped into a small number of clusters centered on the metastable states . Interestingly , these assignments generalize very well: in Fig . 9b we independently identify the metastable states on testing and training sets for each time bin , assign all patterns seen in the experiment to these states , and count and compare how many times each state appears on testing and training repeats . Virtually all ( ) metastable states appearing in training repeats are found on testing repeats and vice versa , and this intersection is vastly larger than the intersection of the activity patterns themselves , a lot of which can appear only once in all 626 repeats . Moreover , the frequency with which patterns belonging to a particular metastable state occur is reproducible between the training and test data , suggesting that the partitioning of the high-dimensional activity space into clusters defined by the energy function of the S2 model is a productive dimensionality reduction method in this context . The S2 model is an approximation to the neural codebook , , while the static ME model describes the population vocabulary , . With these two distributions in hand , we can explore how the population jointly encodes the information about the stimulus into neural codewords—the joint activity patterns of spiking and silence . We make use of the fact that we can estimate the entropy of the maximum entropy distributions using a procedure of heat capacity integration , as explained in Refs . [27] , [32] ( see Methods ) . The information ( in bits ) that the codewords carry about the stimulus is then ( 7 ) that is , the information can be written as a difference of the entropy of the neural vocabulary , and the noise entropy ( the average of the entropy of the codebook ) , where the entropy is . Because of the maximum entropy property of our model for , the entropy of our static pairwise model in Eq . ( 6 ) is an upper bound on the transmitted information; expressed as an entropy rate , this amounts to . The brain does not have direct access to the stimulus , but only receives codewords , drawn from , by the retina . It is therefore useful to estimate for every moment in time , the surprise about the output of the retina , and thus about the stimulus , which is given by . We , as experimenters—but not the brain—have access to stimulus repeats and thus to , so we can compute the average value of surprise ( per unit time ) at every instant in the stimulus: ( 8 ) This quantity can be expressed using the entropies and the learned parameters of our maximum entropy models , and is plotted as a function of time in Fig . 10 . Since averaging across time is equal to averaging over the stimulus ensemble , we see from Eq . ( 8 ) that would have to be identically equal to under the condition that ( marginalization ) . Since we build models for ( static ME ) and ( S2 ) from data independently , they need not obey the marginalization condition exactly , but they will do so if they provide a good account of the data . Indeed , by using the static ME and S2 distributions in Eq . ( 8 ) for surprise , we find that , very close to the entropy rate of the total vocabulary and within the estimated error bars of the entropy , which are 1% . To estimate the information transmission , we have to subtract the noise entropy rate from the output entropy rate , as dictated by Eq . ( 7 ) . The entropy of the S2 model is an upper bound on the noise entropy; since this is not a lower bound , we cannot put a strict bound on the information transmission , but can nevertheless estimate it . Fig . 10 shows the “instantaneous information” [64] , , as a function of time; from Eq . ( 7 ) , the mutual information rate is a time average of this quantity , . We find . This quantity can be compared to the total entropy rate of the stimulus itself ( which must be higher than ) , which in our case is ( see Methods ) . While our estimates seem to indicate that a lot of vocabulary bandwidth ( 730 bit/s ) is “lost” to noise ( 600 bit/s ) , the last comparison shows that the Gaussian FFF stimulus source itself is not very rich , so that the estimated information transmission takes up more than half of the actual entropy rate of the source . Lastly , we asked how important is the inclusion of pairwise interactions , , into the S2 model , compared to the S1 model , when accounting for information transmission . We therefore estimated the noise entropy rate for the S1 model , , which was found to be , considerably higher than the noise entropy of the S2 model . Crucially , this noise entropy rate is larger than the total entropy rate estimated above , which is impossible for consistent models of the neural codebook and the vocabulary ( since it would lead to negative information rates ) . This failure is a quantitative demonstration of the inability of the uncoupled LN models to reproduce the statistics of the population vocabulary , as shown in Fig . 6b , despite a seemingly small performance difference on the level of single cell PSTH prediction . We presented a modeling framework for stimulus encoding by large populations of neurons , which combines an individual neuronal receptive field model , with the ability to include pairwise interactions between neurons . The result is a stimulus-dependent pairwise maximum entropy ( S2 ) model , which is the most parsimonious model of the population response to the stimulus that reproduces the linear-nonlinear ( LN ) aspect of single cells , as well as the pairwise correlation structure between neurons . In two limiting cases , the S2 model reduces to known models: if the single cell parameters are static , S2 becomes the static pairwise maximum entropy model of the population vocabulary; if the couplings are 0 , S2 reduces to S1 , the set of uncoupled LN models . We applied this modeling framework to the salamander retina presented with Gaussian white noise stimuli , and found that the interactions between neurons play an important role in determining the detailed patterns of population response . In particular , the S2 model gave better prediction of PSTH of single cells , yielded orders-of-magnitude improvement in describing the population patterns , and captured significant aspects of noise correlations . The deviations between the S2 and the S1 model became significant for cells , and tended to occur at “interesting” times in the stimulus , precisely when the neural population was not silent . The S2 model allowed us to improve over LN models for salamander retinal ganglion cells in terms of the PSTH prediction of single cells . But , more importantly , it gave a huge improvement in terms of describing and predicting the population activity patterns , or codewords . Interestingly , for parasol cells in the macaque retina under flickering checkerboard stimulation , the generalized linear model did not yield firing rate improvement relative to uncoupled LN models ( but did improve the prediction of higher order statistics of neural activity ) [41] . In both cases , however , the improvements reflect the role of dependencies among cells in encoding the stimulus , and their effect becomes apparent when we ask questions about information transmission by a neural population . Maximum entropy models can only put upper bounds on the total entropy and the noise entropy of the neural code ( and this statement remains true even if successive codewords are not independent ) , and as such cannot set a strict bound , but only give an estimate , for the information transmission . Nevertheless , ignoring the inter-neuron dependencies by using the S1 model would predict the total population responses so badly that the estimated noise entropy would be higher than the upper bound on the total entropy , which is a clear impossibility . In contrast , S2 model gives noise entropy rates that are consistent with the estimate from the static maximum entropy model , and transmission rates that amount to about 60% of the source entropy rate ( comparable to estimates of coding efficiency in single neurons , e . g . , Ref . [65] ) . An alternative approach to describing the joint response of large populations of neurons to external stimuli has been presented in Ref [41] . The Generalized Linear Model ( GLM ) gives a generative model from which one can sample simulated responses to new stimuli , relying on activity history and temporal dependencies between cells . The crucial assumption of the GLM is that the responses of the neurons are conditionally independent given the stimulus and the spiking history; to satisfy this assumption , the discretization of time has to be as fine grained as possible , but certainly well below the discretization of or typically used for maximum entropy models in our retinal preparation . This conditional independence , guaranteed by very short time bins , allows tractable inference procedures to be devised for fitting the GLMs from data . On the other hand , it makes—by its very definition—successive activity patterns dependent on each other , because that is the only way to introduce interactions between the spikes . In contrast , maximum entropy models pick the time bin to be short enough such that multiple spikes are rarely observed in the same time bin , but long enough so that most of the strong spike-spike interactions ( as well as fine temporal detail , such as spike-timing jitter ) occur within a single bin . This allows us to view activity patterns in successive time bins as codewords ( although some statistical dependence between them remains: in the SDME models this is probably due to multiple timescales on which the neurons respond to stimuli; and in the static ME model [31] due to , in part , stimulus correlation ) . If we were to make the time scale in maximum entropy models much shorter , e . g . by an order of magnitude or more , we could make the conditional independence assumption of the responses given the stimuli and previous spiking . This would lead us to GLM-like models in the maximum entropy framework , e . g . , to dynamic/nonequilibrium generalizations of Ising models [48]; in this case , however , we would again lose the interpretation where the instantaneous state of the retina is represented well by a single codeword . For this reason , GLM and SDME are complementary approaches: the first allows for a temporally-detailed probabilistic description of a spiking process , while the second gives an explicit expression for the probability distribution over codewords in longer temporal bins . To our knowledge , there is no easy way to derive one model from the other: while one can fit the GLM with a very small time bins , use it to generate rasters and re-discretize those into time bins of longer duration to get a codeword representation , building a probabilistic model for the codewords from the GLM-derived rasters is as difficult as building it for original data . While a more detailed comparison of these models is beyond the scope of the current work , it is interesting to note that these approaches are different and complementary also in terms of the potential interpretation of their parameters: GLM couplings between neurons have an intuitive interpretation in terms of causal dependency between cells , whereas the SDME ones suggest a prior on the coding vocabulary of the population ( see below ) . Finally , from a modeling viewpoint , GLM lends itself to a clean and tractable maximum likelihood inference framework with regularization , whereas the SDME offers the tools and insights of statistical physics [27] , [42]–[53] ( including , e . g . , advanced Monte Carlo schemes for entropy estimation [66] and the partitioning of the space of codewords in terms of metastable states briefly discussed in this paper ) . Tkačik and colleagues [62] have suggested that one can interpret in an SDME model as a prior over the activity patterns that the population would use to optimally encode the stimulus . For low noise level they argued that the prior should be “weak” ( and could help decorrelate the responses ) because the population could faithfully encode the stimulus , whereas in the noisy regime , the prior should match the statistics of the sensory world and thus counteract the effects of noise . Berkes and colleagues [67] suggested a similar reason for the relationship between ongoing and induced activity patterns in the visual cortex . Our results show that interactions are necessary for capturing the network encoding , and implicitly reflect the existence of such a prior . The recovered interactions are strongly correlated with the interaction parameters of a static , stimulus independent model over the distribution of patterns , making it possible for the brain ( which only has access to the spikes , not the stimulus ) to learn these values . Whether the interactions are matched to the statistics of the visual inputs as suggested in Ref [62] will be the focus of future work . The maximum entropy models presented here can be immediately applied to other brain areas where one can get stable recordings of many neurons over a few tens of minutes [35] , [68] . SDME could be applied to spatially structured stimuli , for instance , to capture the response to the flickering checkerboards: obtaining good estimates of the spatio-temporal receptive fields is standard procedure , identical to that in LN or GLM-type models , while fitting the parameters of the SDME is equally tractable on full field flicker ( as presented here ) or movie with spatial structure . In practice , a different tradeoff would be chosen in experimental design , by making stimulus segment longer to sample the linear filters better from many different stimuli , and decreasing the number of repeats . As we noted above , for fitting the model , one could also eliminate repeated structure altogether , yet repeated presentations of the same stimuli would still be needed to assess the model quality in terms of the PSTH . The current design of the experiment focused on a very large number of repeats of the same stimulus , to allow for as accurate estimate of the PSTH and correlations of individual cells , while future experiments could allow for evaluation of the model on novel repeated stimuli . Given the results we have presented here and those of [41] , we expect that the SDME models would significantly outperform the LN models on novel stimuli as well . Other potential extensions of the pairwise SDME model would include temporal dependencies as in Refs [31] , [49] or a SDME model where the pairwise interactions are also stimulus dependent . While it is not immediately clear how such dependency would be expressed for the ( unlike the linear filter description of the single cell parameters , 's ) , such a model would be instrumental for analysis of population adaptation or learning . Another extension would be to include the dependence of on multiple stimulus projections , or to include high-order interaction terms between spikes , which are likely to play an important role for large populations responding to natural stimuli [34] , [35] . Finally , we also expect that sampling from larger populations , as future experiments will allow , would enable us to give a full characterization of the interaction maps between cells of different classes , which would most likely reflect independence between classes with strong correlations between the cells of the same class , or even stronger correlations at the population level including across different classes; the two alternatives represent an exciting ( and still mostly unanswered ) question . We expect that increasingly detailed statistical models of neural codes , and the efforts to infer such models from experimental data , will allow us to focus our attention on population-level statistics and on finding principled information-theoretic measures for quantifying the code , like the surprise and instantaneous information suggested here . Experiments were performed on the adult tiger salamander , Ambystoma tigrinum . All experiments were in accordance with Ben-Gurion University of the Negev and government regulations . Extracted retinas were placed with the ganglion cell layer facing a multielectrode array with 252 electrodes ( Ayanda Biosystems , Switzerland ) , and superfused with oxygenated Ringer medium at room temperature . Extracellularly recorded signals were amplified ( MultiChannel Systems , Germany ) and digitized at 10 kHz , and spike-sorted using custom software written in MATLAB . Stimuli were projected onto the retina from a CRT video monitor ( ViewSonic G90fB ) at a frame rate of 60 Hz; each movie frame was presented twice , using standard optics . Full Field Flicker ( FFF ) stimuli were generated by independently sampling spatially uniform gray levels ( with a resolution of 8 bits ) from a Gaussian distribution , with mean luminance of 147 lux and the standard deviation of 33 lux . These data allow us to estimate the entropy rate of the source ( as used in the main text ) , by multiplying the entropy of the luminance distribution with the refresh rate . To estimate the cells' receptive fields , checkerboard stimulus was generated by selecting each checker ( on the retina ) randomly every 33 ms to be either black or white . To identify the RF centers , a two-dimensional Gaussian was fitted to the spatial profile of the response . The movies were gamma corrected for the computer monitor . In all cases the visual stimulus entirely covered the retinal patch that was used for the experiment . The firing rates of the cells and the overall covariance of the spiking activity are the key statistics for inferring the models we present , so the reliability of our estimates for these quantities is a key systematic issue . Previous work has shown that 10–20 minute recordings give very reliable estimates [35] , [68] , and that train data of similar size allows for reliable estimates of pairwise-maximum-entropy-based models for populations of this size [68] . The error on instantaneous firing rate was estimated by splitting 626 repeats into two random halves 50 times , and estimating firing rate for each neuron . The relative error in the firing rate , , estimated as ( relative ) std over random splits of data , scales tightly with the mean firing rate with the power , such that at instantaneous rates of about the error is , at the error is , and at the error is . For correlations , we assess their significance by comparing the distribution of real correlation coefficients to the ( null ) distribution where the spikes for each neuron have been randomized in time . The null distribution is evaluated over one half of the repeats , because this is the data size used for training; the mean of the distribution is , and the std , making 95% of observed correlations larger than this spread due to sampling . More in detail , the relative error on correlations was assessed by splitting data 50 times randomly into two halves , and seeing that the relative error scales with the value of the correlations , so that the typical error at significance threshold was about 60% , for ( 80% of all correlations ) it was 18% , for ( 23% of all correlations ) it was 4% , and for it was less than 2% . The average error on significant correlations is slightly below 10% . To quantify the stability of the recordings across time , we computed for each cell the approximate drift in the firing rate , by linearly regressing the average firing rate in each repeat against the repeat index . For about half of the cells the relative change in the firing rate across the whole duration of the experiment was below 25% ( average 14% ) , while for 80% of the cells the drift was below 50% ( average 24% ) . To deal with the remaining non-stationarity , we selected as our training data all odd numbered repeats , and for our test data all even numbered repeats , so that the models were trained and tested across the non-stationary behavior . The LN model for each neuron consists of the linear filter , and the nonlinear function , which is defined pointwise on a set of binned values for the generator signal , . We used binning into bins such that initially each bin contains roughly the same number of values for , but subsequently the binning is adaptively adjusted ( separately for each neuron ) to be denser at higher values of , where the firing rates are higher . We fitted LN models with varying number of bins , and have chosen when the performance of the LN models appeared to saturate [69] . To find the parameters of the stimulus-dependent maximum entropy model ( ) , we retained the binning of the generator signal used for LN model construction . Given trial values for the SDME parameters , we estimated the chosen expectation values ( covariance matrix of neural activity , and the firing rate conditional on , ) by Monte Carlo sampling from the trial distribution in Eq . ( 4 ) ; the learning step of the algorithm is computed by comparing the expectation values in the trial distribution and the empirical distribution ( computed over the training half of the stimulus repeats ) . In detail , we used a gradient ascent algorithm , applying a combination of Gibbs sampling and importance sampling in order to efficiently estimate the gradient , by using optimizations similar to those described in Ref . [70] . Sampling was carried out in parallel on a 16 node cluster with two 2 . 66 GHz Intel Quad-Core Xeon processors and 16 GB of memory per node . The calculation was terminated when the average error in firing rates and coincident firing rates reached below 1% and 5% respectively , which is within the experimental error . To compute the single neuron PSTH and compare the distributions of codewords from the model to the empirical distribution , we used Metropolis Monte Carlo sampling to draw codewords from the model distributions; we drew 5000 independent samples ( to draw uncorrelated configurations , a sample was recorded only after 100 “spin-flip” trials ) for every timepoint , for a total of samples; the same procedure was used also to draw from the conditionally independent ( T1 , S1 ) models . To estimate the entropies of high dimensional SDME distributions , we used the “heat capacity integration” method , detailed in Ref [32] . Briefly , a maximum entropy model ( where is the Hamiltonian function determined by the choice of constrained operators and the conjugated parameters ) is extended by introducing a new parameter , much like the temperature in physics , so that . The entropy of the distribution is given by , where the heat capacity , and the variance in energy can be estimated at each by Monte Carlo sampling . In practice , we run a separate Monte Carlo sampling for a finely discretized interval of temperatures , , estimate for each temperature , and numerically integrate to get the entropy . We have previously shown that this procedure yields robust entropy estimates even for large numbers of neurons [27] , [32] . To evaluate the performance of the models on the testing data , we computed ( i ) the average per-codeword log-likelihood ( reported in the Results section ) , and ( ii ) the GoF ( goodness-of-fit ) figure , reported in Fig . 6 . Regarding ( i ) , for model the log-likelihood is , where the average is over all testing repeats and all times . For models S1 , S2 , the evaluation is straightforward . For T1 model , there is a problem whenever the firing rate of a neuron in the training set is 0 , which leads to undefined log likelihoods . To address this , we add a small regularizer to the estimated firing rates that define the T1 model , and choose to maximize the log-likelihood of T1 on the test set , thus giving maximal possible advantage to the T1 . We also created two models by empirically sampling the frequencies of codewords on training ( testing ) data . Sampling the frequencies on testing data and evaluating on testing data gives the absolute lower bound to the log likelihood . When the frequencies are sampled on training data , we again face a possible problem for codewords whose empirical probability is 0 , but which occur in test data . We introduce a pseudocount regularizer to give these codewords non-zero probability , and set the regularizer to maximize the log-likelihood on testing data , again maximally favoring this model . Regarding ( ii ) , we compute GoF ( goodness-of-fit ) figure as std , where . is the empirical probability of a codeword on the test set , is its model probability , is the expected error on , computed from the multinomial variance for every codeword given its empirical probability , and the std is taken over all non-silent patterns of the test set plotted in Fig . 6 , top row .
In the sensory periphery , stimuli are represented by patterns of spikes and silences across a population of sensory neurons . Because the neurons form an interconnected network , the code cannot be understood by looking at single cells alone . Recent recordings in the retina have enabled us to study populations of a hundred or more neurons that carry the visual information into the brain , and thus build probabilistic models of the neural code . Here we present a minimal ( maximum entropy ) yet powerful extension of well-known linear/nonlinear models for independent neurons , to an interacting population . This model reproduces the behavior of single cells as well as the structure of correlations in neural spiking . Our model predicts much better the complete set of patterns of spiking and silence across a population of cells , allowing us to explore the properties of the stimulus-response mapping , and estimate the information transmission , in bits per second , that the population carries about the stimulus . Our results show that to understand the code , we need to shift our focus from reproducing single-cell properties ( such as firing rates ) towards understanding the total “vocabulary” of patterns emitted by the population , and that network correlations play a central role in shaping the code of large neural populations .
You are an expert at summarizing long articles. Proceed to summarize the following text: Unlike their counterparts in bacterial and higher eukaryotic hosts , most fungal viruses are transmitted intracellularly and lack an extracellular phase . Here we determined the cryo-EM structure at 3 . 7 Å resolution of Rosellinia necatrix quadrivirus 1 ( RnQV1 ) , a fungal double-stranded ( ds ) RNA virus . RnQV1 , the type species of the family Quadriviridae , has a multipartite genome consisting of four monocistronic segments . Whereas most dsRNA virus capsids are based on dimers of a single protein , the ~450-Å-diameter , T = 1 RnQV1 capsid is built of P2 and P4 protein heterodimers , each with more than 1000 residues . Despite a lack of sequence similarity between the two proteins , they have a similar α-helical domain , the structural signature shared with the lineage of the dsRNA bluetongue virus-like viruses . Domain insertions in P2 and P4 preferential sites provide additional functions at the capsid outer surface , probably related to enzyme activity . The P2 insertion has a fold similar to that of gelsolin and profilin , two actin-binding proteins with a function in cytoskeleton metabolism , whereas the P4 insertion suggests protease activity involved in cleavage of the P2 383-residue C-terminal region , absent in the mature viral particle . Our results indicate that the intimate virus-fungus partnership has altered the capsid genome-protective and/or receptor-binding functions . Fungal virus evolution has tended to allocate enzyme activities to the virus capsid outer surface . The virus capsid is sufficiently robust to protect the genome and ensure its propagation during passage from one host cell or organism to another; it must simultaneously be labile enough to allow genome delivery within the host cell to initiate infection . The capsid is not only a metastable macromolecular assembly , but is also a dynamic structure whose protein components have transient conformations related to its distinct roles during the viral life cycle [1 , 2] . Numerous high resolution structural studies have shown that , despite the vast diversity and complexity of virus capsids , assembly of these closed protein shells is limited to a few capsid protein ( CP ) folds . As a result , evolution leads to a small number of viral lineages that share a CP fold as well as a set of common assembly principles [3] . Icosahedral viruses are grouped in four lineages , ( i ) the dsDNA viruses with an upright double β-barrel CP ( prototypes are phage PRD1 and adenovirus ) , ( ii ) the tailed dsDNA phages , tailed haloarchaeal viruses , and herpesviruses , which all share the Hong Kong 97 ( HK97 ) -like CP fold , ( iii ) the ssRNA picornavirus-like superfamily with a single β-barrel as the CP fold , and ( iv ) the dsRNA or bluetongue virus ( BTV ) -like viruses [4] . The dsRNA viruses have a specialized icosahedral capsid that remains structurally undisturbed during endogenous transcription , thus avoiding induction of host cell defense mechanisms [5] . The capsid also participates in genome transcription and replication by organizing packaged genomes and the RNA-dependent RNA polymerase ( RdRp ) complex ( es ) . This unusual T = 1 capsid is built from 60 asymmetrical dimers of a single CP ( i . e . , a 120-subunit T = 1 capsid ) , in which the CP fold is the hallmark of the BTV lineage . This capsid structure is found in members of the families Reoviridae [6–9] , Picobirnaviridae [10] and Cystoviridae [11 , 12] , and in the mycoviruses of the families Totiviridae [13–15] , Partitiviridae [16 , 17] , and Megabirnaviridae [18] . Chrysoviruses , a group of dsRNA mycoviruses with a multipartite genome , have a T = 1 capsid with 60 subunits of a single 982-amino-acid CP , but the CP is an almost perfect structural duplication of a single domain [19–21] . Birnaviruses are an exception , as they have a single T = 13 shell and lack the T = 1 capsid [22 , 23] . A distinctive feature of the fungal dsRNA viruses is their lack of an extracellular route of transmission [24] . Probably as a result of their intracellular transmission ( by cell division , sporogenesis and cytoplasmic fusion ) , most mycoviruses have a single-shell capsid . In contrast , reo- and cystoviruses have multilayered concentric capsids , with one or two T = 13 shells surrounding the T = 1 layer ( referred to as the inner core ) . Whereas most dsRNA virus capsids are based on dimers of a single protein , the type species of the family Quadriviridae , Rosellinia necatrix Quadrivirus 1 ( RnQV1 ) , has a single-shelled T = 1 capsid formed by 60 heterodimers of two proteins of 1356 ( P2 ) and 1059 residues ( P4 ) [25] . RnQV1 is a mycovirus with a multipartite genome consisting of four monocistronic dsRNA segments ( genome sizes range from 3 . 7 to 4 . 9 kbp ) , in which one or two dsRNA segments are encapsidated in a similar particle [26] . dsRNA-1 codes for a protein of unknown function , dsRNA-2 encodes the P2 CP , dsRNA-3 codes for RdRp ( 1117 amino acids ) , and dsRNA-4 codes for the P4 CP . In RnQV1 strain W1075 , P2 and P4 are cleaved into several discrete peptides without altering capsid structural integrity , whereas in strain W1118 both proteins remain nearly intact [27 , 28] . Here we report the three-dimensional ( 3D ) cryo-EM structure of the RnQV1 W1118 capsid at 3 . 7 Å resolution . We found that P2-P4 heterodimers are organized in a quaternary structure similar to that of reovirus , chrysovirus and totivirus . Despite the low sequence identity ( ~15% , after introduction of 23% gaps ) , superimposition of P2 and P4 showed a common core similar to the CP fold of the dsRNA virus lineage . P2 and P4 have also acquired new functions by insertion of complex domains with potential enzyme activity in preferential insertion sites . Purified empty RnQV1-W1118 particles were visualized in a 300-kV FEI Titan Krios cryo-electron microscope ( Fig 1A ) . Signal was detectable to 3 . 4 Å ( Fig 1A inset , red arrow ) . We merged ~37 , 500 particle images to calculate a 3 . 7 Å resolution map ( Fig 1B ) , as estimated by the criterion of 0 . 143 Fourier shell correlation coefficient ( Fig 1C ) . Some regions on the uneven outer surface of the RnQV1 capsid , especially those at the five-fold axis , were less well-defined than its smooth inner surface ( S1A Fig ) , although the backbone was traceable unambiguously ( S1B Fig ) . Approximately 96% of the amino acid side chains were resolved ( S1C and S1D Fig ) . We used the Coot program to build the polypeptide chain , based initially on structural predictions for some amino acid sequence segments ( Figs 2 and 3 ) . The ~470 Å-diameter T = 1 capsid shows 12 outwardly protruding decamers at the pentameric positions ( Fig 1B , orange ) , each bearing five copies of P2 and P4 [25] . Each pentameric capsomer is formed by an inner ring of five P2 monomers ( Fig 1D , blue; inset ) surrounded by an outer ring of five partially intercalated P4 monomers ( Fig 1D , yellow; inset ) , in which the asymmetric unit is a P2-P4 heterodimer ( Fig 1B and 1D ) . The quaternary organization of P2 and P4 in the capsid shell is similar to that of homodimers of reoviruses and other dsRNA mycoviruses . Our near-atomic models for P2 and P4 had 972 and 1005 residues , respectively , of the total 1356 ( P2 ) and 1059 ( P4 ) amino acids . P2 lacks 383 residues in the C-terminal region , predicted to have a high helical content ( Fig 2 ) , and P4 lacks 54 C-terminal residues ( Fig 3 ) . The last visible P4 residue is Lys1005 ( Fig 3 ) , located at the five-fold axis channel . Although the remaining P4 C-terminal 54-residue segment is very flexible ( see below ) , five small densities that follow Lys1005 occlude the channel ( Fig 1B , white ) . The P2 383-residue C-terminal region is invisible in our map . Analysis of this region in the RnQV1 capsid 3D reconstruction at lower resolution indicated no density that could account for the missing P2 domain . Sequence-based SSE prediction methods indicated a large number of α-helices , which are predicted very reliably . To determine whether the P2 C-terminal region was present , we first analyzed the purified RnQV1 capsids by SDS-PAGE . P4 and P2 proteins , detected as 110- and 100-kDa bands as reported [25] , were analyzed by MALDI-TOF/TOF mass spectrometry ( MS ) . Mass spectra showed no evidence of P2 C-terminal peptides ( S2 Fig and S1 Table ) , nor of the P4 55-residue C-terminal region . With previous biochemical analyses [25] and structural studies of this region ( see below ) , these data suggested that the P2 C-terminal end is indeed proteolyzed and absent from the viral particles . The structure of P2 ( built from 29 α-helices and 20 β-strands ) and P4 ( 29 α-helices , 9 β-strands ) was organized similarly and was divided into two domains , a shell ( S ) and a protruding ( P ) domain ( Fig 1D , right ) . Although the P4 ( residues 648–805 ) and P2 ( 204–365 ) protruding domains were similar in size , they had very different folds . P2 and P4 overlapped considerably at the S domain . Both had a long α-helix tangential to the capsid surface , the α16 helix in P2 ( 36 Å long , 24 residues ) and α15 in P4 ( 50 Å , 33 residues ) , a common characteristic of the dsRNA virus lineage CP ( Fig 1D , right , arrows ) . The conserved fold in P2 and P4 had two internal peptide insertion sites facing the outer capsid surface . For P2 , these segments are Ser204-Asp365 ( 162 residues ) in insertion site 1 and Ser599-Asp849 ( 251 residues ) in insertion site 2 and for P4 , the 89-residue His239-Tyr327 and the 367-residue Gly563-Thr929 segments , respectively ( Fig 5A ) . Both insertion sites coincided with ScV-L-A Gag insertion sites , and one with the single-insertion site of the PcV CP domains ( S4 and S5 Figs ) . One interesting possibility is that the P2 and P4 insertions have associated enzymatic activities . The P2 Asp227-Tyr350 domain ( in insertion site 1 ) is a twisted β-sheet of five antiparallel β-strands and three α-helices ( Fig 5B ) . Analyses with Fatcat , Cath and Dali servers showed that this P2 domain has structural similarity , in addition to ferredoxin , to gelsolin and profilin ( S3 Movie ) , two actin-binding proteins with a variety of actin regulatory functions [30] . The equivalent P4 segment is rather small , an 89-residue segment that includes the α-helices α7-α8-α9 . The P4 Val648-His805 domain ( in insertion site 2 ) has a surface cavity in which the last visible P2 C-terminal residue , Gly973 , contacts P4 Ile791 ( Fig 5C and 5D ) . Preceding Gly973 , the α29 helix and a 43-residue loop are positioned in a defined area on the P4 outer surface ( S4 Movie ) . We hypothesize that this surface crevice in P4 participates in proteolytic processing of the P2 C-terminal domain . Whereas contact interface between P2 and P4 has a surface of 7742 Å2 , the contact surface area of each P2-P4 heterodimer with the surrounding heterodimers is 15 , 989 Å2 . In addition to the numerous weak interactions in the buried intra- and interdimeric surfaces that stabilize the capsid , there are five molecular hooks formed by short loops or α-helices that extend further on the rough contact surfaces , and contribute to pentamer stability ( Fig 6A , labeled I-V ) . Hook I , mediated by the P2 α2 helix ( Gly11- Lys27 ) , is a molecular swap between related P2 five-fold subunits . The α2 helices also form the broad opening of the channel on the capsid inner surface ( Fig 6B , red helices ) . Another interdimeric hook , in this case between P2 and P4 , is formed by P2 helix α6 ( Thr154-Arg17 ) on the outer surface ( hook II ) . Within the P2-P4 heterodimer , the P2 Leu883-Cys904 segment ( which includes two short α-helices , α27 and α28 ) and its C-terminal end ( Gly913-Gly973 segment ) embrace the P4 inner and outer surfaces , respectively ( Fig 6A , hooks III and IV ) . Hook V interdimeric connections are P4-mediated , in which loop Gln963-Gly976 interacts with the P2 inner surface . These numerous reinforcements indicate that once dimers are formed , the pentamer constitutes the most stable capsid assembly unit . P2 C-terminal ends are relatively close to each other at the two-fold axes , separated by 45 Å ( Fig 6A , double arrow ) . The missing 383-residue P2 C-terminal segment ( Fig 6A , large orange circles ) might constitute an external scaffolding domain . Pores are located at the five- and three-fold axes ( Fig 6C ) . The electrostatic potential of the inner capsid surface showed a very negatively charged surface ( Fig 6C ) that maintains RNA density ~25 Å from the capsid surface [25] . Except for the Penicillium chrysogenum virus ( PcV ) capsid ( with positively charged regions in the inner surface that maintain RNA density in close contact ) , this feature resembles that of other single-shelled T = 1 capsids of dsRNA mycoviruses , as well as reo- and picobirnavirus ( S6 Fig ) . The relatively low packed genome density in RnQV1 [25] and other fungal dsRNA viruses [16 , 19 , 31] could help minimize electrostatic repulsion between dsRNA and the acidic inner capsid surface . The P2 α2 helix forms the inner opening of the pores ( ~11 Å-diameter hole ) at the five-fold axis ( Fig 6B , red helices; Fig 6C , black arrows ) ; these openings narrow to ~5 Å diameter between the Lys27 side chains ( Fig 6B , red ) . The P2 α5 helices , which are tangential to the capsid surface ( Fig 6B , green helices ) , form the channel wall , maintaining the ~5 Å-diameter hole between Arg150 side chains ( Fig 6B , green ) . The last visible residue of the P4 C termini , Lys1005 , is surface-exposed and ends at the external pore opening ( Fig 6B , blue ) . The P4 54-residue C-terminal segment , not traceable from the cryo-EM density map , is partially ordered near this surface opening , and acts as a molecular plug ( Fig 6C , green; S7 Fig ) . Pores at the three-fold axis left a ~7 Å-diameter hole ( Fig 6C , blue arrows , bottom right ) . These structural features indicate that exit of ssRNA viral transcripts ( as in other dsRNA viruses ) would require conformational changes in P2 and/or P4 segments . The structural features described thus for the ~300-residue conserved region between P2 and P4 ( P2* and P4* ) indicate that it could have evolved from the ancestral domain of the dsRNA virus lineage . We compared P2* and P4* with the CP of several mycoviruses such as L-A virus of the yeast Saccharomyces cerevisiae ( ScV-L-A ) , a totivirus with a single genome dsRNA segment , and PcV , a chrysovirus with four monocistronic dsRNA segments . These mycoviruses , including RnQV1 , are transmitted intracellularly and have no outer shell on the rough outer surface of their T = 1 capsids , in contrast with the smooth outer surface of reo- and cystovirus capsids . Dali structural alignments between the Gag CP of ScV-L-A or the PcV A domain with P2* indicated notable structural similarity ( Fig 7 , Table 1 , S4 Fig ) . When P2* and Gag or the A domain were superimposed , 11 α-helices and 5–6 β-strands showed close relative spatial positions and required only minor local adjustments for overlap ( S5 and S6 Movies ) . Comparable analysis of Gag and the A domain with P4* showed superimposition of 8–9 α-helices and 3 β-strands ( Fig 7 , Table 1 , S5 Fig , S5 and S6 Movies ) . We performed similar analyses between PcV B domain and P2* and P4* ( Table 1 ) . The most conserved α-helices between P2* and P4* with Gag and A and B domains were located near the five-fold axis . The P2* and P4* β-sheet structure , formed by three relatively long β-strands and found respectively at the two- and three-fold axes , was also very conserved in all mycoviruses tested . The P2 SIID ( residues 738–848 ) and P4 ( 817–929 ) were detected neither in CP of the other mycoviruses nor of higher dsRNA viruses . The 120-subunit T = 1 capsid is an almost ubiquitous architecture of the protein layer that surrounds the dsRNA virus genome . The asymmetric unit is a CP homodimer with a conserved fold exemplified by the BTV CP fold . In addition to capsid assembly constraints , this fold is necessary for capsid function , to organize the viral genome and replicative machinery , and act as a molecular sieve to evade intracellular host defense mechanisms . Here we show the RnQV1 capsid at near-atomic resolution; its asymmetric unit is a dimer of two distinct proteins , P2 and P4 ( 2415 amino acid residues total ) and is thus the most complex T = 1 capsid known so far . Despite their lack of sequence similarity , structural alignment of P2 and P4 showed two common domains in the shell region , SID and SIID . Whereas SID is preserved in other fungal dsRNA viruses ( toti- and chrysoviruses ) [32] as well as in reoviruses , SIID is unique to P2 and P4 . P2 and P4 SID ( P2* and P4* ) have 11 α-helices ( including a long α-helix tangential to the capsid surface ) and a β-sheet with similar tertiary structures . These SSE show the folding signature of most dsRNA virus CP . SIID , which has 3 α-helices and a short β-sheet , is much smaller than SID ( ~110 vs . ~350 residues ) . Whereas P2* and P4* preserved folds are compatible with a common CP ancestor of dsRNA viruses , P2 and P4 SIID suggest a common origin for P2 and P4 . Duplication of an ancestral gene for a CP with the BTV-like fold might have resulted in two separate ( as in quadriviruses ) or covalently joined folds ( as in chrysoviruses ) . This event could direct assembly of a T = 1 capsid with 120 subunits or domains with a dimer as the asymmetric unit , a necessary arrangement for dsRNA replication/transcription . Separate and joined folds are found in other virus families , which implies a recurrent evolutionary event . Whereas adeno- and comovirus CP are the result of a joined fold [33 , 34] , the picornavirus capsid is assembled from three different proteins with very similar folds , although with negligible sequence similarity [4] . Once the 120-subunit capsid was well established , later divergent evolutionary events would have introduced additional changes in each copy , or even the complete removal of one of them , giving a CP that assembles as a dimer of unfused identical monomers ( the so-called A and B subunits ) . Alternatively , the ancestral CP could have initially acquired dimer assembly ability , followed by gene duplication; domain swapping could be another mechanism by which dimers and oligomers assemble from monomers [35] . Besides the subunit interactions in the solvent-buried surfaces , there are five molecular hooks in the RnQV1 capsid that probably stabilize assembly of five dimers into a pentamer , which has major effects on the RnQV1 assembly pathway . In reo- , toti- , and chrysoviruses , all built from asymmetric dimers , CP are arranged approximately as parallel dimers [8]; these viruses probably initiate capsid assembly from pentamers of dimers . Partiti- and picobirnavirus capsids ( built from quasi-symmetric dimers ) , as well as the cystovirus capsid , are thought to use dimers of CP dimers as assembly intermediates [10 , 16] . The P2 and P4 asymmetric dimer lacks the 383-residue P2 C terminus . This cleaved polypeptide fragment is predicted to be helical; we hypothesize that it acts as an external scaffold domain covalently bound to P2 . Given that the last P2 C-terminal residue is only 45 Å from a neighboring P2 C terminus end , dimerization of these scaffolds could direct pentamer accretion or , more probably , assembly of two dimers into a tetramer ( S8 Fig ) . The immature RnQV1 capsid would thus have 30 dimeric α-helical densities extruding radially relative to the capsid shell . Generation of cDNA clones for P2 and P4 might allow introduction of mutations in the P2 processing site to assemble immature virus-like particles . Dimerization and high α-helical content are ubiquitous features of scaffold proteins [36 , 37] . External scaffold proteins are reported for capsids of bacteriophages P4 [38 , 39] and ϕX174 [40] . The protease-free Prohead-1 map of bacteriophage HK97 shows rod-shaped densities intruding on its internal surface , which correspond to the scaffold domains fused to the CP N termini [41] . The P1 CP of bacteriophage ϕ8 , a cystovirus , is found as a soluble tetramer in an in vitro assembly system [42] . In multilayer capsids of dsRNA viruses ( as in Reo- and Cystoviridae ) , the T = 1 capsid has a smooth outer surface that serves as nucleation site for the T = 13 surrounding capsid . Fungal dsRNA viruses are transmitted by cytoplasmic interchange and commonly confined to the host , and have a single-shelled T = 1 capsid with an uneven outer surface . The original roles of the capsid , such as receptor binding and/or genome protection , have given way to new abilities reflected in altered capsid surface features . In RnQV1 , in addition to the N and C termini , we detected two well-defined insertion sites into which insertions of ~90–370 residue segments lead to variations . These preferential insertion sites , which face the outer capsid surface as also seen in PcV and ScV-L-A , are conserved among dsRNA viruses and are probably ancient . The 41-residue insertion in ScV-L-A Gag insertion site 1 is responsible for the decapping that transfers cap structures from the 5’ end of cellular mRNA to the 5’ end of viral RNA [43 , 44]; it coincides with the 162-residue insertion in P2 , which has structural similarity to proteins involved in actin cystoskeleton regulation . In P4 insertion site 2 , the 367-residue insertion has a surface cleft with putative proteolytic activity , which our structural results suggest is primarily exerted on the P2 C terminus , although other proteins might be targeted . PcV insertions are much smaller and no activity or structural similarity has yet been found . The similarity of 3D locations of these inserted peptides/domains could indicate a mechanism for acquisition of new functions without altering the basic structural core of the dsRNA virus CP . The CP of many tailed dsDNA phages with the canonical HK97-like core ( also termed Johnson fold ) has additional domains or defined regions with specific functions related to capsomer or capsid stability ( reviewed in [45] ) . The human cytomegalovirus ( HCMV ) , a herpesvirus , has a 1370-residue CP folded into seven domains [46] and thus shares some similarity with the RnQV1 CP . The HCMV Johnson fold or floor domain at the shell has a six-domain protruding tower that interacts with several outer CP and has specific functions based on the virus transmission cycle . The Johnson fold has a five-stranded β-core that acts as an organizational hub of the major CP; the additional domains are considered modular insertions into the peripheral loops [46] . In this context , conserved α-helices and/or the β-sheet structure preserved in the dsRNA virus basic fold might constitute a similar functional center . Analyses of the RnQV1 outer capsid surface showed that the insertional domains for these activities are proximally aligned and could form an efficient assembly line of molecular machines . To our knowledge , mycovirus capsids are not yet exploited for nanotechological applications; our structural studies might be a step toward future protein-based nanocages that incorporate active whole protein in a controlled platform . RnQV1 virions were purified from Rosellinia necatrix strain W1118 by two cycles of differential centrifugation and one rate zonal centrifugation in a sucrose density gradient , as described [25 , 47] . The two UV-absorbing bands , corresponding to empty ( top ) and to a mixture of empty and full particles ( bottom ) , were collected separately by side puncture , diluted with buffer A ( 50 mM Tris-HCl buffer , pH 7 . 8 , 5 mM EDTA , 150 mM NaCl ) , and concentrated by centrifugation ( 106 , 000 x g , 12 h , 4°C ) [25] . Concentrated particles were loaded on a 36% CsCl cushion and ultracentrifuged ( 180 , 000 x g , 110 min , 4°C ) . The purified empty particles , derived from a mixture of full and empty particles , were homogeneous and appropriate for further cryo-EM analysis . Purified RnQV1-W1118 empty particles ( 5 μl ) were applied to R2/2 300 mesh copper-rhodium grids ( Quantifoil Micro Tools , Germany ) and vitrified using a Leica EM CPC cryofixation unit . Data were collected on a FEI Titan Krios electron microscope operating at 300 kV and images recorded on a FEI Falcon II detector at a calibrated magnification of 104 , 478 , yielding a pixel size of 1 . 34 Å . A dose rate of 28 electrons/Å2/s and 1 . 4 s exposure time were used to record 1 , 125 24-frame movies with a defocus range of 0 . 7 to 3 . 5 μm . To correct for beam-induced movement , the 18 central frames of each movie were aligned using whole-image motion correction [48] , after which local movements were corrected using an Optical Flow approach [49] . Contrast transfer function parameters of each averaged movie were determined using CTFFIND3 [50] . Images showing astigmatism and/or motion signs were discarded , maintaining a total of 996 . General image processing operations were performed using Xmipp [51] [http://xmipp . cnb . csic . es/] and Relion [52] [http://www2 . mrc-lmb . cam . ac . uk/relion/index . php/Main_Page] . Graphics were produced by UCSF Chimera [53] [http://www . cgl . ucsf . edu/chimera/] . A total of 53 , 683 particles were picked with the Xmipp automatic picking routine and 37 , 531 particles were selected manually . Alternatively , Relion reference-free 2D classification was used to discard bad particles and a similar data set of 42 , 267 particles was obtained . 3D classification with Relion resulted in four classes using the RnQV1-W1075 structure [25] ( EMD-3437 ) and low-pass filtered to 40 Å as initial reference . Further final Relion iterative auto-refine was performed including 19 , 194 particles ( for the best 3D class ) , 28 , 101 ( including the two best 3D classes ) , or including all four 3D classes . The Relion particle polishing protocol was used for per-particle motion correction by fitting linear tracks and to weight each frame with a different B-factor based on a dose-dependent model for radiation damage . The best resolution , 3 . 70 Å based on the gold-standard FSC = 0 . 143 criterion corrected for the effects of a soft mask on the FSC curve using high-resolution noise substitution , was obtained using the complete data set of 3D-selected particles [54] . The final density map was corrected for the modulation transfer function ( MTF ) of the detector and sharpened by applying the estimated B-factor [55] . Local resolution variations were calculated using ResMap [56] . The backbone of each polypeptide chain in a single asymmetric unit of the 3 . 7 Å cryo-EM map was built de novo using Coot crystallographic modeling [57] . A poly-Ala sequence was entered manually for each protein and clear densities of bulky side chains were marked . The comparison of predicted and observed SSE was used as starting point to register the amino acid sequence for P2 and P4 proteins . In this process , we used the P4 region of the helices α14-α16 ( residues 472–541 ) , which matched the four predicted helices α15-α 18 ( residues 476–548 ) , with focus on the bulky side chains at residues 522–526 . Once the sequence was registered , positions of the main and side chains were adjusted manually and fit of the atomic model to the density map was improved by iterative cycles of model rebuilding using Coot [57] . To evaluate and improve model accuracy , we used Refmac5 [58] to eliminate clashes and inconsistencies in geometry . Comparison of coordinates before and after Refmac5 processing was used to locate regions that required further refinement . In addition , the geometry of the model was tested iteratively with Coot validation tools , which helped to detect and correct residues outside allowed regions in the Ramachandran plot . The final modeled coordinates were refined in 10 additional cycles of Refmac5 , and Coot . The Ramachandran plot showed 2 . 59% of residues in disallowed regions . The electrostatic potential for the RnQV1-W1118 capsid was calculated using DelPhi software [59] and surface-colored with UCFS Chimera . The Dali server [60] ( http://ekhidna . biocenter . helsinki . fi/dali_server/start ) was used for structural alignment between RnQV1 CP P2 and P4 proteins and their overlap with PcV CP A and B domains as well as L-A Gag CP . We used FATCAT [61] ( http://fatcat . burnham . org ) , CATH [62] ( http://www . cathdb . info ) and Dali servers to search for structures related to the P2 Asp227-Tyr350 domain . Protein gel bands from sucrose gradient fractions enriched with RnQV1 capsids were excised from Coomassie-blue stained gels , placed in a 96-well plate and digested using the DP Proteineer digestion robot ( Bruker Daltonics ) with trypsin ( 5 h , 37°C ) as described [63] . Resulting peptides were analyzed by MALDI-TOF/TOF using an ABI Sciex 4800 Proteomics Analyzer . To submit the combined peptide mass fingerprint ( PMF ) and MS/MS data to MASCOT software v . 2 . 5 . 1 ( Matrix Science , UK ) , GPS Explorer v4 . 9 was used , searching the nonredundant NCBI protein database ( NCBInr_20160610 ) . The RnQV1-W1118 cryo-EM map is deposited in the Electron Microscopy Data Bank ( accession no . emd-3619 ) and the atomic coordinates of the RnQV1-W1118 P2 and P4 proteins are deposited in the PDB ( ID code 5ND1 ) .
Most fungal RNA viruses are transmitted by cytoplasmic interchange without leaving the host . We report the cryo-electron microscopy structure , at near-atomic resolution , of the double-stranded RNA Rosellinia necatrix quadrivirus 1 ( RnQV1 ) ; this virus infects the fungus Rosellinia necatrix , a pathogenic ascomycete to a wide range of plants . At difference most dsRNA viruses , whose capsid is made of protein homodimers , RnQV1 is based on a single-shelled lattice built of 60 P2-P4 heterodimers . Despite a lack of sequence similarity , P2 and P4 have a similar α-helical domain , a structural signature shared with the dsRNA virus lineage . In addition to organizing the viral genome and replicative machinery , P2 and P4 have acquired new functions by inserting complex domains in preferential insertion sites . Whereas the P2 insertion domain has a fold like that of actin-binding proteins , the structure of the P4 insertion domain indicates proteolytic activity . Understanding the structure of a fungal virus capsid with enzyme activities could allow its development as nanoreactors for biotechnological application .
You are an expert at summarizing long articles. Proceed to summarize the following text: Microsporidia are a group of obligate intracellular parasitic eukaryotes that were considered to be amitochondriate until the recent discovery of highly reduced mitochondrial organelles called mitosomes . Analysis of the complete genome of Encephalitozoon cuniculi revealed a highly reduced set of proteins in the organelle , mostly related to the assembly of iron-sulphur clusters . Oxidative phosphorylation and the Krebs cycle proteins were absent , in keeping with the notion that the microsporidia and their mitosomes are anaerobic , as is the case for other mitosome bearing eukaryotes , such as Giardia . Here we provide evidence opening the possibility that mitosomes in a number of microsporidian lineages are not completely anaerobic . Specifically , we have identified and characterized a gene encoding the alternative oxidase ( AOX ) , a typically mitochondrial terminal oxidase in eukaryotes , in the genomes of several distantly related microsporidian species , even though this gene is absent from the complete genome of E . cuniculi . In order to confirm that these genes encode functional proteins , AOX genes from both A . locustae and T . hominis were over-expressed in E . coli and AOX activity measured spectrophotometrically using ubiquinol-1 ( UQ-1 ) as substrate . Both A . locustae and T . hominis AOX proteins reduced UQ-1 in a cyanide and antimycin-resistant manner that was sensitive to ascofuranone , a potent inhibitor of the trypanosomal AOX . The physiological role of AOX microsporidia may be to reoxidise reducing equivalents produced by glycolysis , in a manner comparable to that observed in trypanosomes . Microsporidia are a large and diverse group of eukaryotic intracellular parasites that infect a wide variety of animal lineages , including humans [1] . Although once thought to be early branching eukaryotes , they are now widely accepted to be very atypical parasitic fungi [2] , [3] , [4] , [5] . They are highly adapted to the infection process , and many typical eukaryotic features have been simplified , reduced , or lost completely . Microsporidian genomes are reduced and organelles such as the peroxisome , mitochondria and Golgi apparatus are absent or altered from their canonical forms [6] , [7] , [8] . In particular , microsporidian mitochondria have been severely reduced into biochemically and physically streamlined “mitosomes” [8] . Mitosomes lack their own genome , and there is no evidence from the nuclear genome of any microsporidian for genes encoding any of the respiratory chain complexes or an F1-ATP synthase complex . In the absence of the ability to synthesize ATP through oxidative phosphorylation , microsporidia appear to import ATP directly from their host cell via ATP translocases located in the cell membrane [9] , [10] , using a transporter which may have been acquired by lateral gene transfer from bacterial energy parasites such as Chlamydia and Rickettsia [11] . Identification of which mitochondrial-derived genes have been retained in the complete genome of Encephalitozoon cuniculi , together with immunolocalization studies in E . cuniculi and Trachipleistophora hominis , suggest that the major functional role for the mitosome is not in energy generation , but instead the assembly of iron-sulphur clusters for export to the cytoplasm [6] , [12] , [13] . Biochemical and genomic evidence all generally point to glycolysis as the major route of energy generation in most microsporidia [6] , [9] . In order for ongoing glycolytic activity to be sustainable , however , some mechanism to reoxidise reducing equivalents produced by this pathway is also required . Of the few proteins associated with the microsporidian mitosomes that are not involved in iron-sulfur cluster assembly , one is glycerol-3-phosphate dehydrogenase . This enzyme is the mitochondrial component of the glycerol-3-phosphate shuttle , a pathway used in some eukaryotes to move reducing equivalents into mitochondria [14] . Both cytosolic and mitochondrial components of this shuttle are encoded in the genomes of several microsporidia that have been well studied [6] , [15] , and it has been suggested that this could provide a mechanism sustaining glycolysis in the cytosol by reoxidising glycerol-3-phosphate [9] . However , the E . cuniculi mitochondrial glycerol-3-phosphate dehydrogenase does not appear to be located in the mitochondrion any longer [12] , and even if a working shuttle was present , there is no obvious mechanism for reoxidation of the co-reduced FAD produced by this shuttle in the genome of E . cuniculi [6] . In the bloodstream form of Trypanosoma brucei parasites , the mitochondrial glycerol-3-phosphate dehydrogenase is coupled to an alternative oxidase ( AOX ) that together achieve this process [16] , and a similar system has been postulated to be present in the apicomplexan parasite Cryptosporidium parvum [17] . AOX is a cyanide-insensitive terminal oxidase that is typically located on the inner surface of the inner mitochondrial membrane . It branches from the main respiratory chain at the level of the ubiquinone pool , results in the net reduction of oxygen to water , and is non-protonmotive [18] , [19] , [20] . It has been found in some prokaryotic lineages , including alpha-proteobacteria [21] , and has a wide but discontinuous distribution across eukaryotes: it is widely distributed in plants , and has also been found in a handful of invertebrate animals [22] , [23] , [24] , [25] . In parasitic protists , the distribution of AOX is also uneven: it is known from the amoebozoan Acanthamoeba castellanii , the heterokont Blastocystis hominis , and the trypanosomes . In the alveolates , it is found in the apicomplexan Cryptosporidium and some other distantly related alveolates including some ciliates , but absent from the more closely related Plasmodium parasites [26] , [27] , [28] . The broad overall distribution of AOX may be indicative of an early origin in eukaryotes , and is perhaps even derived from the endosymbiotic alpha-proteobacterium that gave rise to mitochondria [27] , [29] . In fungi , the protein also has a wide but discontinuous distribution [30] , but it is absent from the completely sequenced genome of E . cuniculi and from the recent large-scale genome surveys of Nosema ceranae and Enterocytozoon bieneusi [6] , [31] , [32] . Interestingly , however , we identified a homologue in the partially sequenced genome of Antonospora locustae , demonstrating the pattern of retention versus loss is also uneven within the microsporidia , despite their otherwise common mode of intracellular parasitism and apparently similar metabolism . The possible presence and function of AOX in microsporidia is of practical interest as well , because the absence of AOX in mammals , including humans , renders it a potential therapeutic target for the treatment of microsporidiosis , as is the case in a number of protisitan parasites [16] , [33] , [34] . This is of particular importance in microsporidia as current medical treatments are not universally effective . The drugs of choice for microsporidiosis are currently albendazole and fumagillin [35] . Whilst albendazole is used in treating many species , some , such as V . corneum and E . bieneusi are resistant and in these cases fumagillin , which is mildly toxic , has to be used [36] . Here , we characterize the phylogenetic distribution of microsporidian AOX , and examine the functional activity of AOX enzymes from the human parasite T . hominis and the insect parasite A . locustae . Phylogenetically the microsporidian AOX is weakly related to mitochondrial homologues from other eukaryotes , and both A . locustae and T . hominis AOX proteins include an N-terminal leader that was demonstrated by confocal microscopy to target the proteins to mitochondria in yeast , altogether suggesting the enzyme is likely derived from the mitosome and may be localized in the organelle still , though direct co-localization would be required to give a definitive location of function . Enzyme assays with recombinant proteins demonstrated both possess cyanide-resistant oxidase activities sensitive to inhibition by the very specific trypanosome AOX inhibitor ascofuranone [37] , suggesting the enzyme functions as a terminal electron receptor . The complete genome of E . cuniculi lacks any gene resembling AOX , but we identified a full-length homologue of the AOX gene in the partial genome of A . locustae ( gmod . mbl . edu/perl/site/antonospora01 , Antonospora locustae Genome Project , Marine Biological Laboratory at Woods Hole , funded by NSF award number 0135272 ) . To determine the distribution of this gene , degenerate PCR was used to amplify a short fragment of AOX from other species of microsporidia , Glugea plecoglossi ( 233 bp ) , Spraguea lophii ( 235 bp ) , and T . hominis ( 236 bp ) . To examine the complete sequence of an AOX from a human parasite , the ends of the T . hominis AOX gene were also sequenced using 5′ RACE and splinkerette protocols [38] , resulting in a full length gene of 957 bp with a translated protein of 318 amino acids ( compared to A . locustae AOX , which had a length of 831 bp ) . Hypothetical translations of A . locustae and T . hominis sequences contain all conserved sites consistent with AOX activity . Specifically , both genes encode the six conserved di-iron binding ligands that are essential for AOX activity ( Figure 1 ) , which are conserved in all alternative oxidases sequenced to date [17] , [20] , [39] . In addition both sequences contain 4 highly conserved tyrosine residues , one of which ( Tyr at the S . guttatum equivalent position 275 ) is considered to be critical for the net reduction of oxygen to water and probably plays a key role in enzyme catalysis ( Figure 1 ) [19] . Further confirmation that A . locustae and T . hominis sequences encode AOX proteins is the finding that a putative substrate binding site ( residues 242–263 ) [19] is also conserved in both microsporidia . However , one striking difference between the microsporidian AOX sequences and those AOX sequences found in all other mitochondria and protists is the lack of tryptophan-206 , which is most unusual since it is highly conserved and has been proposed to play either a structural or catalytic role [18] . In A . locustae the tryptophan has been replaced by serine whilst in T . hominis it has been replaced by alanine . Similar to other parasite AOXs however , none of the cysteines postulated to play a role in the regulation of AOX activity in plants [40] , are present in either A . locustae or T . hominis . Mitoprot I predicted both microsporidian AOX sequences to encode amino-terminal mitochondrial transit peptides , and the T . hominis AOX protein was also predicted by Predotar and TargetP 1 . 1 to have a mitochondrial targeting peptide . In order to test the degree of conservation and functionality of potential targeting signals , full-length proteins were expressed in S . cerevisiae cells fused to a green fluorescent reporter protein . Expression in yeast shows that GFP overlays mitotracker fluorescence , indicating successful heterologous targeting for both proteins ( Figure 2 ) . The phylogenetic relationship among alternative oxidases is in general poorly resolved . There are several well-supported clades , including the microsporidia , the ascomycete fungi , and the basidiomycete fungi , but the fungi do not form a single well-supported clade ( Figure 3A ) , similar to results recovered in earlier AOX phylogenies [27] . The strong support uniting AOX from A . locustae and T . hominis does , however , confirm the microsporidian genes share a single common origin . Phylogenetic analysis based on the conserved region of the gene amplified from other microsporidia similarly places S . lophii and the G . plecoglossi in the same monophyletic microsporidian group ( Figure 3B ) , further supporting the common origin of all microsporidian AOX genes . The overall distribution of microsporidian AOX was therefore mapped onto an SSU phylogeny including all major clades of microsporidia as defined by molecular and ecological data [41] , which showed that AOX is widely distributed in microsporidia , and perhaps only absent from a single clade of predominantly vertebrate and insect parasites , including E . cuniculi , E . bieneusi and N . cerenae ( Figure 3C ) . To directly examine the function of A . locustae and T . hominis AOX proteins ( especially given the sequence difference reported in Figure 1 ) , recombinant A . locustae and T . hominis AOX proteins were expressed in E . coli and the enzyme structure and activity was measured . Antibodies raised against the plant AOX recognize both A . locustae and T . hominis proteins ( Figure 4 ) , and both a monomer and a dimer can be detected in Western blots of non-reducing gels , as is the case within the thermogenic plant Sauromatum guttatum , although in the case of A . locustae the monomer is not very prominent . ( Figure 4 ) . In E . coli membrane fractions containing either A . locustae or T . hominis recombinant AOX ( rAOX ) , ubiquinol-1 oxidase activity indicates that the activities of both proteins are as expected for AOX ( Table 1 ) . In both cases , 1 µM antimycin A , 2 µM myxothiazol and 1 mM potassium cyanide were included in the assay system to ensure inhibition of the cytochrome bo and bd complexes of E . coli , and the specific activities reported in Table 1 have been corrected for auto-oxidation of ubiquinol-1 in the absence of membranes ( see methods ) . It is important to note that , although A . locustae rAOX was more active than T . hominis rAOX , both proteins were equally sensitive to 10 nM ascofuranone ( Table 1 ) , the very specific and potent inhibitor of the trypanosomal alternative oxidase [37] . Furthermore , it is apparent from Table 1 that the specific activities of these microsporidia are considerably higher than those reported for rAOX from C . parvum [17] but comparable to those observed with overexpression studies of T . brucei rAOX in E . coli membranes [42] . The genome of E . cuniculi has served as a model for microsporidian metabolism since it was completed [6] , however , it has never been clear how this model organism dealt with the reducing potential built up through ongoing glycolysis , since it lacks a terminal oxidase . Here we show that this model does not reflect microsporidia as a whole , because alternative oxidase has a broad distribution amongst microsporidian parasites . This distribution remains discontinuous , however , because we can say with some confidence that AOX is not present in either the E . cuniculi or N . ceranae genomes , which have been sequenced to near completion [6] , [32] . It also appears to be absent from the genome of E . bieneusi , although this genome is not completely sampled [31] . Our negative PCR results from E . aedis and A . ( Brachiola ) algerae are less conclusive ( these have previously been shown to have a high AT content that may prevent the successful amplification of the AOX gene by degenerate PCR [43] ) , but it suggests the gene may also be absent in several other lineages . Whilst G . plecoglossi , T . hominis and S . lophii are quite closely related and within the Marinosporidia clade , Antonospora locustae falls within the distantly related Aquasporidia clade as defined by molecular and ecological analysis [41] ( Figure 3C ) . As we know that the alternative oxidase is present in at least two major clades , and in many fungi , the most parsimonious explanation for its distribution in microsporidia is that it was present in their last common ancestor , but has been lost in E . cuniculi and probably other lineages during their more recent evolutionary history . Analysis of the AOX sequences from A . locustae and T . hominis reveals that both possess the iron-and substrate-binding motifs found in other AOXs . In S . guttatum , Tyr-253 has been shown to be involved in substrate binding , and Tyr-275 to be critical for catalytic activity [19] , [44] , and both of these are also conserved in microsporidia . The absences of Trp-206 in A . locustae and T . hominis AOX sequences is somewhat surprising , as it is conserved across all other known mitochondrial AOX sequences . Since A . locustae and T . hominis AOX sequences are demonstrably functional ( Table 1 ) , Trp-206 cannot play a universally critical role in electron transport , but it may have a role in other mitochondrial AOXs as helping to anchor the protein to the leaflet of the inner mitochondrial membrane in a manner seen with other monotopic membrane proteins [19] , [20] , [45] . The demonstration that A . locustae and T . hominis rAOX have a high quinol oxidase activity that is sensitive to ascofuranone at nanomolar concentrations not only solves a significant puzzle in microsporidian metabolism , but also offers a new avenue of treatment for some microsporidian species and further “in tissue culture” trials can establish the efficiency of the drug across the life cycle of the microsporidian . There is currently considerable interest in this antibiotic , originally isolated from the phytopathogenic fungus Ascochyta visiae , for its potential promise in the treatment of trypanosomiasis and cryptosporidiosis . The fact that it also appears to potently inhibit the microsporidian AOX may give the drug a more widespread use than previously considered . Of course several of the microsporidia that parasitise humans lack the AOX ( e . g . E . cuniculi and E . bieneusi ) , but for other human parasites ( e . g . T . hominis ) the AOX is clearly a potential target , and may also be in other unexplored lineages ( e . g . , Vittaforma corneae ) . With respect to the potential function of AOX in microsporidia a possible role may be similar to that proposed in the bloodstream form of some trypanosomes . In the bloodstream form of Trypanosoma brucei , where glucose is abundant and there is no conventional respiratory chain [16] , ATP synthesis is switched from oxidative phosphorylation to substrate level phosphorylation . Glycolysis is contained within a glycosome , a membrane-bound organelle containing glycolytic enzymes . In this system , reducing equivalents generated by glycolysis in the form of glycerol-3-phosphate are shuttled out of the glycosome and re-oxidised by a glycerol-3-phosphate dehydrogenase ( G3PDH ) located on the outer surface of the inner membrane . G3PDH itself reduces the mitochondrial ubiquinone pool that in turn is then re-oxidised by the alternative oxidase . In this way , glycerol-3-phosphate within the glycosome is continuously being re-oxidised to supply further substrate for the net oxidation of NADH [16] . Thus in an indirect manner mitochondrial alternative oxidase activity maintains the NADH/NAD balance within the glycosomes . In addition to the alternative oxidase , however , trypanosomes also possess a glycerol kinase that under anaerobic conditions helps to maintain the glycosome NADH/NAD balance by converting glycerol-3-phosphate to glycerol [16] . It is plausible that most microsporidia rely on a similar system and that AOX fulfils the role of the terminal oxidase , as shown in Figure 5 . Whether the microsporidian AOX functions in the mitosome or cytosol is not completely certain , but its very presence in the cell and its carrying out the functions we have demonstrated in vitro significantly change our view of microsporidian metabolism and drug sensitivity in either event . Overall , the presence of an N-terminal leader with characteristics of a transit peptide , together with the likely mitochondrial origin of the protein , all suggest a mitosomal location is most plausible . This also fits well with previously unusual observations on the glycerol-3-phosphate shuttle . Localization studies on mitochondrial glycerol-3-phosphate dehydrogenase in E . cuniculi show no evidence that the enzyme is confined to mitochondria or specifically localized there , unlike ferredoxin , frataxin , ISCU and ISCS [12] , [13] , and in E . bieneusi the gene appears to be absent altogether [31] . This suggests that the glycerol shuttle has been displaced in these microsporidia , which is functionally consistent with the absence of the alternative oxidase protein in both species . The A . locustae alternative oxidase sequence was retrieved from the GMOD MBL A . locustae database and used to design degenerate primers to amplify a fragment of the alternative oxidase gene from T . hominis , G . plecoglossi and S . lophii ( Forward 5′-GAAACWGTWGCWGCWGTNCCNGG-3′ , Reverse 5′-ATWGCTTCTTCTTCNAKRTANCCNAC-3′ ) . Degenerate PCR was carried out on DNA from E . cuniculi to exclude the possibility that the AOX gene is present in the genome within the subtelomeric regions that have not been fully assembled [6] . This gave negative results . Negative degenerate PCR results were found for Brachiola algerae and Edhazardia aedis . The full-length gene was amplified from T . hominis DNA and RNA obtained from purified spores from cultures maintained in rabbit kidney cells at Rutgers , State University of New Jersey . The 5′ prime end of the gene was amplified using RLM-RACE using primers designed from within the fragment amplified by degenerate PCR . The first round of PCR yielded a product truncated at the 5′ end . Primers were then designed from within that fragment to obtain the presumed full-length gene . A splinkerette strategy was used to obtain 3′ end of the gene [38] . Amplified PCR products were cloned using the TOPO TA cloning system ( Invitrogen ) and sequenced using Big Dye 3 . 2 ( ABI ) . Mitochondrial transit peptides were predicted using Mitoprot I [46] , Predotar [47] , and TargetP 1 . 1 [48] . ( New sequences are deposited in the GenBank Database under the accession numbers GU221909-GU221911 ) . DNA fragments corresponding to A . locustae and T . hominis AOX open reading frames were amplified by PCR by using primers that generated in-frame restriction sites . PCR products were cloned upstream of green fluorescent protein ( GFP ) -S65T under the control of the MET25 promoter [49] for analysis by confocal or fluorescence microscopy . Constructs were then transformed into the diploid yeast strain JK9-3da/a ( leu2-3 , 122/leu2-3 , 122 ura3-52/ura3-52 rme1/rme1 trp1/trp1 his4/his4 GAL+/GAL+ HMLa/HMLa ) , and plated on uracil and methionine deficient SD plates ( 2% ( w/v ) agar , 2% ( w/v ) glucose and 0 . 67% ( w/v ) yeast nitrogen base supplemented with the relevant amino acids ) . Positive colonies were grown overnight in SD medium lacking uracil and methionine and stained with MitoTracker ( MitoTracker Red CM-H2XRos ) according to the manufacturer's protocol ( Molecular Probes ) . Yeast cells were visualized using the Zeiss meta confocal microscope . Separation of yeast mitochondrial proteins on non-reducing SDS-polyacrylamide gels , transfer to nitrocellulose membranes , and detection of AOX protein using monoclonal antibodies raised against the S . guttatum AOX [50] was performed as described previously [51] . The A . locustae and T . hominis gene sequences were amplified using Phusion High-Fidelity Taq ( New England Biolabs ) and cloned into the pet14b expression vector . Both constructs were used to transform E . coli strain C41 , which is especially suited to the expression of transmembrane proteins . Bacterial membranes were prepared using 2 . 5 L Luria broth cultures , adapted from Berthold [52] and as described in detail by Crichton et al 2009 [53] . Flasks containing Luria Broth , 0 . 02% glucose , 0 . 002% FeSO4 and 50 µgml−1 ampicillin were inoculated with 10 mlL−1 starter culture , and incubated at 37°C for 4 hours . The temperature was reduced to 18°C , and the cultures were incubated for one hour prior to induction with 100 µM IPTG . After induction , the cultures were incubated for 18 hours at 18°C . Cells were then harvested using centrifugation at 11 , 000×g for 10 minutes . After initial centrifugation , cells were resuspended in 60 mM Tris-HCl ( pH 7 . 5 ) , 5 mM DTT , 300 mM NaCl and 0 . 1M PMSF and then sonicated for 8 minutes at 14 microns . After sonication , cell debris was removed by centrifugation at 12 , 000×g for 15 minutes , and clear supernatant was further refined by a 2-hour ultracentrifugation step at 200 , 000×g . Pellets from final spin were resuspended in 60 mM Tris-HCl ( pH 7 . 5 ) , 5 mM DTT , 300 mM NaCl and used for subsequent gel and assay analysis . Ubiquinol oxidase activity ( AOX activity ) was measured by recording the absorbance change of ubiquinol-1 at 278 nm ( Cary UV/vis -400 Scan spectrophotometer ) . Reactions were started by the addition of ubiquinol-1 ( final concentration 150 µM , ε278 = 15 , 000 M−1cm−1 ) after 2 min preincubation at 25°C in the presence of rAlAOX and rThAOX in 50 mM Tris-HCl ( pH 7 . 4 ) . Endogenous ubiquinol activities were inhibited by inclusion of 1 µM antimycin A , 2 µM myxothiazol and 1 mM potassium cyanide in the assay medium . The A . locustae and T . hominis AOX amino acid sequences were aligned to 47 diverse proteins sequences with representatives from animal , kinetoplastid , fungal , heterokont , plant and proteobacterial lineages . Sequences were aligned using ClustalW [54] and manually edited and masked . The alignment was analysed using Modelgenerator to select an appropriate model for amino acid change [55] . Phylogenetic trees were inferred using MrBayes 3 [56] with a Blosum62 matrix and with 2 runs each of 1000000 generations carried out on the freely available Bioportal ( www . bioportal . uio . no ) . A burn-in of 400 trees was removed from each run and a consensus created from remaining trees . Five hundred bootstrapped data matrices were also analysed by maximum likelihood using PhyML 3 . 0 [57] with a JTT model of amino acid change and an estimated gamma parameters with four rate categories of amino-acid change . A second alignment restricted to the conserved area amplified by degenerate PCR from S . lophii , G . plecoglossi was also analysed . Trees were inferred and 100 bootstrap datasets analysed from this short alignment using PhyML , using the parameters described above . The SSU rRNA backbone phylogeny was based on available SSU sequences from NCBI , which were aligned using ClustalW , manually edited and masked and analysed using PhyML 3 . 0 with a JC69 nucleotide substitution model with estimated gamma parameter and 4 categories of rate change .
Microsporidia are obligate intracellular parasites responsible for a number of diseases in commercially important animals ( e . g . bees ) and of significant medical concern , in particular in immunocompromised humans . Though related to fungi , microsporidia have undergone a rapid phase of adaption to the intracellular environment and have in the process reduced many aspects of their biology . Notably , microsporidia have highly reduced mitochondria ( powerhouses of the cell ) reflected in reduced energy metabolic pathways . Thus they likely produce ATP only through the process of glycolysis . In some parasites , this glycolytic pathway is dependent on an additional step involving a protein called the “alternative oxidase” . We have shown that this protein is also present in several species of microsporidia . Crucially , this protein is absent from humans and so can potentially be exploited as a drug target . Our experiments show that this protein is likely widespread in microsporidia , and is sensitive to the antibiotic ascofuranone , which is currently being tested as a potential treatment for the agent causing sleeping sickness . Our results suggest that knowledge gleaned from drug trials on sleeping sickness is potentially transferrable to the treatment of some cases of microsporidiosis .
You are an expert at summarizing long articles. Proceed to summarize the following text: The glycosphingolipid isoglobotrihexosylceramide , or isogloboside 3 ( iGb3 ) , is believed to be critical for natural killer T ( NKT ) cell development and self-recognition in mice and humans . Furthermore , iGb3 may represent an important obstacle in xenotransplantation , in which this lipid represents the only other form of the major xenoepitope Galα ( 1 , 3 ) Gal . The role of iGb3 in NKT cell development is controversial , particularly with one study that suggested that NKT cell development is normal in mice that were rendered deficient for the enzyme iGb3 synthase ( iGb3S ) . We demonstrate that spliced iGb3S mRNA was not detected after extensive analysis of human tissues , and furthermore , the iGb3S gene contains several mutations that render this product nonfunctional . We directly tested the potential functional activity of human iGb3S by expressing chimeric molecules containing the catalytic domain of human iGb3S . These hybrid molecules were unable to synthesize iGb3 , due to at least one amino acid substitution . We also demonstrate that purified normal human anti-Gal immunoglobulin G can bind iGb3 lipid and mediate complement lysis of transfected human cells expressing iGb3 . Collectively , our data suggest that iGb3S is not expressed in humans , and even if it were expressed , this enzyme would be inactive . Consequently , iGb3 is unlikely to represent a primary natural ligand for NKT cells in humans . Furthermore , the absence of iGb3 in humans implies that it is another source of foreign Galα ( 1 , 3 ) Gal xenoantigen , with obvious significance in the field of xenotransplantation . Identification of endogenous antigens that regulate NKT cell development and self-recognition represents a major goal in immunology . This unique population of T cells is characterised by expression of an invariant Vα14Jα18 TCR—Vα24Jα18 in humans—and the recognition of glycolipid antigens presented by CD1d [1] . When activated , natural killer T ( NKT ) cells regulate immune responses through their ability to produce large amounts of cytokines such as interferon ( IFN ) -γ and interleukin ( IL ) -4 [2] . NKT cell deficiencies are associated with a range of diseases , including cancer , autoimmunity , and infection , in mice and humans [2] . This , combined with the fact that NKT cell numbers vary widely in humans [3] , highlights the importance of understanding the endogenous antigens in humans that regulate NKT cell development and function . Initial work demonstrated that α-galactosylceramide ( α-GalCer ) , a glycosphingolipid originally derived from a marine sponge [4] , was a potent agonist for NKT cells in a CD1d-dependent manner in both mice and humans [5 , 6] . However , the physiological relevance of this in mammalian systems was difficult to understand because α-GalCer is not a mammalian product . Zhou et al . [7] demonstrated that a deficiency in the lysosomal enzymes β-hexaminidase A and B selectively abrogated NKT cell development , suggesting that glycolipid ( s ) downstream of these enzymes are responsible for NKT cell selection . Experiments to directly test which of the candidate glycolipids were capable of stimulating NKT cells pointed to the glycosphingolipid , isogloboside 3 ( iGb3 ) . Both mouse and human fresh NKT cells , and NKT cell hybridomas and lines , responded to iGb3 , and furthermore , specific inhibition of iGb3 on human cells , using isolectin B4 ( IB4 ) that should selectively target iGb3 via its terminal Galα ( 1 , 3 ) Gal sugars , suggested that iGb3 was also a primary human self-antigen for NKT cells [7] . These data led the authors to suggest that iGb3 was the main endogenous ligand responsible for NKT cell development and self-recognition in both mice and humans . Subsequent studies from independent groups have confirmed that iGb3 is an agonist ligand for at least a subset of mouse and human NKT cells [8–13] , and furthermore , that this glycosphingolipid appears to be important for shaping the NKT cell TCR repertoire in mice [12] . However , recent studies have challenged the hypothesis that iGb3 is the primary ligand responsible for NKT cell development in mice [14–16] . One of these studies [16] failed to detect iGb3 in mouse or human thymus , although this study could not exclude the existence of low levels of iGb3 , or higher levels of iGb3 expressed by a minor subset of the thymus . Another study [15] demonstrated , by using iGb3 synthase ( iGb3S ) knockout mice , that NKT cell development was apparently normal , which more strongly suggested that iGb3 is at least not essential for this process in mice . Lastly , two papers have provided evidence that the defect in NKT cell development in Hex-b–deficient mice may be the lysosomal storage disease that occurs with this mutation , thus causing a nonspecific defect in glycolipid processing and presentation [14 , 17 , 18] . The ability of iGb3 to activate human NKT cells is not in dispute; what remains in question is the role of iGb3 in human NKT cell biology . Another issue of major importance involving iGb3 is from the perspective of xenotransplantation , in which expression of this glycolipid on the cell surface of pig tissue could represent a major problem if it is not present in humans . iGb3 is synthesized by iGb3S , a member of the α1 , 3Gal/GalNAc transferase or Family 6 glycosyltransferases . Other family members include α1 , 3galactosyltransferase ( α1 , 3 GT ) , and the B blood group transferase , which like iGb3S , transfer αGal . In contrast , the two other members of the family , A blood group transferase and Forssman synthetase , transfer αGalNAc . Members of Family 6 are the only known mammalian glycosyltransferases that transfer either αGal or αGalNAc in an α1 , 3 linkage to their respective acceptor molecules . Analysis of the human genome shows the genes for the α1 , 3GT , iGb3S , A/B blood group transferase , and the Forssman synthetase are present [19] . Recently , GT6m7 , a new Family 6 member , was reported [19]; however , in humans , the gene for this glycosyltransferase contains a premature stop codon in the last exon . Indeed , mutation appears to be common in this family , with varying effects ( see Table 1 ) . In a similar fashion to ABO blood groups , in which natural antibodies are made to specificities that individuals lack , humans produce anti-αGal antibodies as a consequence of nonfunctional , or nontranslated enzymes . The evolutionary event that led to selection of the αGal-ve phenotype in humans is not clear , but selective pressure of the αGal+ve protozoan parasites has been postulated [20] . It is well known that the presence of the Galα ( 1 , 3 ) Gal xenoepitope , synthesized by α1 , 3galactosyltransferase ( α1 , 3GT ) , causes hyperacute rejection of donor organs in pig-to-human xenotransplantation [21] . To avoid this problem , the α1 , 3GT gene has recently been deleted in pigs [22] . However , there is still low-level expression of Galα ( 1 , 3 ) Gal [23] , presumably synthesized by iGb3S . Thus , organs from GT−/− pigs transplanted into humans may still potentially be subject to rejection by human natural antibodies to Galα ( 1 , 3 ) Gal in the form of iGb3 . The study from Zhou and colleagues [7] provided data suggesting that this is unlikely to pose an immediate problem , because they showed that human anti-Gal antibodies did not react with iGb3 , presumably because it was a self-ligand that caused deletion of iGb3-reactive lymphocytes in humans . Thus , although there is clearly a significant level of controversy surrounding iGb3 , the fact remains that there are compelling results both for and against a role for iGb3 in NKT cell development . For the sake of understanding the factors that regulate this process , as well as whether iGb3 poses an additional problem for xenotransplantion , further studies are required to resolve this issue . We recently characterized the mouse iGb3S cDNA [24] that encodes the enzyme that synthesizes the Galα ( 1 , 3 ) Gal xenoepitope on iGb3 by catalysing the transfer of donor sugar from UDP-Gal in an α−1 , 3 linkage to its acceptor molecule Galβ ( 1 , 4 ) Glc-ceramide [25] . This reaction is the first step in the isoglobo-series pathway , which also results in the generation of iGb4 and isoForssman glycolipids ( Figure 1 ) . Here , we have examined iGb3S expression and functional potential in human tissues . Moreover , to assess whether iGb3 might represent a xenoantigen that remains in α1 , 3GT knockout pigs , we investigated whether iGb3 glycolipid is recognized by natural human anti-Gal antibodies present in normal human serum , and we also determined whether cells expressing this glycolipid on the cell surface are readily targeted for complement-mediated lysis . Several lines of evidence from our studies of the Galα ( 1 , 3 ) Gal epitope suggest that iGb3S is not expressed in humans . In contrast to both rat and mouse , in which the iGb3S gene is transcribed and the RNA processed [24 , 25] , analysis of a human multiple tissue northern blot did not detect iGb3S mRNA ( unpublished data ) . Furthermore , anti-Gal monoclonal antibodies ( mAbs ) that detect both rat and mouse iGb3 on tissues and cell lines do not react with a range of both normal and malignant human tissues and cell lines [24] ( and our unpublished data ) . To examine expression of iGb3S mRNA in greater detail , reverse-transcription PCR ( RT-PCR ) was used to analyse several human tissues . Oligonucleotide primers for these experiments ( Table S1 ) were designed based on the exon arrangement of the human iGb3S gene , established by the analysis of Genbank DNA sequences . RNA from heart , kidney , spleen , lung , and thymus ( the latter two tissues express iGb3S in both rat and mouse ) generated a product of the correct size ( ∼550 bp ) with forward and reverse primers within exon five ( unpublished data ) . A product was also obtained from cDNA from human dendritic cells ( Figure 2B , lane 6 ) . Some products were confirmed as human iGb3S by direct sequencing ( unpublished data ) . However , generation of products within an exon may be due to genomic DNA or heteronuclear RNA; therefore , amplification across exon boundaries is required to show the presence of mRNA . We have previously shown that iGb3S mRNA can be successfully used as a template for cross-exon RT-PCR from mouse RNA [24] . Despite exhaustive attempts ( at least 50 times ) to amplify human iGb3S using a combination of primers spanning all five exons in different tissues ( Figure 2A ) , products were either not obtained from human template or the size of several of the PCR products corresponded to that expected from genomic DNA rather than spliced mRNA . The data shown are the amplification from dendritic cells ( Figure 2B ) ; however , similar results were also obtained from all tissues examined . Primers across exon 1 to 3 , exons 1 to 4 , and exons 2 to 4 yielded products expected from amplification of genomic DNA rather than spliced RNA ( lanes 1 , 2 , and 4 , respectively , Figure 2B and Table S2 ) . The products observed in lanes 6 and 7 are within a single exon and could represent either genomic or mRNA products as there is no splicing over this region of the iGb3S gene . Despite using numerous primer combinations , including forward primers from exons 2 , 3 , or 4 with a reverse primer from exon 5 ( Figure 2C ) , we were unable to detect any products of the correct size to suggest spliced iGb3S mRNA in any human tissue examined ( Figure 2D ) , even when a high cycle number ( up to 40 ) was used ( unpublished data ) . As expected , products were not observed when template was omitted . From our experience , mouse iGb3S mRNA is expressed at low levels and is difficult to amplify . Therefore , our inability to detect human iGb3S mRNA was not conclusive evidence that it was absent . Transfection of CHOP cells with mouse iGb3S cDNA results in high level expression of its product Galα ( 1 , 3 ) Gal [24] . We used the same approach to determine whether humans express functional iGb3S . In vitro functional studies indicate that the catalytic domain of iGb3S ( which represents 75% of the entire molecule ) is encoded by two exons , a small exon ( exon 4 ) and a larger one ( exon 5 ) encoding the major part of the functional domain [26] . Soluble forms of the truncated catalytic domain of several members of the glycosyltransferase family have been shown to be enzymatically active . Using splice overlap extension PCR , we initially generated a chimeric molecule in which exon 5 of the functional rat iGb3S was substituted with that of the human iGb3S homolog ( generated from human genomic DNA ) ( Figure 3A and Table S3 ) . The other rat exons encode the cytoplasmic tail , transmembrane domain , and stalk that anchors the molecule in the lipid bilayer . This approach of exchanging catalytic domains to examine function has been successfully used with Forssman synthetase , another member of this glycosyltransferase family [27] . The ability of this chimeric rat/human ( exon5 ) -iGb3S molecule to synthesize Galα ( 1 , 3 ) Gal was determined by analysis of transfected CHOP cells . As expected , cells transfected with DNA encoding rat iGb3S displayed strong cell surface expression of the Galα ( 1 , 3 ) Gal epitope on glycolipid as determined by binding of the monoclonal antibody 15 . 101 [28] and human anti-Gal immunoglobulin ( Ig ) purified from normal human serum ( Figure 3B ) . The 15 . 101 mAb has been shown to bind preferentially to Galα ( 1 , 3 ) Gal on iGb3 lipid [28] . The chimeric molecule containing the majority of the catalytic domain of human iGb3S ( rat/human ( exon5 ) -iGb3S ) was unable to synthesise the Galα ( 1 , 3 ) Gal epitope as staining was not observed with 15 . 101 or human anti-Gal Ig ( Figure 3B ) . A second chimeric molecule comprising the entire human catalytic domain , exon 4 together with exon 5 ( rat/human ( exon4 , 5 ) -iGb3S ) , was also unable to synthesize Galα ( 1 , 3 ) Gal ( Figure 3B ) . Data from several other mAbs and Bandeiraea simplicifolia IB4 lectin that bind the Galα ( 1 , 3 ) Gal epitope ( Figure S1 ) support the conclusion that the human iGb3S catalytic domain is not functional . Detection of the FLAG epitope in both chimeric enzymes confirmed that the absence of Galα ( 1 , 3 ) Gal synthesis was not due to impaired translation or expression ( Figure S2A ) . As glycosyltransferases are integral membrane proteins of the Golgi complex where oligosaccharides are synthesized , perinuclear staining ( Golgi-like ) confirmed correct trafficking of the chimeric enzymes ( Figure S2B ) . Staining was not observed with cells transfected with vector alone . To explore the unlikely possibility that iGb3 staining was not observed due to antibody inaccessibility , an alternative detection method was used . Synthesis of iGb3 is the initial step for the formation of the isoglobo-series glycolipid pathway , and iGb3 is the precursor to iGb4 and , ultimately , isoForssman [25] ( see Figure 1 ) . To examine whether iGb3 was synthesized , a complementation assay in CHOP cells , which lack both iGb3S and Forssman synthetase ( FS ) , was used to determine whether coexpression of chimeric iGb3S with FS results in expression of isoForssman glycolipid . As expected , cells transfected with FS alone did not stain for isoForssman ( unpublished data ) , whereas cells transfected with both rat iGb3S and FS were positive for isoForssman ( Figure 3C ) . Expression of Galα ( 1 , 3 ) Gal was confirmed by 15 . 101 binding . Cells cotransfected with either of the chimeric molecules ( rat/human ( Exon5 ) -iGb3S or rat/human ( Exon4 , 5 ) -iGb3S ) and FS did not show any detectable isoForssman staining ( Figure 3C ) . As expected , no Galα ( 1 , 3 ) Gal was observed following staining with 15 . 101 . Thus , the human catalytic domain appears to be incapable of generating detectable iGb3 and does not initiate the downstream synthesis of the iGb4 structure required for FS to function . Using site-directed mutagenesis , we analysed which amino acid ( s ) contributed to the loss of function we observed in human iGb3S . Despite an overall similarity of approximately 72% , there are 77 differences within the catalytic domain of the functional rat iGb3S and nonfunctional human iGb3S , any of which , either alone or in combination , may be involved in the loss of function observed with human iGb3S . The targeted residues were selected by comparison of the aligned amino acid sequences of the iGb3S catalytic domains ( exon 4 and 5 encoded ) of species known to synthesize iGb3 ( rat , mouse , and dog ) with that of human . To identify more precise candidates , amino acids were excluded: ( 1 ) if the human amino acid was identical to either the mouse or dog , ( 2 ) the amino acid residue was different in all four species , or ( 3 ) the substitution was with an homologous amino acid . Four of these amino acids in rat exon 5 were selected in these initial studies and mutated to their human equivalent ( Figure 4A ) . The single isolated substitution of rat Y252N resulted in the complete elimination of Galα ( 1 , 3 ) Gal staining ( Figure 4B ) , showing that this asparagine in human iGb3S is sufficient to ablate enzymatic function . Rat L187P showed a significant reduction ( typically 70%–95% ) in Galα ( 1 , 3 ) Gal staining , whereas both the rat A221S and rat E280A substitutions showed strong Galα ( 1 , 3 ) Gal expression that was comparable with that observed following transfection with rat iGb3S ( Figure 4B ) . As expected , a complementation assay with FS resulted in strong isoForssman staining with both rat A221S and rat E280A substitutions ( Figure 4C ) . A similar high level of isoForssman staining was also observed with the rat L187P substitution , despite there being minimal Galα ( 1 , 3 ) Gal expression , thus demonstrating the sensitivity of this method . IsoForssman staining was not observed with cells cotransfected with rat Y252N ( Figure 4C ) . It is possible that the Y252N and L187P substitutions are not the only ones in humans that influence function . This was examined by reverse mutation of the nonfunctional chimeric rat/human ( exon 5 ) -iGb3S to their rat equivalents with either point mutation alone ( i . e . , P187L or N252Y ) , or in combination ( P187L+N252Y ) . A gain of function would suggest these are the primary residues involved in determining whether the transferase is functional . Staining with mAb 15 . 101 showed no Galα ( 1 , 3 ) Gal expression following transfection of CHOP cells with either the single or combined reverse-mutated chimeric cDNA molecules ( Figure 5 ) . The implications of these data are that human iGb3S must have multiple mutations that have resulted in its inactivation . Typical strong Galα ( 1 , 3 ) Gal expression was observed with cells transfected with rat iGb3S . High-performance thin layer chromatography data from Zhou et al [7] suggested natural mixed human serum antibodies did not recognize iGb3 , suggesting that iGb3-reactive B cells had been deleted from the human repertoire , further evidence that iGb3 lipid is present in humans . To test this ourselves , we used a lipid ELISA , and by this approach , demonstrated clear binding of both natural human anti-Galα ( 1 , 3 ) Gal antibodies and the mAb 15 . 101 to purified iGb3 lipid over several antibody dilutions ( Figure 6A ) . Binding was not observed with either anti-CD17 ( lactosylceramide ) or anti-CD77 ( Gb3 ) mAbs . Furthermore , as a specificity control , treatment of iGb3 lipid with α-galactosidase , which specifically removes the terminal α ( 1 , 3 ) Gal moiety , resulted in a significant inhibition ( up to 60% ) of binding by natural human anti-Galα ( 1 , 3 ) Gal antibodies ( Figure 6B ) , yet had no effect on anti-CD17 binding to lactosylceramide ( no αGal moiety ) in a parallel assay . Antibody specificity was further demonstrated by the lack of binding of natural human anti-Galα ( 1 , 3 ) Gal antibodies to either Gb3 ( Figure 6C ) or lactosylceramide ( Figure 6D ) . However , as expected specific binding of both anti-CD77 and anti-CD17 mAbs ( used at the same dilutions as in Figure 6A ) were observed ( Figure 6C and 6D , respectively ) . A key question that remains to be answered is , if human cells were to express iGb3 , would they be susceptible to antibody-dependent complement-mediated lysis due to natural human anti-αGal antibodies present in normal human serum ( NHS ) ? In contrast to nontransfected human cells ( αGal−ve ) that do not undergo lysis with NHS , human cells expressing iGb3 ( αGal+ve ) were lysed by NHS ( in the presence of rabbit complement ) in a dose-dependent manner ( Figure 7A ) . Removal of anti-αGal antibodies from NHS by absorption with Galα ( 1 , 3 ) Gal coupled to glass beads abolished lysis to background levels ( Figure 7A ) . However , lysis was not affected by NHS absorption with uncoupled glass beads ( unpublished data ) . Furthermore , lysis of human cells expressing iGb3 was re-established when anti-αGal IgG antibodies were purified from NHS and used in the cytotoxicity assay ( Figure 7A ) . In addition , this activity could be removed by absorption with Galα ( 1 , 3 ) Gal coupled to glass beads ( unpublished data ) . Inhibition experiments verified that Galα ( 1 , 3 ) Gal is the epitope that the antibodies recognise , as a significant dose-dependent reduction in lysis was observed by preincubation of both NHS and anti-αGal IgG antibodies with Galα ( 1 , 3 ) Gal disaccharide ( Figure 7B ) . No inhibition was observed when lactose ( Galβ ( 1 , 4 ) Glc ) was used ( Figure 7B ) . Glycolipids represent one of the last molecular frontiers in immunological recognition . Whereas glycolipids are known to be synthesized in the Golgi and are typically expressed on the cell surface , the exact transport pathway ( s ) for newly synthesized glycolipids is not well defined . However , it is assumed to be similar to glycoproteins and involve vesicular flow from the endoplasmic reticulum through the Golgi complex to the plasma membrane . Glycolipids , particularly exogenous glycolipids , can localize to lysosomal compartments via endocytosis . Similarly , our knowledge of how glycolipids control immune responses and the context in which they are presented by CD1d and recognized by NKT cells is also still very limited . Since the glycolipid , α-GalCer , was originally shown to potently stimulate NKT cells in a CD1d-dependent manner , there has been an enormous effort to identify other ligands . Several classes of natural CD1d-binding ligands for NKT cells have been identified , including microbial-derived α-linked glycosphingolipids from the nonpathogenic Sphingomonas bacteria and phosphatidylinositol mannoside from Mycobacteria ( reviewed in [29] ) . Recently , a diacylglycerol glycolipid from Borrelia burgdorferi , a human pathogen responsible for Lyme disease , was shown to directly stimulate both human and mouse NKT cells [30] . Although these ligands are all candidates for NKT cell recognition of non-self , none of these are present in normal mammalian cells . The main candidate self glycolipid-antigen is iGb3 . The original collective data , primarily based on the use of β-hexosaminidase-B–deficient mice that are incapable of degrading iGb4 into iGb3 in lysosomes , supported the claim that iGb3 lipid was a principle endogenous ligand for Vα14 NKT cells in mice and , albeit indirectly , in humans [7 , 12 , 31] . The interpretation of data using β-hexosaminidase-B–deficient mice was contested by Gadola et al . [14] , where it was argued that these mice have a generalised lysosomal storage disease that indirectly impaired CD1d loading in lysosomes . Their interpretation was that it was the accumulation of glycolipids in lysosomes , rather than the lack of iGb3 , that abrogated NKT cell development . Some of the data in this paper [14] simply conflicted with that of the earlier study of β-hexosaminidase-B–deficient mice [7] , making it difficult to determine which interpretation was correct [14] . Similar suggestions were raised in an independent study of mutations leading to lysosomal storage diseases [18] . Recently , Porubsky et al . and Speak et al . [15 , 16] failed to detect iGb3 in mouse and human thymus using a biochemical approach . Furthermore , Porubsky et al . [15] reported normal development and function of invariant NKT ( iNKT ) cells in iGb3S−/− mice . Although the lack of biochemical evidence for iGb3 in thymus might simply be an issue of insufficient sensitivity , the results from the iGb3S−/− mice more strongly challenge the significance of iGb3 in mouse NKT cell development . There is no easy interpretation that incorporates and integrates the findings from the studies for , and against , a role for iGb3 in mouse NKT cell development . In our opinion , this represents one of the most important and controversial issues in the NKT cell field that requires additional input from independent research groups . In humans , synthetically derived iGb3 can stimulate human NKT cells to proliferate and produce cytokines [7 , 8 , 32] and recognition of human dendritic cell self-antigen can be blocked by IB4 lectin [7] . However , direct biochemical evidence to show that human iGb3 is an endogenous NKT cell ligand has been lacking . Although iGb3 was not detected in human thymus or human dendritic cells using a high-performance liquid chromatography ( HPLC ) assay , this assay had a detection limit of 1% iGb3 to 99% Gb3 , which does not exclude the presence of iGb3 at low but still biologically significant levels [16] . Indeed , during review of this manuscript , two publications from Li et al . , claimed to be able to discriminate iGb3 from Gb3 ( in artificial mixtures and from rat cells ) and identified iGb4 from human paediatric thymi , using electrospray ionisation-ion trap mass spectrometry [33 , 34] . Although these analyses are at odds with our own , they have yet to conclusively demonstrate immunologically significant levels of iGb3 in human tissue . Specifically , the formal possibility remains that the minor MSn mass spectral signature for iGb4 detected in these studies is derived from related tetraglycosylceramides , as acknowledged by these investigators . Alternatively , the very low levels of iGb4 detected in these analyses may be derived from dietary sources and distributed throughout the body via lipoprotein particles . The presence or absence of iGb3 in humans has potential major implications for xenotransplantation . If humans express iGb3S , iGb3 lipid present on transplanted pig tissues will not be “seen” as foreign and therefore would not represent a drawback for xenotransplantation . However , as humans do not express functional iGb3S ( reported herein ) , then the presence of lipid-linked Galα ( 1 , 3 ) Gal in pigs , synthesized by iGb3S , may pose a serious risk to successful xenotransplantation , even when using α1 , 3GT knockout pigs as donors ( which were specifically generated to eliminate Galα ( 1 , 3 ) Gal epitopes for xenotransplantation purposes ) . What are the implications of this in a transplant setting ? Currently , we know that expression of iGb3 does not mediate hyperacute rejection of pig tissues transplanted into baboons [35 , 36] . However , human serum has at least a 4-fold higher level of natural anti-Galα ( 1 , 3 ) Gal antibodies ( ∼1% of human IgG ) than other primates [37] , so this may not directly represent the human situation . Furthermore , iGb3 expression in pigs may have more serious consequences in the later phases of graft rejection . Firstly , changes in the affinity/avidity of the elicited antibodies may cause tissue damage by complement fixation . It is clear that the level of anti-Galα ( 1 , 3 ) Gal antibody is critical for the speed of rejection in experimental models [38] . Alternatively , elicited anti-Galα ( 1 , 3 ) Gal antibodies may contribute to the acute vascular rejection observed when hyperacute rejection is eliminated , such as in knockout pig-to-primate transplants , by activation of endothelial cells via cross-linking of the lipid itself . Pathological features similar to acute vascular rejection are seen in humans when the Gb3 lipid ( closely related to iGb3 ) is cross-linked by bacterial toxins [39] . Secondly , because iGb3 activates human NKT cells [7 , 32] ( in which synthetic , purified . and enzymatically derived iGb3 were all tested ) , consequently the expression of iGb3 on pig cells could lead to NKT cell activation resulting in destruction of the xenograft . Furthermore , our data clearly show that the anti-Gal antibodies in NHS can lyse iGb3 expressing cells ( Figure 7 ) and therefore any remaining iGb3 on pig cells may be a target for antibody-mediated destruction . Whereas it is clear that the use of heavy immunosuppression can control the later phases of xenograft rejection , the major advantage of xenotransplantation over allotransplantation is the ability to genetically modify the donor . The ultimate goal is to engineer a donor pig such that minimal , or indeed no immunosuppression is required for long-term graft survival . It is likely that genetic modification of pigs may be required to eliminate any effects of iGb3 . Only at that stage will other obstacles be revealed . Thus , in formally demonstrating the lack of functional iGb3S in humans , this study alerts transplantation immunologists to a previously unrecognised risk associated with expression of iGb3 glycolipid on α1 , 3GT knockout pig tissues . Expression of this glycolipid could act as a secondary source of Galα ( 1 , 3 ) Gal xeno-antigen capable of binding natural human anti-Gal antibodies present in normal human serum and marking these cells for destruction by complement mediated lysis . In a perspectives article , Godfrey , Pellicci , and Smyth [40] asked whether the search for the elusive NKT cell antigen is over . In mice , in view of several recent publications [14–16] , the possible existence of NKT cell-selecting ligands other than iGb3 remains an important consideration [17] . It had generally been assumed that experimental data obtained from mice would be directly relevant to humans , and Zhou et al . [7] provided indirect evidence that iGb3 is also a self-ligand for human NKT cells . However , our new data demonstrate that there appears to be critical differences between the two systems , and suggests that we are a long way from calling off the search for NKT cell-selecting antigens in humans . This remains one of the most important objectives in the field , and will ultimately lead to a better understanding of the factors that regulate NKT cell development and function in health , and in developing novel therapies for the treatment of disease . The human genomic iGb3S sequence was obtained from the National Center for Biotechnology Information Web site ( http://www . ncbi . nlm . nih . gov ) and searching the human genomic database . The nucleotide sequence of the gene A3GALT2 ( accession number NT 032977 ) was used , with the exon/intron boundaries for the human iGb3S gene as listed with the sequence . We were unable to clone human iGb3S from total RNA from adult human tissues ( heart , lung , kidney , spleen , and thymus ) ( Stratagene ) or cDNA from dendritic cells using the TITANIUM One-Step RT-PCR Kit ( Clontech ) with a series of degenerate primers ( Tables S1 and S2 ) . The chimeric rat/human molecules included the exchange of rat exon 5 ( rat/human ( exon5 ) -iGb3S ) and rat exons 4 and 5 ( rat/human ( exon4 , 5 ) -iGb3S ) with the equivalent human exon ( s ) . The rat/human chimeras were generated using splice overlap extension PCR . The specific primer combinations used are shown in Table S3 . The single amino acid substitutions in rat iGb3S ( L187P , A221S , Y252N , and E280A ) and the reverse mutations in rat/human ( exon5 ) -iGb3S ( P187L , N252Y , and the combined P187L+N252Y ) were introduced using the QuikChange site-directed mutagenesis kit ( Stratagene ) ( Table S4 ) . Sequence fidelity , orientation of the insert , and presence of the desired mutation ( s ) were confirmed by DNA sequencing ( Big Dye 3 . 1; PE-Applied Biosystems ) . CHOP cells ( Chinese Hamster Ovary cells transformed with Polyoma Large T antigen ) [41] and E293 cells ( human kidney fibroblasts ) were cultured in DMEM ( CSL ) supplemented with 10% FCS overnight at 37 °C . Transfections were with LipofectAMINE Plus ( Life Technologies ) as recommended by the manufacturer . Cells were examined after 48 h for either cell surface or intracellular expression of Galα ( 1 , 3 ) Gal using purified natural human anti-Gal antibodies ( 0 . 49 mg/ml ) , and the anti-Galα ( 1 , 3 ) Gal mAbs 15 . 101 , 22 . 121 , 24 . 7 , 25 . 2 , and 8 . 17 ( supernatants ) [24 , 42] and Bandeiraea simplicifolia IB4 lectin . Expression of the FLAG epitope was revealed by staining with the anti FLAG M2 mAb ( Sigma ) . Expression of IsoForssman glycolipid was revealed using an anti-Forssman mAb , FOM-1 ( BMA Biomedicals ) . Expression of lactosylceramide and Gb3 were revealed with anti-CD17 ( ascites ) and purified anti-CD77 ( 0 . 15 mg/ml ) mAbs , respectively ( Pharmingen ) . Antibodies were detected with FITC-labelled sheep anti-mouse or human IgG ( Dako ) or HRP conjugate Sheep anti-human Ig ( Silenus ) and rabbit anti-mouse Ig ( Dako ) , and analysed either by fluorescence microscopy , flow cytometry ( Becton Dickinson FACS Canto II ) , or lipid ELISA . Porcine lactosylceramide ( Calbiochem ) and iGb3 ( Alexis Biochemicals ) were dissolved in methanol at 1 mg/ml and stored at −20 °C . The ELISA was performed in 96-well Maxisorb plates ( Nunc ) . Lipids were diluted in n-hexane and used at 500-ng/well , incubated for 1 h in a fume hood to dry; plates were then blocked with 3% BSA/PBS for 2 h and washed ×1 with PBS . Primary antibodies , diluted in blocking buffer , were added and incubated for 1 h . After washing ×5 with PBS , secondary antibodies , diluted in blocking buffer , were added and incubated for 1 h before washing ×8 with PBS . All incubations were carried out at room temperature ( RT ) on a rocking platform . TMB peroxidase substrate ( KPL ) was used to develop the plate . Colour development was stopped with 0 . 18 M H2SO4 and quantitated at an optical density at 405 nm ( OD405nm ) on an ELISA plate reader . For the enzyme digestion , α-galactosidase ( Sigma-Aldrich ) was diluted in 0 . 1 M citrate/phosphate buffer ( pH 6 ) and incubated with the lipids overnight at RT , after which an ELISA was performed as described above . Human E293 cells expressing rat iGb3 [28] were tested for lysis with rabbit complement and normal human sera ( NHS , pooled from ten healthy individuals and heat inactivated ) or purified human anti-Gal IgG antibodies ( prepared by fractionation of the NHS pool on a Protein G Sepharose column ( Pharmacia ) followed by affinity chromatography on Galα ( 1 , 3 ) Gal-coupled macroporous glass beads ( Syntesome ) as described previously [43] . In brief , 50 μl of antibody at doubling dilutions were added to 2 . 5 × 105 cells per well in round-bottomed 96-well plates ( Greiner ) , resuspended , and incubated on ice for 30 min . After two washes , 50 μl of rabbit complement , at an appropriate dilution , was added to the cell pellet , resuspended , and incubated at 37 °C for 30 min ( NHS ) or 60 min ( purified anti-Gal IgG ) . Cells were pelleted and resuspended in 400 μl of DMEM/0 . 5% BSA containing 1 μg/ml propidium iodide ( PI; Sigma ) and analysed by flow cytometry . Percentage lysis ( cytotoxicity ) was determined by analysis of 10 , 000 cells . The importance of anti-Gal antibodies for lysis was determined by serum absorption and carbohydrate inhibition: ( 1 ) Absorption; 200 μl of NHS or human anti-Gal IgG was added to an equal volume of Galα ( 1 , 3 ) Gal-coupled macroporous glass beads or non-coupled beads ( control ) at 4 °C for 30 min; the beads were removed by centrifugation and the absorption step repeated with another aliquot of beads . ( 2 ) Carbohydrate inhibition; 25 μl of 20 mM Galα ( 1 , 3 ) Gal disaccharide or lactose ( control ) was serially diluted and mixed with an equal volume of NHS or human anti-Gal IgG at an appropriate dilution ( two dilutions less than the 50% titre of the antibody ) and incubated at 4 °C for 16 h . After both of these treatments , the sera were analysed for complement-mediated lysis .
Identification of endogenous antigens that regulate natural killer T ( NKT ) cell development and function is a major goal in immunology . Originally the glycosphingolipid , iGb3 , was suggested to be the main endogenous ligand in both mice and humans . However , recent studies have challenged this hypothesis . From a xenotransplantation ( animal to human transplants ) perspective , iGb3 expression is also important as it represents another form of the major xenoantigen Galα ( 1 , 3 ) Gal . In this study , we assessed whether humans expressed a functional iGb3 synthase ( iGb3S ) , the enzyme responsible for lipid synthesis . We showed that spliced iGb3S mRNA was not detected in any human tissue analysed . Furthermore , chimeric molecules composed of the catalytic domain of human iGb3S were unable to synthesize iGb3 lipid , due to at least one amino acid substitution . We also demonstrated that purified human anti-Gal antibodies bound iGb3 lipid and mediated destruction of cells transfected to express iGb3 . A nonfunctional iGb3S in humans has two major consequences: ( 1 ) iGb3 is unlikely to be a natural human NKT ligand and ( 2 ) natural human anti-Gal antibodies in human serum could react with iGb3 on the surface of organs from pigs , marking these tissues for immunological destruction .
You are an expert at summarizing long articles. Proceed to summarize the following text: Nidoviruses with large genomes ( 26 . 3–31 . 7 kb; ‘large nidoviruses’ ) , including Coronaviridae and Roniviridae , are the most complex positive-sense single-stranded RNA ( ssRNA+ ) viruses . Based on genome size , they are far separated from all other ssRNA+ viruses ( below 19 . 6 kb ) , including the distantly related Arteriviridae ( 12 . 7–15 . 7 kb; ‘small nidoviruses’ ) . Exceptionally for ssRNA+ viruses , large nidoviruses encode a 3′-5′exoribonuclease ( ExoN ) that was implicated in controlling RNA replication fidelity . Its acquisition may have given rise to the ancestor of large nidoviruses , a hypothesis for which we here provide evolutionary support using comparative genomics involving the newly discovered first insect-borne nidovirus . This Nam Dinh virus ( NDiV ) , named after a Vietnamese province , was isolated from mosquitoes and is yet to be linked to any pathology . The genome of this enveloped 60–80 nm virus is 20 , 192 nt and has a nidovirus-like polycistronic organization including two large , partially overlapping open reading frames ( ORF ) 1a and 1b followed by several smaller 3′-proximal ORFs . Peptide sequencing assigned three virion proteins to ORFs 2a , 2b , and 3 , which are expressed from two 3′-coterminal subgenomic RNAs . The NDiV ORF1a/ORF1b frameshifting signal and various replicative proteins were tentatively mapped to canonical positions in the nidovirus genome . They include six nidovirus-wide conserved replicase domains , as well as the ExoN and 2′-O-methyltransferase that are specific to large nidoviruses . NDiV ORF1b also encodes a putative N7-methyltransferase , identified in a subset of large nidoviruses , but not the uridylate-specific endonuclease that – in deviation from the current paradigm - is present exclusively in the currently known vertebrate nidoviruses . Rooted phylogenetic inference by Bayesian and Maximum Likelihood methods indicates that NDiV clusters with roniviruses and that its branch diverged from large nidoviruses early after they split from small nidoviruses . Together these characteristics identify NDiV as the prototype of a new nidovirus family and a missing link in the transition from small to large nidoviruses . Viruses employing positive-sense , single-stranded RNA genomes ( ssRNA+ ) form the most abundant class and its members are known to infect all types of hosts except Archaea . They have evolved genome sizes in the range of ∼3 . 0 to 31 . 6 kb ( Fig . 1 ) . This size range is the largest among those of the different classes of RNA viruses , although it is small compared to those of DNA viruses and cellular organisms . These profound genome size differences between RNA and DNA life forms are inversely correlated with mutation rates , which are highest in RNA viruses , thought due to the lack of proofreading during replication [1]–[3] . Recently , the molecular basis of the relation between RNA virus genome sizes and mutation rates has been revisited in studies of nidoviruses with large genomes ( “large nidoviruses” ) . These viruses , with genomes of 26 . 3 to 31 . 6 kb , include the Coronaviridae and Roniviridae families and are at the upper end of the RNA virus genome size range [4] . They are uniquely separated from other ssRNA+ viruses ( 3 . 0–19 . 6 kb genomes ) , including the distantly related Arteriviridae family ( 12 . 7–15 . 7 kb genomes; “small nidoviruses” ) with which they form the order Nidovirales [4]–[6] . The order includes five major lineages of viruses that infect vertebrate and invertebrate hosts . Their complex genetic architecture includes multiple open reading frames ( ORFs ) that are expressed by region-specific mechanisms . The first two regions are formed by the two 5′-most and partially overlapping ORFs , ORF1a and ORF1b , which are translated from the genomic RNA to produce polyproteins 1a ( pp1a ) and pp1ab . The expression level of ORF1b is downregulated relative to that of ORF1a by the use of the ORF1a/1b ribosomal frameshifting signal [7] , [8] . Both pp1a and pp1ab are autoproteolytically processed by ORF1a-encoded proteases to yield numerous products that control genome expression and replication [9] . The third , 3′-located region of the nidovirus genome includes multiple smaller ORFs ( 3′ORFs ) , although the number of these ORFs varies considerably among nidoviruses . These genes are expressed from 3′-coterminal subgenomic mRNAs to produce the structural proteins incorporated into the enveloped nidovirus particles and , optionally , other proteins modulating virus-host interactions [10]–[13] . With the exception of a few nidoviruses , the subgenomic and genomic mRNAs are also 5′-coterminal . A mechanism of discontinuous negative-stranded RNA synthesis , yielding the templates for subgenomic mRNA production , is thought to control this mosaic structure of nidovirus mRNAs . The synthesis of subgenome-length negative stranded RNAs is guided by short transcription-regulating sequences ( TRSs ) – located in the common “leader sequence” ( near the genomic 5′ end ) and in each “mRNA body” ( upstream of the expressed ORFs ) - that share a conserved core sequence and flank the genome region that is not present in the respective subgenomic mRNAs . The nidovirus ORF1b encodes key replicative enzymes whose number and type vary between the major nidovirus lineages . They invariably include an RNA-dependent RNA polymerase ( RdRp ) and a superfamily 1 helicase ( HEL1 ) [14] , which are most common in other RNA viruses , and several other RNA-processing enzymes that are either unique to nidoviruses ( uridylate-specific endonuclease ( NendoU ) and 3′-to-5′exoribonuclease ( ExoN ) ) or rarely found outside nidoviruses ( 2′-O-methyltransferase ( OMT ) ; [4] ) . Among these enzymes , the ExoN domain has properties that are most relevant for understanding the relation between genome size and mutation rate in RNA viruses . Bioinformatics-based analysis originally identified the ExoN domain only in the genomes of large nidoviruses and mapped it in the vicinity of HEL1 , a key replicative enzyme [15] . It also revealed a distant relationship between ExoN and a cellular DNA-proofreading enzyme . Based on these observations , nidoviruses were proposed to have acquired ExoN to control the replication fidelity of their expanding genome [15] . The enzymatic activities of ExoN were subsequently verified and detailed in biochemical studies [16] , [17] . Likewise , and in line with the expectations , ExoN-inactivating mutations were shown to decrease RNA replication fidelity by ∼15–20 fold in two coronaviruses , mouse hepatitis virus ( MHV ) and SARS coronavirus ( SARS-CoV ) , while only modestly affecting virus viability [18] , [19] . These results strongly support a critical role of ExoN in the control of replication fidelity of large nidoviruses , although more mechanistic insight is clearly required before the current paradigm connecting RNA virus mutation rates and genome size control could be definitively revised to include proof-reading during the replication of large RNA genomes [20] . Major advancements toward this goal are expected to come from studies of the structure and function of ExoN , which aim to elucidate the molecular mechanism of its action . In addition , genomics studies could contribute to this quest by providing insights into the role of ExoN in RNA virus evolution . Accordingly , if ExoN was acquired to ensure the expansion of RNA genomes beyond a certain size , we may expect ( i ) a genome size threshold that separates RNA viruses with and without ExoN; ( ii ) all nidoviruses with genome sizes above this threshold to encode ExoN; and ( iii ) no other domain than ExoN to correlate , functionally and phyletically , with genome size control in large nidoviruses . In this respect , the characterization of nidoviruses with a genome size in the gap that currently separates small and large nidoviruses should , in theory , be particularly insightful . However , whether these viruses actually exist has thus far remained an open question . Three considerations suggest that if nidoviruses with intermediate-sized genomes ever evolved they may already have gone extinct . First , it is recognized that the evolution of RNA viruses is characterized by a high birth-death rate and the extinction of numerous virus lineages , resulting in the fast turnover of species [21] . Secondly , the genome size gap between large nidoviruses and all other known ssRNA+ viruses has existed without exception since genome sequencing began in the 1980s . As of the late 1980s , this gap has been bordered by closteroviruses ( from the bottom ) and nidoviruses ( from the top ) ( Fig . 1 ) . Likewise and thirdly , all nidovirus genomes sequenced to date have sizes that are similar to either IBV ( 27 , 600 nt ) [22] or EAV ( 12 , 700 nt ) [23] , which were the first fully sequenced coronavirus and arterivirus genomes , respectively . The evident under-representation of RNA viruses with relatively large genomes is even more striking in the light of the continuous flow of newly identified ssRNA+ viruses with smaller genome sizes [24] ( Fig . 1 ) . In sharp contrast to these considerations and prior observations , we here report the discovery of a nidovirus with a genome size that is intermediate between those of small and large nidoviruses . This elusive and precious evolutionary link is an insect-borne virus with the largest ssRNA+ genome for any insect virus known to date . Comparative genome analyses involving this newly identified virus provide evolutionary evidence for the acquisition of the ExoN domain by a nidovirus ( ancestor ) with a genome size in the range of ∼16–20 kb . This range appears to define the size limit for the expansion of ssRNA+ virus genomes , which may be achieved in evolution without the recruitment of a specialized enzyme that controls replication fidelity . Furthermore , we found that two other replicative enzymes , N7-methyltransferase ( NMT ) and NendoU , are not encoded by toroviruses and invertebrate nidoviruses , respectively , indicating that they may contribute “optional activities” for the nidovirus replication machinery . Together our results highlight the broad benefits of virus discovery efforts applied to mosquitoes . In Vietnam , between 2 , 000 and 3 , 000 cases of acute encephalitis syndrome ( AES ) are reported annually , of which about 40% are confirmed to be associated with Japanese encephalitis virus ( JEV ) . The etiological agent ( s ) in the other 60% of cases remains unknown [25] , but they share demographic characteristics and seasonality with the JEV cases . Hence , the involvement of other arboviruses in non-JE AES was postulated and the virus described in this paper was identified in search of such pathogens , which may infect both humans and mosquitoes . During continued JEV surveillance between September 2001 and December 2003 , 359 pools containing one of six mosquito species ( see Materials and Methods ) were collected indoors in Northern and Central Vietnam at one- to three-month intervals . The study areas included Hanoi and other cities located in the provinces of Ha Nam ( Chuyenngoan , Mocbac ) , Ha Tay ( Catque , Phuman and Chuongmy ) , Nam Dinh , and Quang Binh ( Fig . 2 ) . The majority of Catque inhabitants are farmers who cultivate rice in watered paddy fields and raise pigs . Phuman and Quangbinh , however , are highlands . Mosquito pools were tested for the presence of viruses using infection of different cell lines as a read-out assay . Homogenates that were prepared from some pools containing Culex tritaeniorhynchus and Culex gelidus induced cytopathic effects in the C6/36 mosquito cell line . Most of these were attributed to JEV ( 24 different strains; data not shown ) , but for 10 specimens a routine laboratory screening for JEV and other circulating flaviviruses ( such as Dengue and West Nile viruses ) by RT-PCR and/or serology yielded negative results . Subsequently , infected culture fluid ( ICF ) from cells infected with unknown agents were analyzed by electron microscopy , which revealed an enveloped virus with a diameter of 60–80 nm ( Fig . 3A ) . This virus was named Nam Dinh virus ( NDiV ) , after the geographic locality of its first apparent isolation , although this origin could not be confirmed later on . However , for historical reasons , this name was retained for all subsequent isolates , and the analysis of one of those ( 02VN178 ) is described here . NDiV was identified in four mosquito pools , two from Culex vishnui and two from Culex tritaeniorhynchus , collected in two other provinces of Vietnam ( Table 1 ) . PCR amplification using virus-specific primers to an ORF1b region ( see below ) was employed to verify the presence of NDiV in the mosquito samples , but to date no other insects have been probed for the presence of the virus . It also remains to be investigated whether NDiV causes disease in susceptible hosts and whether it may infect humans . Purified NDiV was used for virion protein analysis ( Fig . 3B ) and genome sequencing ( Fig . S1; Materials and Methods ) . In silico translation of the unsegmented , 20 , 192 nt-long NDiV genome ( GenBank accession number DQ458789 ) indicated that it contained at least six ORFs: ORF1a ( nt 361–7869 ) , ORF1b ( 7830–15635 ) , ORF2a ( 15660–18356 ) , ORF2b ( 15674–16309 ) , ORF3 ( 18402–18875 ) and ORF4 ( 18754–19101 ) ( Fig . 3D ) . The region encompassing ORFs 3 and 4 also contains a few smaller potential ORFs . The coding region of the genome is flanked by a 5′-untranslated region ( UTR ) ( 1–360 ) and a 3′-UTR ( 19102–20192 ) , with the latter being followed by a poly ( A ) tail . The 5′-UTR includes two AUG codons indicating that translation initiation for ORF1a/ORF1b is likely mediated by another mechanism than ribosomal scanning . Three pairs of ORFs ( 1a–1b , 2a–2b , and 3–4 ) overlap to variable degrees; particularly , ORF1b overlaps ORF1a in the −1 frame ( Fig . 3D; see also below ) . Overall , these results showed that NDiV is an insect-borne ssRNA+ virus with the largest genome known so far - twice the size of the next largest one , which is the genome of the Iflavirus Brevicoryne brassicae picorna-like virus [26] ( Fig . 1 ) . The NDiV genome organization most closely resembles that of nidoviruses , the only group of ssRNA+ viruses that includes representatives with genomes larger than that of NDiV . This putative relationship was subsequently verified in experimental and bioinformatics analyses of the function and expression of the 3′-ORFs region and in bioinformatics analyses of ORF1a and ORF1b , as described below . The latter studies also provided insights into the evolution and molecular biology of other nidoviruses . Three virion proteins , p2a , p2b , and p3 , were assigned to ORFs 2a , 2b , and 3 , respectively , by peptide sequencing analysis ( Fig . 3D ) . No significant similarity was found between these ORFs of NDiV and proteins of other origin in BLAST-mediated searches [27] . The p2b protein is highly hydrophilic and enriched with proline ( 7 . 5% ) and acidic residues ( 17 . 8% ) , and – relative to other virion proteins – with basic residues ( 7 . 9% ) making it a potential nucleocapsid ( N ) protein . The p2a and p3 proteins , and the putative protein encoded in ORF4 ( p4 ) contain , respectively , six , two , and two stretches of hydrophobic residues indicative of transmembrane helices ( Fig . S2 ) . These proteins also include , respectively , twelve , two , and three potential N-linked glycosylation signals ( NXS/T ) , and fifteen , six , and four cysteine residues that might form disulfide bridges at locations flanked by hydrophobic regions . These characteristics are typical for glycoproteins of other RNA viruses . Based on size considerations , the largest protein , p2a , might be an equivalent of the spike ( S ) protein , while p3 and/or p4 might be a smaller glycoprotein and an equivalent of the membrane ( M ) protein of nidoviruses . We also asked whether NDiV resembles other nidoviruses in using subgenomic mRNAs for expressing the 3′-end ORFs located downstream of ORF1b . First , we attempted to identify potential TRS motifs in the viral genome sequence , which were expected to reside in the 5′-UTR as well as in the regions immediately upstream of ORF2a , 3 , and 4 . Although no common repeats larger than six nucleotides were identified in these four areas , we noticed the presence of two pairs of near-perfect repeats: the first pair located in the 5′-UTR ( nt 26–40 of the genome ) and the region upstream of ORF3 ( 14 out of 15 residues are identical ) , and the second pair encompassing nt 125–137 of the 5′-UTR and a sequence immediately upstream of ORF2a/2b ( 12 out of 13 residues are identical ) ( Fig . 3D ) . The two pairs share from ∼43 to 52% pair-wise sequence identity in an alignment containing a single gap ( Fig . 3D ) , and no other repeats of comparable or larger size were found in the analyzed areas . The locations and sizes of these repeats suggest they are TRS signals , although no candidate TRS was identified immediately upstream of ORF4; to our knowledge , the use of two alternative leader TRSs has not been observed in other nidoviruses thus far . These observations suggested that NDiV uses at least two subgenomic mRNAs for the expression of the 3′-located ORFs and that these mRNAs have 5′-terminal sequences of different size in common with the viral genome . To verify this model , we used a P32-labelled probe complementary to the 3′-end of the NDiV genome in a Northern blot hybridization with total RNA isolated from NDiV–infected C6/36 cells ( see Materials and Methods; Table S2 , and Fig . 3C ) . This analysis revealed three prominent RNA species with apparent sizes of about 20 , 4 . 5 , and 1 . 8 kb , which match those expected for the genomic RNA and two subgenomic RNAs , mRNA2 ( to express ORF2a and ORF2b ) and mRNA3 ( for ORF3 and possibly ORF4 ) , respectively . We also observed a set of less abundant bands in the 0 . 9–1 . 1-kb size range , whose origin ( s ) and relevance remain to be established . Nidoviral ORF1a/ORF1b −1 ribosomal frameshifting ( RFS ) is controlled by a “slippery sequence” and a stem-loop or pseudoknot RNA structure immediately downstream [7] . RFS is conserved in nidoviruses and this property is widely used for computational mapping of its determinants in newly sequenced genomes . We followed this approach to map potential RFS signals in the NDiV genome ( Fig . 4 ) . The 40-nt NDiV ORF1a/ORF1b overlap region was found to have the best match ( GGAUUUU ) with the slippery sequence ( AAAUUUU ) of invertebrate roniviruses [28] , which deviates considerably from the pattern ( XXXYYYZ ) conserved in vertebrate nidoviruses ( Fig . 4A ) . No appreciated similarity with the latter motif was found in the NDiV ORF1a/ORF1b overlap region . The distances separating the NDiV putative RFS from the termination codons flanking the ORF1a/ORF1b overlap are within the range found in large nidoviruses , while being out of the distance range to the ORF1a stop codon of small nidoviruses ( Fig . 4B ) . According to the analysis of a 190-nt sequence - which starts within the NDiV ORF1a/ORF1b overlap - with Mfold [29] and pknotsRG [30] , the predicted slippery sequence is followed by a complex stem-loop structure; no pseudoknots , unless forced , are predicted in this region ( Fig . 4C ) . The slippery sequence , distance to the downstream RNA secondary structure , and predicted fold resemble those of Red clover necrotic mosaic virus ( RCNMV ) , a ssRNA+ plant virus of the family Tombusviridae [31] , [32] ( Fig . 4C–D ) . These results identified the critical elements of the putative NDiV RFS as being most unique among those described for members of the order Nidovirales . Nidoviruses are distinguished from other RNA viruses by a constellation of 7 conserved domains having the order TM2-3CLpro-TM3-RdRp-Zm-HEL1-NendoU , with the first three being encoded in ORF1a and the remaining four in ORF1b . TM2 and TM3 are transmembrane domains , Zm is a Zn-cluster binding domain fused with HEL1 , and 3CLpro is a 3C-like protease [4] ( however see below ) . Since NDiV was found to be very distantly related to the other nidoviruses known to date , sequence-based functional characterization presented a considerable technical challenge . In comparative sequence analysis , profile-based methods that employ multiple sequence alignments are known to achieve the best signal-to-noise ratios [27] , [33] , [34] . They have been the methods of choice for establishing remote relations in biology , also in our prior studies of nidoviruses [15] , [35]–[37] . In this study we used profile vs . sequence and profile vs . profile searches as implemented in HMMer and HHsearch , respectively , for general comparisons . To prepare profiles , we selected representatives of small and large nidoviruses , and also three subsets of large nidoviruses ( coronaviruses , toro/bafiniviruses , and roniviruses ) . Using profile-based searches we identified counterparts ( orthologs ) of nidovirus-wide conserved enzymatic domains in the NDiV pp1ab . For the identification of TM2 and TM3 , predictions of transmembrane helices by TMpred were used . Six out of the seven nidovirus-wide conserved protein domains , TM2-3CLpro-TM3-RdRp-Zm-HEL1 , were mapped in the canonical position and order in the NDiV ORF1a/1b sequence ( Table 2 ) . Three of these putative NDiV domains , 3CLpro [9] , [38] , RdRp [39] , and HEL1 [40] are enzymes conserved in all nidoviruses [14] . They have counterparts of all invariant and highly conserved residues implicated in catalysis in other nidoviruses , a finding indicative of the functionality of these proteins in NDiV . Like its orthologs in corona- and roniviruses , the NDiV 3CLpro is predicted to employ a catalytic His-Cys dyad . Its substrate-binding site is predicted to include a conserved His residue which was implicated in controlling the P1 specificity for Glu/Gln residues in other viruses , a hallmark of 3C/3CLpros [41] . Surprisingly , despite this finding , no candidate cleavage sites with the characteristic 3CLpro-specific signatures could be identified in the NDiV pp1a/1ab . Consequently , the sizes of all NDiV replicative domains described in this paper ( Table 2 ) are based on the hit sizes in profile searches and are subject to future refinement . Collectively , these results strongly indicate that NDiV encodes all nidovirus-wide conserved replicase domains except for NendoU ( Figure 3D; see also below ) , thus supporting the classification of NDiV as a nidovirus . All large nidoviruses express an ExoN [16] of the DEDD superfamily , which is not found in other ssRNA+ viruses , and an OMT [42] , [43] of the RrmJ family , that is not present in arteriviruses [15] . The presence of these domains therefore discriminates large from small nidoviruses . Using profile searches in the ORF1b-encoded part of pp1ab , homologs of these two enzymes were identified in the NDiV genome ( Table 2 ) . Using an ExoN multiple sequence alignment of NDiV and large nidoviruses , the conserved motifs I , II , and III , including the catalytic residues ( two Asp and one Glu ) , as well as the ExoN-specific Zn-finger module were identified in the NDiV ortholog ( Fig . 5A ) . Furthermore , the NDiV ExoN shows an insertion whose size and position correspond to those of the second Zn-finger-like module that is exclusively found in roniviruses . However , unlike the ronivirus domain , NDiV appears to lack His/Cys residues potentially involved in Zn-binding . According to a multiple sequence alignment of nidovirus OMTs ( Fig . 5B ) , the putative NDiV OMT contains motifs X , IV , VI and VIII , encompassing residues of the catalytic KDKE tetrad , as well as motif I involved in binding of the methyl donor [42] . These data imply that NDiV ORF1b encodes functional ExoN and OMT domains ( Fig . 3D ) , which are both typical of large nidoviruses . NDiV ORF1b includes a ∼750-nt region that is flanked by the upstream ExoN and downstream OMT domains and was expected to encode a NendoU domain [44]–[47] , given its presence at this locus in all nidoviruses known so far [15] , [48] . Surprisingly , however , profile searches of nidovirus NendoUs revealed no significant hits in the corresponding region of the NDiV sequence ( E-values>9 . 5 ) . This observation prompted us to re-examine the NendoU assignment in other nidoviruses , including the invertebrate roniviruses [15] . Using profile-sequence and profile-profile comparisons mediated by HMMer and HHsearch , respectively , NendoU counterparts were readily identified in all corona- , toro/bafini- , and arteriviruses ( E-values<10−4 ) , but not in roniviruses ( E-values>4 . 5 ) . We therefore conclude that , unlike other ( vertebrate ) nidoviruses , the invertebrate NDiV and roniviruses do not encode a NendoU domain ( Fig . 3D ) . We proceeded to analyze this genomic region flanked by ExoN and OMT in invertebrate nidoviruses in more detail . First , using a ronivirus profile vs . NDiV pp1ab sequence comparison , we found that these domains are moderately similar to each other ( E-value = 0 . 18 ) , suggesting a weak conservation of a common function in these newly recognized orthologous domains of NDiV and roniviruses . Their alignment was converted into a profile with which we screened all domains of our in-house nidovirus profile database ( see Materials and Methods ) . Remarkably , the only significant hit ( E-value<10−4 ) was recorded against the coronavirus NMT profile ( Table 2 ) . For comparison , its similarities with NendoU profiles of corona- , toro/bafini- or arteriviruses were not significant ( E-value>1 . 5 ) . These data indicate that NDiV and roniviruses may encode an NMT domain that is flanked by ExoN and OMT . The coronavirus NMT domain was originally mapped to the C-terminal half of nsp14 [43] , [49] . The corresponding domain in toro/bafiniviruses has a much smaller size ( 80 aa vs . 200 aa ) . According to our analysis , it has no significant similarity with the NMT of coronaviruses , or the newly recognized putative NMT of roniviruses and NDiV . Based on these observations , we generated an alignment of the NMT domains of corona- and roniviruses and NDiV ( Fig . 5C ) in order to search for remote cellular homologs . The N-terminal part of the nidovirus NMT includes a conserved methyl donor binding site ( motif I ) , according to the prior assignment for coronavirus NMTs . In line with this observation , a weak hit between nidovirus NMTs and a cellular guanine N7-methyltransferase involving the motif I region was detected in this study . In their C-terminal part , nidovirus NMTs uniquely include four conserved Cys/His residues indicative of a Zn-binding site that may be part of a separate domain ( Fig . 5C ) . Collectively these results established a mosaic domain relationship in the pp1ab area flanked by ExoN and OMT domains for large nidoviruses and NDiV . In this genomic region coronaviruses encode both NMT and NendoU domains , while other viruses encode either NendoU ( toro/bafiniviruses ) or NMT ( roniviruses and NDiV ) . Next , we proceeded to determine the phylogenetic position of NDiV among nidoviruses . The phylogeny was inferred using Bayesian posterior probability trees for a concatenated alignment of three enzymes , 3CLpro , RdRp , and HEL1 , that are conserved in all nidoviruses ( see Materials and Methods ) . In line with the current nidovirus taxonomy and genomic data [28] , [48] , [50] , [51] , this analysis consistently identified the four known major lineages ( arteri- , roni- , corona- , and toro/bafiniviruses ) , as well as a new one represented by NDiV , as the most deeply rooted branches . Our initial attempts to resolve the relationship among the five lineages produced uncertain results . To address this challenge , we adopted a step-wise approach starting from the analysis of close intra-group relationships in the most abundantly sampled subfamily , Coronavirinae , and the family Arteriviridae , and finishing with an analysis of the most distant inter- ( sub ) family relationships between the five major lineages . Prior to the nidovirus-wide phylogenetic analysis , the affinity of arteri- , roni- , and toro/bafiniviruses to the subfamily Coronavirinae was evaluated through a profile-based analysis involving conserved domains ( see Supplementary Text S1 and Table S1 ) . The obtained results confirmed that the strongest sequence affinity exists between corona- and toro/bafiniviruses , which was evident for the 6 out 8 domains that are conserved between coronaviruses and one or more of the other lineages . The HEL1 was the only domain for which an alternative strongest affinity – between corona- and roniviruses – was documented . The affinity established above was incorporated as prior knowledge in the nidovirus-wide phylogenetic analysis in order to improve the resolution of the most distant relationships . Accordingly , two alternative reconstructions were conducted with the clustering of toro/bafiniviruses and coronaviruses being either fixed or not . When the clustering was not fixed , roniviruses were found to be closest to coronaviruses ( Fig . 6A ) . This topology indicated that the HEL1 sequence affinity dominated over that of the RdRp ( Table S1 ) in the concatenated 3CLpro-RdRp-HEL1 alignment . An alternative nidovirus phylogeny was inferred when the clustering of coronaviruses and toro/bafiniviruses was fixed prior to the inference ( Fig . 6B ) . Importantly , in both trees , NDiV was consistently albeit relatively distantly clustered with roniviruses , indicating that this grouping does not depend on the choice of tree-building parameters and is likely genuine . To infer the direction of nidovirus evolution , we sought to root the nidovirus phylogeny using an outgroup approach . Neither other viruses nor cellular organisms encode the domain constellation that is conserved in nidoviruses , precluding an expansion of the original nidovirus dataset with outgroup sequences to root the tree . This prompted us to split the domain constellation and perform separate analyses of the evolution of the two most conserved nidovirus protein domains , RdRp and HEL1 , which are also among the most conserved in ssRNA+ viruses ( Fig . 6C–D ) . Prior to the analysis , major clades comprising coronaviruses , toro-/bafiniviruses , roniviruses , and arteriviruses , and an outgroup were each fixed to be monophyletic . For the HEL1 tree ( Fig . 6C ) , the part of the alignment covering the most conserved region from motif I to motif VI ( see [52] ) was used . Representatives of rubiviruses , betatetraviruses , omegatetraviruses , and hepeviruses were used as an outgroup . The resulting topology closely resembles that of the relaxed nidovirus phylogeny ( Fig . 6A ) , in which vertebrate coronaviruses and invertebrate nidoviruses are sister clades , thus confirming that it is dominated by the HEL1-related component . For the RdRp tree ( Fig . 6D ) , an alignment of the most conserved RdRp region delimited by motifs G and E ( see [53] ) was used . Representatives of three divergent picornaviruses ( an enterovirus , a parechovirus , and a hepatovirus ) were used as an outgroup . The resulting topology matches that of the constrained nidovirus phylogeny ( Fig . 6B ) , in which the grouping of corona- and toro-/bafiniviruses was forced , and could thus be considered RdRp-like . Despite somewhat incongruent topologies in the two protein-specific phylogenies , in both cases the outgroups are consistently placed at the branch leading to arteriviruses , thus separating small- from large- and intermediate-size viruses in nidovirus evolution . The support for the positioning of the outgroups in the RdRp and HEL1 trees by Bayesian/ML estimates ( 0 . 69/522 and 0 . 48/990 , respectively ) is relatively low and/or varied in analyses by two methods , possibly due to the very large evolutionary distances separating the major virus groups , including the outgroups . We used the rooting on the arterivirus branch to root the nidovirus tree that was inferred using a concatenated alignment of three domains ( Fig . 6A–B ) . According to this analysis , small nidoviruses are separated from other nidoviruses , and NDiV is monophyletic with roniviruses in a separate clade of invertebrate nidoviruses , which clusters with the group formed by corona- and toro/bafiniviruses . NDiV and roniviruses are separated by a large evolutionary distance indicating that NDiV likely is the prototype of a separate family . The topology of the tree in Fig . 6B is compatible with a scenario in which genome size change during nidovirus evolution was dominated by expansion , with contemporary nidoviruses representing different stages in the transition from small to large ssRNA+ genomes . We describe the discovery of an insect-borne ssRNA+ virus , called NDiV , possessing a genome organization , virion properties , mRNAs , and putative proteome characteristics that place it in the order Nidovirales . In phylogenetic and protein domain analyses NDiV consistently , albeit relatively distantly , clustered with viruses of the family Roniviridae , which seems to make sense biologically given that both infect invertebrate hosts . Although the NDiV classification as the first insect nidovirus is beyond doubt , its characterization was only just initiated in this study . NDiV is likely to possess unique properties concerning , for example , the leader-body junctions of its sg mRNAs and the cleavage sites recognized by its 3CLpro , which both require further characterization . The principal biological significance of the discovery of NDiV is in the intermediate position this virus occupies between small and large nidoviruses in the genome size distribution observed for ssRNA+ viruses . Prior to this study , the existence of currently circulating nidoviruses with genome sizes within this gap was even highly uncertain ( see Introduction ) . Together small and large nidoviruses cover the upper ∼19 kb ( ∼66% ) of the entire ssRNA+ genome size range and are separated by ∼10 kb ( 32% ) . The very existence of NDiV validates the previously established evolutionary relationship between the remotely related arteriviruses and coronaviruses that have very different genome sizes [23] . Characterization of arteri- and coronaviruses by comparative genomics has been instrumental in defining the common and unique features of members of the order Nidovirales [14] , and has guided the delineation of potential targets for antiviral drug design [54] . The inclusion of NiDV in this analysis yields additional and novel insights with implications for nidoviruses and other RNA viruses at large . It allowed us to revise and expand the assignment for two replicative enzymes of nidoviruses – NendoU and NMT . Prior to this study , the former was considered to be a genetic marker of nidoviruses [15] . Still , its ( universal ) function in the replication cycle of ( vertebrate ) nidoviruses has remained enigmatic , despite steady progress in the biochemical , structural , and genetic characterization of this enzyme in arteri- and coronaviruses [44]–[47] , [55]–[60] . Our analysis showed that invertebrate roniviruses and NDiV do not encode a NendoU domain implying that , contrary to the current paradigm , the utilization of this enzyme in replication may be restricted by the host organism . Surprisingly , and in contrast to the case of NendoU , invertebrate nidoviruses were found to encode a putative NMT , whose ortholog was previously identified in SARS-CoV and shown to be conserved in the subfamily Coronavirinae [43] , [49] . Our observation indicates that certain aspect ( s ) of the nidovirus replicative cycle that are controlled by the NMT domain could be similar in coronaviruses and invertebrate nidoviruses , but not toro/bafiniviruses which are otherwise closer to coronaviruses . Collectively , our insights into the phyletic distribution of NendoU and NMT reveal a modularity of some of the major subunits of the replication apparatus in large nidoviruses , which must be rationalized in future mechanistic studies and taken into account in drug development efforts . Although the NDiV genome size is intermediate between those of small and large nidoviruses , NDiV most closely resembles large nidoviruses in properties that are not universally conserved in the order . Particularly , NDiV does not encode a homolog of the replicative protein of unknown function ( nsp12 ) that is exclusively conserved in arteriviruses [14] and it has a set of three replicative enzymes , OMT , NMT , and ExoN , encoded in large but not in small nidoviruses . These three enzymes are encoded in ORF1b , downstream of the RFS ( Fig . 3D and Fig . 4 ) and in the vicinity of the two key enzymes for RNA synthesis , RdRp and HEL1 , with their expression level being downregulated relative to that of the ORF1a-encoded subunits . Despite these common properties , the two methyltranferases ( OMT and NMT ) differ from ExoN in their relation to genome size . Particularly , OMTs are known to be also encoded by flaviviruses [61] whose genome size of ∼10 kb is average for RNA viruses , while the NMT domain was found to be lacking in a subset of large nidoviruses represented by toro-/bafiniviruses ( this study ) . Furthermore , an N-methyltransferase function , albeit associated with a domain seemingly unrelated to the NMT domain of nidoviruses , was identified in the large Alphavirus-like supergroup of ssRNA+ viruses , whose members have genome sizes from ∼7 , 000 to 19 . 500 nt [62]–[64] . ssRNA+ viruses use methyltransferases to modify the 5′-end of their mRNAs ( cap structure ) , which was recently found to be essential in the control of translation and innate immunity [65] , [66] . It is not clear whether the use of methyltransferases may provide particular benefits for genome size control and/or promote genome expansion , although the involvement of OMT in other modifications than 5′-end capping was previously proposed for large nidoviruses [15] . In contrast to the case of the methyltransferases , the link between ExoN and genome size control in nidoviruses is supported by accumulating evidence obtained from different hypothesis-driven genetic studies [4] , [20] . First , ExoN is exclusively found in a phylogenetically compact cluster of ssRNA+ viruses with large genome sizes . Second , cellular homologs of ExoN control the fidelity of replication in DNA-based life forms and are essential to maintain these large genomes . Third , ExoN active site mutants in MHV and SARS-CoV showed a stable phenotype characterized by a clearly enhanced mutation rate and nearly wild-type progeny yields . The identification of the ExoN-encoding NDiV further strengthens the case for the direct involvement of ExoN acquisition in genome size expansion . First , because of its distant relation to any known virus and its insect host range that is a novelty for nidoviruses , NDiV provides an essentially independent verification for the association of ExoN with RNA viruses employing large genomes . Second , it increases our confidence that no other domain is associated with large genome sizes in nidoviruses as strongly as ExoN is . The existence of such a domain is unlikely but it cannot be formally excluded because the entire proteomes of nidoviruses are yet to be fully described . However , our confidence about the lack of this alternative domain grows with the decrease of difference between genome sizes of nidoviruses containing and lacking ExoN: the smaller this difference the less capacity remains to encode an additional domain . With the identification of NDiV , this genome size gap decreased from ∼10 . 6 kb to ∼4 . 5 kb , the largest drop since this gap could have been recognized ( ∼14 . 9 kb in 1991 ) ( Fig . S3 ) . Third , following the discovery of NDiV , only ∼0 . 8 kb remains of the other genome size gap of ∼7 kb that previously separated the ExoN-containing nidoviruses from all other ssRNA+ viruses ( Fig . 1 ) . Thus , a major step has been made towards a more precise definition of the RNA genome size limit above which the recruitment of a specialized enzyme for replication fidelity control may be a prerequisite . According to a custom binomial test ( see Materials and Methods ) , the probability to observe the association of ExoN and large ssRNA+ genome size by chance may be 10−6 or lower . The genome size threshold of ∼20 kb , as defined by NDiV and a closterovirus [67] , which has the largest genome size among ssRNA+ viruses other than nidoviruses , is also valid for unsegmented RNA viruses of other classes , all of which do not employ an ExoN in their replicative machinery [21] . The fixation of the ExoN domain in nidovirus genomes may be rationalized in the framework of a unidirectional triangular relationship that includes complexity , replication fidelity ( mutation rate ) , and genome size [68] ( Fig . 7 ) . In RNA viruses , the low fidelity of replication severely restricts the size of their genomes , which can encode only relatively simple replication complexes that , hence , suffice to support low-fidelity replication [21] , [69] . This low-state trap is known as the “Eigen paradox” . Accordingly , a transition from the “low” to the “high” state may not be accomplished by changing only one element of the triangle , e . g . improving replication fidelity , since such a change would not be compatible with the “low” state of the other two elements [68] [70] . The exclusive presence of ExoN in ssRNA+ viruses above 20 kb supports the logic of the Eigen paradox [68] . It also shows how the paradox could be solved with a single evolutionary advancement , the acquisition of ExoN , which may have relieved the constraints on all three elements of the triangular relationship ( Fig . 7 ) , providing a lasting benefit to the virus lineage that acquired ExoN . This advancement may have been accompanied by an immediate fitness gain . Accordingly , the ExoN acquisition could have provided the ancestral virus with improved control over the fidelity of its replication and the mutation spectrum ( quasispecies structure ) of its progeny [71] , [72] , which may have facilitated virus adaptation to the environment [20] , [73] . Alternatively , ExoN could have been acquired in an evolutionarily neutral event . Through subsequent mutation this enzyme might have gained beneficial properties for the ancestral virus and its progeny . The functional and structural characterization of known nidoviruses and yet-to-be identified viruses in the genome size range around that of NDiV will be required to clarify this key aspect in the transition from small to large nidoviruses . The acquisition of ExoN by an ancestral nidovirus must have produced viable progeny but it remains unknown whether , besides ExoN , any additional properties of the ancestral nidovirus were critical for genome expansion , as was speculated elsewhere [15] . Recently an exoribonuclease was identified in the ssRNA- arenaviruses , which have genome sizes below 10 kb [74] , [75] . Unlike nidoviruses , arenaviruses employ the exoribonuclease as a domain of their nucleocapsid protein that , accordingly , mediates a non-replicative function . In line with these differences , the nidovirus ExoN and the arenavirus exoribonuclease do not share specific sequence affinity ( CL and AEG , unpublished data ) , indicating that both are likely to have been acquired from independent sources and were integrated into different genetic settings to perform different functions . NDiV may be the first but likely not the last nidovirus identified in mosquitoes [76] . Systematic probing of these and other insects could lead to the discovery of new nidoviruses , and characterization of those with genomes in the size range between small and large nidoviruses could be particularly insightful . As presented in this study , benefits of these advancements could be multifold and provide a foundation for both fundamental and applied research on newly discovered and already known viruses . During continued surveillance for JEV in Vietnam between September 2001 and December 2003 , 24 , 097 female mosquitoes belonging to six different Culex species ( Culex tritaeniorhynchus , Culex gelidus , Culex vishnui , Culex fusco , Culex pseudo , and Culex quinquefaciatus ) were collected . They were divided into 359 pools , each containing a single mosquito species and handled with utmost care following the appropriate biosafety measures . For the digestion of blood meals , the samples were kept in 5% glucose for two weeks at room temperature and a humidity of ∼90% . The most abundant species was Culex tritaeniorhynchus ( 10 , 194 mosquitoes accounting for a 42 . 3% share ) , followed by Culex gelidus ( 6 , 199 , 25 . 7% ) , Culex vishnui ( 3 , 780 , 15 . 7% ) , Culex quinquefaciatus ( 2868 , 11 . 9% ) , with the remaining species ranging from 0 . 3%–4 . 1% . Mosquito pools were stored at −70 C prior to processing for virus isolation . Four cell lines were used to isolate viruses , but NDiV was evident only in samples from Aedes albopictus C6/36 cells grown at 28 C in Eagle's Minimum Essential Medium ( EMEM ) containing 10% fetal calf serum ( FCS ) and 0 . 2 mM non-essential amino acids [77] . Pooled mosquitoes were washed three times in sterile phosphate-buffered saline ( PBS , pH 7 . 2 ) containing 1000 g/ml each of penicillin and streptomycin , followed by rinsing with antibiotics-free PBS . The homogenates were prepared by triturating the mosquitoes in 2%-FCS-EMEM with subsequent centrifugation at 2 , 000 g for 10 min . The suspensions were filtered ( 0 . 22 nm Millipore , USA ) and applied to C6/36 cells , which were monitored daily for cytopathic effects , also after three blind passages . The cell death , probably due to apoptosis , was indeed observed upon NDiV infection . The ICF were clarified by centrifugation at 2 , 000 g for 10 min . The nucleic acid was extracted from the purified NDiV virus particles using phenol-chloroform extraction . It migrated as a single band in agarose gel electrophoresis , which was sensitive to RNase but not DNAse treatment , indicative of an RNA virus genome . Accordingly , reverse transcriptase ( RT ) was used to amplify parts of the NDiV genome by Random Arbitrary Primers-PCR ( RAP-PCR ) in order to initiate sequence analysis . Cassette primers ( C1 and C2 ) coupled to random hexamers ( Hx ) were employed . Following synthesis of first and second cDNA strands with C1Hx and C2Hx primers , respectively , PCR amplification was performed using the cassette primers C1 and C2 as per the standard protocol [78] . Three amplicons of different sizes , which were specific for the virus-containing samples , were then cloned in the pCR2 . 1-TOPO vector ( TOPO TA Cloning Kit , Invitrogen ) according to the manufacturer's instructions . The sequence of the first cloned fragment ( referred to as “index clone” ) was determined by Big Dye Terminator Cycle Sequencing using M13 forward and reverse primers in an ABI 310 or 3100 automated DNA sequencer ( Applied Biosystems ) . The cloned region of the genome was extended by ‘gene walking’ using primers based on previously obtained sequence information ( Table S2 ) . To sequence the genomic region upstream of the index clone , the following amplification strategy was used , involving two DNA fragments called double-stranded ( ds ) cDNA and anchor DNA . To produce ds cDNA , viral genomic RNA was mixed with 10 mM dNTP mix and 2 pmol of 15-mer gene-specific primers ( NDiV-RACE492-477RP , NDiV-RACE302-288RPB and NDiV-RACE435-420RPC ) ( Fig . S1A , Table S2 ) . An anchor DNA was synthesized by PCR that amplified a specific fragment of pUC19 , including its multiple cloning site ( Fig . S1B ) . Both , the ds viral cDNA and PCR product obtained from pUC19 ( anchor ) were digested by several restriction enzymes whose sites are present in the pUC19 multiple cloning site ( BamHI , EcoRI , KpnI , HindIII , ScaI , and PstI ) . The digested pUC19 PCR products were then purified using the QIAXII gel purification kit ( Qiagen ) in order to collect the longer DNA fragments . The digested viral cDNAs were also purified by filtration using Micropure-EZ ( Millipore ) and Microcon YM-100 ( Millipore ) to remove enzymes and buffers . In a next step , the purified cDNAs and anchor DNAs were mixed and ligated using T4 DNA Ligase ( TaKaRa ) . The unknown region of viral cDNA was then amplified by semi-nested PCR using LA-taq ( TaKaRa ) , two viral gene specific primers and one pUC19 primer ( Table S2 ) as shown in Fig . S1C . The reaction process included an initial denaturation at 96°C for 5 min , 35 cycles at 96°C for 30 sec , 53°C for 30 sec , and 72°C for 7 min , and a final extension at 72°C for 10 min . The known viral genome sequence was further extended by long RT-PCR which resulted in an 8 kb fragment with a 68-nucleotide polyA tail representing the 3′-end of the NDiV genome . The GeneRacer™ Kit ( Invitrogen ) was used to sequence the 5′-end of the NDiV's genome . The NDiV origin of newly obtained sequences was further validated by probing different samples with a primer pair designed against the index clone . This pair of primers recognized NDiV isolates , but not JE and dengue viruses ( flaviviruses ) or SARS-coronavirus ( Coronavirus ) . These results indicated that NDiV is a novel mosquito virus . Specific primers encompassing NDiV nts 19 , 733 and 20 , 126 ( including 2 Adenines of the poly ( A ) tail ) , respectively , were designed ( Table S2 ) . The generated PCR product was purified using the Qiaex II gel extraction kit ( 500 ) ( Qiagen ) following the manufacturer's instructions . The purified PCR product was then ligated to a 3 . 5 kb plasmid ( PCR-XL-TOPO ) using the TOPO XL PCR cloning kit ( Invitrogen , applying the TA rule based on the Taq polymerase's capacity of adding an extra A at the 3′ end of each DNA chain of a PCR product ) as per the manufacturer's indications . Heat shock transformation into One Shot Top 10 chemically competent cells ( Invitrogen ) was carried out and the transformed cells were incubated in SOC medium at 37 C for 2 hrs . After that , the E . coli cells were cultured in 50 µg/ml containing LB plates overnight and the positive clones were subsequently cultured in LB broth at 37 C overnight . The plasmid alkaline extraction was done using the QIAprep spin Miniprep kit ( Qiagen ) as the manufacturer indicated . As a next step , verification of the probe orientation was carried out by nucleotide sequencing . Finally , transcription of the cloned DNA sequences was done to generate the RNA probe ( in both sense and reverse orientations ) . The RNA probe was then labeled with 32P by using the AmpliScribe T7 High Yield Transcription Kit ( EPICENTRE Biotechnologies ) following the company's instructions . To investigate the possibility that NDiV generates set of 3′-coterminal sub-genomic mRNA's during its replication , Aedes albopictus C6/36 cells were infected with NDiV . Three to four days after infection intracellular poly ( A ) -containing RNA from mock-infected and NDiV-infected cells was prepared using Dynabeads oligo ( dT ) 25 ( Dynal Biotech ) as per the manufacturer's instructions . RNA was separated on a glyoxal-based agarose gel system and blotted on a positively charged nylon membrane ( BrightStar-Plus membrane ) . The mRNA bands were then hybridized with an α-32P-multiprime-labeled RNA probe specific for NDiV at 65°C overnight ( see above RNA probe generation ) . The membrane was then washed with low and high stringency wash solutions and the RNAs were analyzed by autoradiography . All reagents for mRNA separation , transfer and hybridization ( with the exception of the RNA probe ) were provided with the NorthernMax-Gly Kit ( Ambion ) . The manufacturer's instructions were followed . A 0 . 5–10 Kb RNA Ladder ( Invitrogen ) was used as a marker set to calculate apparent molecular mass of the analyzed bands . For electron microscopy , virus was concentrated from ICF by centrifugation at 12 , 000 g for 30 min at 4 C , after which 6 . 6% polyethylene glycol 6000 and 2 . 2% NaCl were added to the supernatant . After stirring for 1 h at 4 C and centrifugation at 12 , 000 g for 1 h , the supernatant was discarded . The virus-containing pellet was dissolved in saline-Tris-EDTA buffer , sedimented at 250 , 000 g for 1 h and resuspended a second time . The concentrated virus was negatively stained with 1% sodium phosphotungstic acid , pH 6 . 0 , and examined at 100 KV using a transmission electron microscope ( JEM-100CX , JEOL , Japan ) [79] . Virions were purified in a 15–50% sucrose density gradient using an SW32Ti rotor ( Beckman Coulter , Inc . , Fullerton , CA ) at 20 , 000 rpm for 12–16 h at 4°C . Gradient fractions were analyzed by 16% SDS-polyacrylamide gel electrophoresis and Coomassie Brilliant Blue G staining ( Fig . 2B ) . Protein bands were excised and either directly sequenced by automated Edman degradation ( Applied Biosystems model 491cLC ) or digested with lysylendopeptidase prior to HPLC purification and sequencing . Genome sizes of ssRNA+ viruses were retrieved from the NCBI Viral Genome Resource [80] . GenBank , version 178 . 0 [81] , Pfam database , version 24 . 0 [34] , SCOP70 , version 1 . 75 [82] , and an in-house nidovirus domain profile database [15] , [54] updated in this study were used to identify putative functional domains encoded by the NDiV genome . Representatives of the nidovirus species defined according to ( http://www . ictvonline . org/virusTaxonomy . asp ? version=2009 ) plus NDiV , whose taxonomical status remains provisional , were used as detailed in Table S3 . Species names of coronaviruses were taken from ICTV proposal 2008 . 085-122V . U that was approved by ICTV in 2009 . Fields after the “_” sign in virus abbreviations represents sampling year or period . The NDiV ORFs were compared with sequence databases using psi-BLAST [27] , HMMer 2 . 3 . 2 [83] , TMpred [84] , or HHsearch [85] . Protein secondary structure predicted by Psipred [86] was included in the HHsearch-mediated profile searches . RNA secondary structure analysis was conducted using Mfold [29] and pknotsRG [30] . MUSCLE [87] was used to produce alignments of nidovirus proteins that were manually refined in poorly conserved regions . Alignment derivatives , with the least conserved columns removed [88] , were prepared using BAGG [89] and were used for profile searches and phylogenetic analyses . Alignments were prepared for publication using JalView [90] . To compile and plot most graphs and conduct statistical analyses we used the R package [91] . Using the de novo repeat detection program RepeatScout [92] a library of perfect repeats with unit sizes ranging from four to the maximum observed size of 16 was compiled for the NDiV genome sequence . The library was filtered to retain repeats of different types according to the following constraints applied to each type separately: ( i ) one repeat copy must be located upstream of ORF1a , and ( ii ) another one must reside within the 300 nt region immediately upstream of either ORF2a , ORF3 , or ORF4 . Each set of the retrieved repeats was subsequently analyzed for conservation by alignment that included flanking regions of 20 nt at each side . The longest repeats with highest similarity were considered TRS candidates . To map major nidovirus replicative proteins to pp1ab of NDiV we applied alignment-based methods . Multiple sequence alignments represent a general tool to infer both common ancestry ( orthology ) of residues for several related sequences ( these residues form a fully occupied alignment column ) and identify insertion/deletion events ( corresponding to alignment columns containing gaps in selected sequences ) . Multiple alignments can be converted into profiles , which are statistical models that capture the degree of conservation and the likelihood to observe a certain residue or gap in each alignment column . One type of profiles are profile Hidden Markov Models ( HMMs ) [93] that are particularly suitable for searching for remotely related sequences ( like NDiV which presumably represents a new virus family ) in a probabilistic framework . They are implemented , for example , in the programs HMMer and HHsearch which were utilized in this study . A profile HMM can be compared to other HMMs or used to search for motifs in a single sequence . Due to the high degree of divergence of nidovirus sequences , we used alignments of amino acid sequences and profiles derived from these alignments to probe relation between proteins in this study . Phylogenetic analyses were performed as described previously [94] . Bayesian posterior probability trees were compiled utilizing BEAST [95] under the WAG amino acid substitution matrix [96] using Tracer [97] to verify convergence . For the nidovirus-wide analysis , whose sampling is detailed Table S3 , we used a concatenated alignment of 3CLpro , RdRp , and HEL1 including 910 aa positions and its derivative of 604 aa positions , from which least conserved columns were removed . In this analysis , the uncorrelated relaxed molecular clock approach ( lognormal distribution ) [98] was used as it was favored [99] over the strict molecular clock ( log10 Bayes factor of 13 . 6 ) and equal to the relaxed molecular clock approach with exponential distribution ( log10 Bayes Factor of 0 . 0 ) . Selected internal nodes were fixed using results of separate analyses of subsets of nidoviruses . For phylogenetic analysis of the subfamily Coronavirinae and the family Arteriviridae , we used respective datasets incorporating between one and three sequences per species and including concatenated alignments of ORF1ab domains that are conserved in each of these groups . The datasets included 35 and 10 sequences for corona- and arteriviruses and consisted of 2302- and 2882-aa alignment positions , respectively . The topologies of these trees closely follow those published [51] . They were used to fix internal nodes in corona- and arterivirus clusters in the subsequent nidovirus-wide phylogenetic analysis . The exception was the basal nodes corresponding to the grouping of the Alpha- , Beta- , and Gammacoronavirus genera and the root of arteriviruses ( EAV or SHFV ) , which were left unfixed . Maximum Likelihood trees were compiled utilizing the PhyML software [100] . The WAG amino acid substitution matrix and rate heterogeneity among sites ( 8 categories ) were applied and support values for internal nodes were obtained using the non-parametric bootstrap method with 1000 replicates . Trees were rooted using domain-specific outgroups: for RdRp , three picornavirus representatives ( accession numbers: NC_001489 , NC_001897 , NC_002058 ) ; for HEL1 , four rubi-/ tetra-/ hepevirus representatives ( NC_001545 , NC_001990 , NC_005898 , NC_001434 ) . We sought to statistically define a genome size threshold that separates ExoN-containing from ExoN-lacking ssRNA+ viruses . To this end , we developed a custom test employing the binomial probability function and including all 43 virus groups displayed in Fig . 1 . These groups consist of thousands of viruses that are believed to have emerged from a common ancestor , implying that they are not independent . Their dependence varies in virus pairs but , generally , for each virus pair is inversely proportional to the pair-wise evolutionary distance . To account for the dependence of these sequences in our test is technically challenging . To circumvent this problem , we have created a derivative of the virus dataset in which each virus family/group is represented by a single virus , in total 43 viruses . We considered the sequences of these representatives to be essentially independent due to the ( extremely ) large divergence that is observed , even in the most conserved genes ( e . g . see Fig . 6 ) , the lack of recognizable similarity in other genes , and the accompanied gene loss and gain . For a given genome size threshold , ssRNA+ viruses were partitioned into two groups ( below and above that threshold ) and the value of the binomial density function was calculated for both groups using information on the presence or absence of ExoN . The final probability of the test is the product of the binomial probabilities for the two groups . We used a binomial success probability of 4/43 since four out of the 43 ssRNA+ virus lineages ( NDiV , toro-/bafiniviruses , coronaviruses , and roniviruses ) employ ExoN . The test was applied to each possible threshold separating two unique ssRNA+ genome sizes , in total – 42 thresholds . The threshold of ∼20 kb , between the genome sizes of NDiV and closteroviruses , gave the lowest probability to observe the ExoN association by chance . We consider the obtained value ( 10−6 ) as an underestimate of the true probability that should be calculated by taking into account the sequence dependence and all viruses in the 43 groups , which without exception conform to the ExoN distribution observed in the selected virus representatives used now . RefSeq accession numbers of proteins referred to in the text for a selection of prototype nidoviruses are: 3C-like proteinase ( EAV: NP_705584 , SARS-CoV: NP_828863 , WBV: YP_803213 , GAV: YP_001661453 ) , RNA-dependent RNA polymerase ( EAV: NP_705590 , SARS-CoV: NP_828869 , WBV: YP_803213 , GAV: YP_001661452 ) , superfamily 1 helicase ( EAV: NP_705591 , SARS-CoV: NP_828870 , WBV: YP_803213 , GAV: YP_001661452 ) , exoribonuclease ( SARS-CoV: NP_828871 , WBV: YP_803213 ) , N7-methyltransferase ( SARS-CoV: NP_828871 ) , uridylate-specific endonuclease ( EAV: NP_705592 , SARS-CoV: NP_828872 , WBV: YP_803213 ) and 2′-O-methyltransferase ( SARS-CoV: NP_828873 , WBV: YP_803213 ) .
Research in virology is driven towards the characterization of a limited number of socioeconomically important pathogens , mostly those infecting humans . Yet , characterization of other viruses may advance our understanding of these topical pathogens and the fundamentals of virology . Here we describe the discovery of a virus of unknown clinical relevance that has many remarkable features . The virus was coined Nam Dinh virus ( NDiV ) after a Vietnamese province . It is a mosquito-borne virus with a 20 . 2 kilobase genome , the largest among non-segmented single-stranded RNA viruses of insects . Employing bioinformatics tools , we show that NDiV prototypes a new family and is a missing evolutionary link connecting the distantly related nidoviruses with small and large genomes , including important and diverse pathogens such as porcine respiratory and reproductive syndrome virus ( ∼15-kilobase genome ) and SARS coronavirus ( ∼30 kilobases ) , respectively . NDiV and large nidoviruses form a phylogenetic cluster and share a set of core replicative enzymes . They exclusively encode an exoribonuclease that presumably controls replication fidelity . Its acquisition may have promoted the emergence of viruses with single-stranded RNA genomes larger than ∼20 kilobases . This study highlights the benefits of broad virus discovery efforts for fundamental and applied research .
You are an expert at summarizing long articles. Proceed to summarize the following text: Many genes are recruited to the nuclear periphery upon transcriptional activation . The mechanism and functional significance of this recruitment is unclear . We find that recruitment of the yeast INO1 and GAL1 genes to the nuclear periphery is rapid and independent of transcription . Surprisingly , these genes remain at the periphery for generations after they are repressed . Localization at the nuclear periphery serves as a form of memory of recent transcriptional activation , promoting reactivation . Previously expressed GAL1 at the nuclear periphery is activated much more rapidly than long-term repressed GAL1 in the nucleoplasm , even after six generations of repression . Localization of INO1 at the nuclear periphery is necessary and sufficient to promote more rapid activation . This form of transcriptional memory is chromatin based; the histone variant H2A . Z is incorporated into nucleosomes within the recently repressed INO1 promoter and is specifically required for rapid reactivation of both INO1 and GAL1 . Furthermore , H2A . Z is required to retain INO1 at the nuclear periphery after repression . Therefore , H2A . Z-mediated localization of recently repressed genes at the nuclear periphery represents an epigenetic state that confers memory of transcriptional activation and promotes reactivation . The subnuclear localization of DNA has important roles in regulating transcription [1 , 2] . In particular , localization of chromatin near the nuclear periphery has well-documented effects on transcription . Heterochromatin and developmentally repressed genes localize at the nuclear periphery in metazoan cells , and peripheral localization promotes silencing of telomeres and the mating type loci in yeast [1 , 3–5] . Conversely , recent studies have shown that certain genes are conditionally recruited to the nuclear periphery when transcriptionally activated in both yeast and mice [6–13] . The yeast genes INO1 and GAL1 distribute randomly within the nucleoplasm under repressing conditions , but become co-localized with the nuclear periphery upon activation [6 , 7] . Live-cell four-dimensional imaging experiments reveal that recruitment is associated with both a change in the subnuclear distribution of genes and a reduction in their mobility , resulting in constrained movement near the nuclear envelope [9 , 14] . Chromatin immunoprecipitation experiments suggest that these and many other transcriptionally active genes physically interact with components of the nuclear pore complex ( NPC ) and associated factors [7] . The mechanism and functional significance of peripheral localization is unclear . Interaction of GAL1 with the nucleoporin Nup2 requires the Gal4 activator , but does not require the SAGA histone acetylase complex , and is not affected by inactivation of RNA polymerase II [13] . Thus , the association with the NPC and , presumably , recruitment of these genes to the nuclear periphery are regulated upstream of TBP binding and transcription . Furthermore , artificial tethering at the nuclear periphery promotes transcriptional activation of the INO1 gene [6] and the HXK1 gene [8] , and is sufficient to activate certain reporter genes [11] . Thus , recruitment to the nuclear periphery appears to have a functional role in promoting transcriptional activation . In contrast , recruitment of genes to the nuclear periphery has also been suggested to reflect coupling between transcription and mRNA export . Chromatin immunoprecipitation studies suggest that the interaction of mating pheromone–induced genes with the NPC is mediated by the mRNA [12] . Likewise , recruitment of HXK1 and GAL1 to the nuclear periphery is affected by sequences in the 3′ UTR [8 , 15] , and recruitment of GAL1 requires proteins that have been implicated in mRNA export [9] . These results raise the possibility that recruitment of genes to the nuclear periphery might simply be the product of physical interactions between nascent transcripts , the mRNA export machinery , and the NPC . Using a quantitative chromatin localization assay [6] , we find that the transcriptional activation of both INO1 and GAL1 genes in yeast is biphasic , with the mRNA levels increasing dramatically after gene recruitment is complete . RNA polymerase II activity was not required for peripheral recruitment of INO1 . Furthermore , when cells were shifted from activating to repressing conditions , INO1 and GAL1 remained localized at the nuclear periphery for generations . We find that localization at the periphery defines a distinct , heritable state that marks recently repressed genes and promotes reactivation . The reactivation of GAL1 was more rapid in cells that had previously activated the gene , even after six generations of repression . The rate of activation of INO1 was accelerated when the gene was artificially tethered to the nuclear envelope and was delayed in a mutant blocked for gene recruitment . Epigenetic mechanisms of transcriptional memory are employed extensively during metazoan development to stably propagate transcriptional states [16] . Such memory can be mediated by DNA methylation [17] , by histone H3 acetylation and methylation [18 , 19] or by incorporation of variant histone H3 . 3 [20] . We find that the histone variant H2A . Z was specifically required for reactivation of recently repressed INO1 and GAL1 , but had no role in the activation of the long-term repressed states of these genes . H2A . Z associated with nucleosomes in the promoter of the recently repressed INO1 gene , but not in the promoter of either activated or long-term repressed INO1 . Finally , we find that H2A . Z is essential for retention of recently repressed INO1 at the nuclear periphery . These results identify a new form of chromatin-based transcriptional memory and highlight an important role for H2A . Z in regulating subnuclear localization to mark recently repressed genes and promote their reactivation . To determine whether gene recruitment to the nuclear periphery requires transcription , we used a chromatin localization assay [6] . This is a quantitative assay for localization of genes at the nuclear periphery based on a system developed by Belmont , Murray , and colleagues [21 , 22] . An array of 128 lac repressor–binding sites is targeted for integration to a location in the yeast genome by homologous recombination . The array can then be localized as a green fluorescent protein ( GFP ) -labeled spot in cells expressing the lac repressor tagged with GFP ( Lac I-GFP ) . Cells within a population are individually analyzed by confocal microscopy and scored as either peripheral , if the Lac I-GFP co-localizes with the nuclear envelope ( marked by the endoplasmic reticulum/nuclear envelope membrane protein Sec63-myc ) , or nucleoplasmic , if the Lac I-GFP does not co-localize with the nuclear envelope [6] ( Figure 1A ) . The URA3 gene , which distributes randomly within the nucleus , co-localizes with Sec63-myc in 27%–30% of cells [6] ( Figure 1A ) . This represents the baseline for this assay ( indicated with a hatched blue line in all relevant figures in this work; [6] ) . When the INO1 gene is artificially tethered to the nuclear envelope , we observe peripheral localization in 81% ± 7% of cells [6] . Therefore , the dynamic range of the chromatin localization assay is between 25% and 80% . For this reason , data from chromatin localization experiments were plotted on an axis from 20%–80% . The repressed INO1 gene distributes randomly , co-localizing with the nuclear envelope in 31% ± 1% of cells in the population ( Figure 1A , +inositol; [6] ) . The activated INO1 gene is recruited to the nuclear periphery , co-localizing with the nuclear envelope in 60% ± 5% of cells in the population ( Figure 1A , −inositol; [6] ) . We used the chromatin localization assay to compare the change in the peripheral localization of INO1 with the change in transcription after shifting cells from repressing to activating conditions . We quantified the levels of INO1 mRNA relative to ACT1 mRNA by reverse transcriptase real-time quantitative PCR ( RT Q-PCR ) . After shifting cells into medium lacking inositol , INO1 mRNA levels increased slowly for the first 2 . 5 h ( Figure 1B , left panel ) . The mRNA levels then increased more rapidly over the next several hours and reached steady state after 5–6 h ( unpublished data ) . Recruitment of INO1 to the nuclear periphery was rapid . The fraction of cells in which INO1 localized to the nuclear periphery increased approximately 10% in the first 5 min after shifting cells to the activating condition and was complete after 60 min . Therefore , INO1 recruitment to the periphery occurred prior to the rapid accumulation of mRNA . However , plotting the data on a logarithmic scale revealed that there was a substantial fold increase in the concentration of the mRNA during this time , consistent with the possibility that mRNA production might lead to recruitment ( Figure 1B , right panel ) . We conclude that ( 1 ) INO1 was activated quickly , resulting in an approximately 50-fold increase in the mRNA level over the first 45 min , ( 2 ) recruitment of INO1 to the nuclear periphery correlated with this early increase , and ( 3 ) the maximal rate of INO1 mRNA accumulation occurred after relocalization was complete . We next adapted the chromatin localization assay to compare the localization and transcriptional activation of the GAL1 gene , which is repressed in cells grown in glucose and expressed in cells grown in galactose . We integrated the lac repressor–binding site array downstream of the GAL1 gene and quantified its co-localization with the nuclear envelope as in Figure 1A . Repressed GAL1 localized at the nuclear periphery in 35% ± 1% ( five replicates of 30–50 cells ) of cells , and activated GAL1 localized at the nuclear periphery in 70% ± 2 . 5% ( three replicates of 30–50 cells ) of cells ( unpublished data ) . When cells were shifted from glucose to galactose , GAL1 mRNA levels increased slowly for the first 60 min and then more rapidly , reaching steady state after approximately 2 h ( Figure 1C ) . Like INO1 , GAL1 was recruited to the nuclear periphery rapidly , increasing approximately 15% in the first 5 min after shifting cells to galactose medium ( Figure 1C ) . Peripheral localization increased to 56% ± 2% after 60 min ( Figure 1C ) and continued to increase to 70% over the next 2 h ( Figure S1 ) . Therefore , like INO1 , the rate of accumulation of GAL1 mRNA was fastest after recruitment to the nuclear periphery . We next tested how localization to the nuclear periphery changed after repressing transcription ( Figure 2 ) . Both GAL1 and INO1 are repressed rapidly [23 , 24] . After addition of inositol to cells expressing INO1 , the mRNA levels decreased quickly , with no lag phase , and returned to the fully repressed level within 30 min ( Figure 2A ) . Likewise , in cells shifted from galactose to glucose , the GAL1 mRNA levels dropped rapidly , with no lag phase ( Figure 2C ) . However , both INO1 and GAL1 remained localized at the nuclear periphery for more than 2 h after repressing transcription ( Figure 2B and 2D ) . This persistent localization at the nuclear periphery suggested that these genes are actively retained . The rapid relocalization of both genes upon shifting cells to activating conditions ( Figure 1 ) indicates that they are capable of rapidly changing their distribution . Furthermore , the diffusion coefficient of repressed GAL1 is approximately 0 . 18 μm2/min [9] . This mobility would predict that , in the absence of an active mechanism of retention , GAL1 should assume a random distribution within minutes of shifting the cells from activating to repressing conditions . To rule out the possibility that the localization of INO1 to the nuclear periphery after repressing transcription was due to very low levels of transcription , we analyzed the localization of INO1 after inactivating a temperature-sensitive version of RNA polymerase II . RNA polymerase II–mediated transcription is blocked within 5 min after shifting rpb1–1 mutant cells to the non-permissive temperature ( Figure S2 and [25] ) . We grew rpb1–1 cells in the absence of inositol to activate INO1 expression , and then shifted the cells to the non-permissive temperature and quantified the localization of INO1 to the nuclear periphery over time . After 30 min at the non-permissive temperature , the INO1 gene remained localized to the nuclear periphery in 60% ± 3% of the cells , despite a 5-fold decrease in INO1 mRNA levels ( Figure 2E and Figure S2A ) . Therefore , ongoing transcription is not required to maintain INO1 at the nuclear periphery . To test if transcription is required to establish INO1 recruitment , we inactivated RNA polymerase II for 15 min before shifting cells into the activating condition . This treatment completely blocked INO1 activation , resulting in an approximately 420-fold difference in the levels of INO1 mRNA ( Figure S2B ) . In the absence of RNA polymerase II function , the INO1 gene was still recruited rapidly to the nuclear periphery ( Figure 2F ) . These results indicate that transcription is not required for either the establishment or maintenance of gene recruitment to the nuclear periphery . This conclusion is consistent with studies of the interaction of the nucleoporin Nup2 with the GAL1 promoter [13] . We next tested if the lingering localization of INO1 and GAL1 at the nuclear periphery was inherited . We quantified the peripheral localization of both INO1 and GAL1 in cells that had repressed transcription through several cell divisions . Cells were maintained in logarithmic growth by continual dilution , and their doubling time was approximately 110 min . The INO1 gene remained localized at the nuclear periphery in more than 50% of the cells after 6 h of repression and then returned to a random distribution after 12 h ( Figure 3A ) . Therefore , localization of INO1 at the nuclear periphery was maintained through at least three to four cell divisions . The retention of the GAL1 gene was even more stable , remaining localized at the nuclear periphery in more than 60% of cells after 12 h of repression ( Figure 3A ) . This suggests that GAL1 is maintained at the nuclear periphery indefinitely in logarithmically growing cells . Consistent with this indefinite switch , we find that GAL1 remained localized at the nuclear periphery for greater than 120 h , or approximately 65 generations ( Figure S3 ) . Therefore , the localization of INO1 and GAL1 at the nuclear periphery is stably maintained after repressing transcription and is inherited by subsequent generations , suggesting that it represents an epigenetic state . Our data suggest that there are two different forms of repressed INO1 and GAL1 . Whereas INO1 and GAL1 that have been repressed for many generations distribute randomly within the nucleus , recently repressed INO1 and GAL1 localize at the nuclear periphery . Therefore , peripheral localization distinguishes between recently repressed and long-term repressed states . This raised the possibility that localization might function as an epigenetic marker to allow cells to “remember” recent transcription of these genes , potentially affecting their rate of reactivation . To test this idea , we compared the rate of transcriptional activation of long-term repressed and short-term repressed GAL1 . The rate of reactivation of GAL1 in cells in which the gene had been repressed for 12 h ( six to seven generations ) was much more rapid than in cells grown continuously in glucose ( Figure 3B ) . Thus , in a culture in which only approximately 1% of the cells have previously experienced galactose , the reactivation of the GAL1 gene is enhanced . We next compared the rate of activation of long-term repressed INO1 to the rate of reactivation of short-term repressed INO1 after 3 h of repression ( ∼1 . 5 generations ) . In contrast to GAL1 , the reactivation of the INO1 gene after 3 h of repression was delayed compared with activation of the long-term repressed gene ( Figure 4A ) . However , this rate of reactivation was clearly enhanced by the localization at the nuclear periphery . Nup2 , a component of the nuclear pore complex that physically associates with transcriptionally active genes such as GAL1 [7 , 13] , is required for recruitment of both INO1 and GAL1 to the nuclear periphery ( Figure 4B ) . Mutants lacking Nup2 exhibited a delay in the reactivation of INO1 ( Figure 4C ) , suggesting that recruitment to the nuclear periphery promotes more rapid reactivation . To determine if recruitment to the nuclear periphery is sufficient to promote activation , we compared the activation of INO1 that was artificially tethered to the nuclear envelope to untethered INO1 . The lac repressor array was integrated beside INO1 in strains expressing either the wild-type Lac I-GFP ( untethered INO1 ) or a modified version possessing an FFAT motif to target the protein to the nuclear envelope ( tethered INO1; [6 , 26] ) . Expressing this form of the lac repressor results in efficient targeting of the INO1 gene to the nuclear envelope [6] . Tethering the INO1 gene to the nuclear envelope had no effect on steady-state levels of INO1 mRNA under activating or repressing conditions ( Figure S4 ) . However , tethered INO1 was activated more rapidly than untethered INO1 ( Figure 4D ) . Therefore , localization at the nuclear periphery enhances the rate of reactivation of INO1 . If so , then why is the rate of reactivation of recently repressed INO1 slower than the rate of activation of long-term repressed INO1 ? This difference is likely due to differences in the physiology of cells grown continuously in the presence of inositol and cells to which inositol has recently been added . Activation of INO1 is regulated by the concentration of phosphatidic acid , a lipid precursor of phosphatidylinositol [27] . Phosphatidic acid consumption is stimulated by both exogenous inositol and the action of the Ino1 enzyme [27] . After repressing INO1 transcription , the Ino1 enzyme in the cells will continue to produce inositol , driving a higher flux through the pathway and depleting phosphatidic acid . We think this may explain the longer lag phase in the reactivation experiment , which represents the time required for phosphatidic acid to accumulate to levels that activate transcription . This feedback , combined with the shorter duration of the memory phenomenon for the INO1 gene , complicates a direct comparison between the rate of activation between the short- and long-term repressed states of INO1 . To explore the molecular nature of the difference between short-term and long-term repressed INO1 , we asked if remaining at the nuclear periphery after repression affects the chromatin state of the gene . Because nucleosome remodeling is important for both INO1 activation and repression [28–32] , we compared the positioning of nucleosomes within the short-term repressed and long-term repressed INO1 promoter . Permeablized cells were treated with micrococcal nuclease for various times to digest unprotected DNA ( Materials and Methods ) . As an internal control for nucleosome protection , we used a known , well-positioned nucleosome within the GAL1 promoter ( GAL NB; Figure 5A; [33] ) and an adjacent , non-nucleosomal sequence ( GAL I; Figure 5A ) . Using Q-PCR to define the concentration of these two sequences in our digestion , we observed protection of the nucleosomal sequence relative to the non-nucleosomal sequence ( Figure 5A , left panel ) . Furthermore , after 15 min and 30 min of digestion , we observed the production of clear mononucleosome and dinucleosome bands , indicating that nucleosomes were providing protection from the nuclease and that linker DNA had been digested ( Figure 5A , right panel , arrows ) . Previous studies have established that relative nucleosomal protection is observable over a large range of digestion and with or without gel purification of mononucleosomes [34] . Therefore , we used Q-PCR and a set of 27 different primer pairs to amplify overlapping 80–100 base pair fragments from the INO1 promoter ( Table S1 ) . The concentration of each of the templates for these 27 primer pairs was quantified after 30 min of digestion . The protection of each template was calculated relative to the GAL NB sequence . Using this method , we identified one well-positioned nucleosome within the INO1 promoter and one at the junction between the promoter and the transcript ( Figure 5B ) . Comparison between short-term and long-term repressed INO1 revealed no significant change in the positioning of these nucleosomes . However , we did observe a decrease in the relative protection provided by these two nucleosomes in the short-term repressed state ( Figure 5C ) . This difference resulted in a 2-fold decrease in the protection at these two sites relative to the GAL NB site . This difference may reflect either an increase in the fraction of cells in the population in which these nucleosomes are absent , or a change in the stability of these nucleosomes in the lysates subjected to nuclease digestion . The positioning of the pair of nucleosomes present in the INO1 promoter suggested that they might contain the histone H2A variant H2A . Z . H2A . Z is incorporated into pairs of nucleosomes that are typically found in the promoters of repressed genes , and incorporation of H2A . Z has been proposed to promote more rapid activation [35–38] . However , genome-wide chromatin immunoprecipitation experiments did not demonstrate a strong association of H2A . Z with the long-term repressed INO1 promoter [37] . Yeast H2A . Z is encoded by the non-essential HTZ1 gene [39] . To test if H2A . Z is important for transcriptional memory , we compared the rates of reactivation of recently repressed INO1 and GAL1 in wild-type and htz1Δ mutant cells ( Figure 6 ) . Loss of H2A . Z led to a strong delay in the rate of reactivation of both short-term repressed INO1 and short-term repressed GAL1 ( Figure 6A and 6E ) . Surprisingly , loss of H2A . Z had no effect on the rate of activation of long-term repressed INO1 or GAL1 ( Figure 6B and 6F ) . These results suggest that H2A . Z plays an important and specific role in the reactivation of these genes . H2A . Z is exchanged for H2A within intact nucleosomes by the SWR1 ATPase complex [40–42] . To test if SWR1 plays a role in the H2A . Z-dependent reactivation of INO1 , we next tested the effect of loss of SWR1 on INO1 activation and reactivation . We find that swr1Δ mutant strains were also defective for reactivation of recently repressed INO1 ( Figure 6C ) , and had little effect on the activation of long-term repressed INO1 ( Figure 6D ) . To examine the deposition of H2A . Z nucleosomes in the INO1 promoter , we used chromatin immunoprecipitation with antiserum against Htz1 . Consistent with previous work , immunoprecipitation of H2A . Z from either long-term repressed cells or the activated cells gave low recovery of the INO1 promoter ( Figure 7A ) . In contrast , immunoprecipitation of H2A . Z from recently repressed cells gave a clear enrichment for the INO1 promoter ( Figure 7A ) , suggesting that H2A . Z is specifically incorporated into promoter nucleosomes in the recently repressed state . We next tested if H2A . Z had any role in the localization of the INO1 gene . Loss of H2A . Z had no effect on recruitment of activated INO1 to the nuclear periphery ( Figure 7B ) . This is not surprising since the histone variant generally associates with repressed genes ( Figure 7A; [35–38] ) . However , cells lacking H2A . Z were unable to retain INO1 at the nuclear periphery after repressing transcription ( Figure 7C ) . Therefore , H2A . Z nucleosomes in the recently repressed INO1 promoter function both to retain recently repressed INO1 at the nuclear periphery and to promote optimal reactivation . Our results show that the recruitment of genes to the nuclear periphery is a rapid , active process that is independent of transcription . The most robust transcription of the GAL1 and INO1 genes occurred after these genes had fully relocalized to the nuclear periphery , suggesting that recruitment to this subnuclear environment allows optimal expression of these genes . Furthermore , both genes remained at the periphery for generations after repressing transcription , suggesting that cells can inherit localization information . Retention of the INO1 gene and optimal reactivation of both INO1 and GAL1 required the histone variant H2A . Z , which associated with nucleosomes within the recently repressed INO1 promoter . Thus , cells have both molecular and cellular sources of memory of past transcriptional activation , and they are able to pass on this information to their progeny . This type of memory is mediated by local changes in chromatin structure that mark recently repressed genes to alter their transcriptional potential and localization , and perhaps to provide a mechanism for inheritance . What is the functional significance of this epigenetic memory ? In the case of the GAL1 gene , the recently repressed state is reactivated much more rapidly than the long-term repressed state , which presumably confers an adaptive advantage upon cells that have previously grown in galactose . We do not see this for INO1 , perhaps because physiological differences between recently repressed and long-term repressed cells complicates the comparison of the rate of INO1 activation and reactivation . However , we can conclude that ( 1 ) there are two distinct states of repressed INO1 and GAL1 , distinguishable by their localization , their transcriptional histories , and the molecular requirements for activation , ( 2 ) localization of INO1 at the nuclear periphery is necessary and sufficient to promote more rapid activation , and ( 3 ) incorporation of H2A . Z is the molecular mechanism of transcriptional memory , retaining INO1 at the nuclear periphery and promoting reactivation of both INO1 and GAL1 . Histone variant H2A . Z is enriched in pairs of nucleosomes within the promoters of repressed genes [35–38] . The histone appears to play an important role in the loss of nucleosomes from promoters upon their activation [37] . This observation , coupled with the fact that H2A . Z nucleosomes are less tightly bound to DNA than H2A nucleosomes , suggests that H2A . Z nucleosomes promote activation by being more easily removed [37] . We find that H2A . Z deposition and function can depend on the transcriptional history of the promoter into which it is incorporated . H2A . Z is required for rapid reactivation of short-term repressed INO1 and GAL1 and for retention of recently repressed INO1 at the nuclear periphery . It is possible that these results represent an indirect effect of loss of H2A . Z . However , we think H2A . Z most likely plays a direct role in promoting the reactivation of INO1 and GAL1 because ( 1 ) loss of H2A . Z ( and SWR1 ) affects reactivation of recently repressed INO1 and GAL1 , but not the activation of long-term repressed INO1 and GAL , 1 and ( 2 ) H2A . Z physically associates with the recently repressed INO1 promoter . Therefore , we have identified a new and novel role for this histone variant: H2A . Z can serve as a molecular identifier of recently repressed genes to promote their retention at the nuclear periphery and their rapid reactivation . Our current model for the mechanism of gene recruitment and transcriptional memory is shown in Figure 8 . In response to signals that regulate transcriptional activation , genes physically interact with the nuclear pore complex via the mobile nucleoporin Nup2 . Recruitment to the nuclear periphery allows access to the optimal subnuclear environment for transcription and , potentially , for mRNA export . After transcription is repressed , previous transcriptional activation of genes such as INO1 and GAL1 is remembered through retention in this optimal environment . Localization at the nuclear periphery is epigenetically inherited and requires incorporation of histone variant H2A . Z . Finally , the reactivation of INO1 and GAL1 is optimized by both localization at the periphery and through more rapid loss of H2A . Z nucleosomes [37] . What is the role of DNA localization in promoting transcriptional memory ? Our data suggest two possible models for how peripheral localization affects H2A . Z-mediated transcriptional memory . In the first model , H2A . Z incorporation into promoter nucleosomes is promoted by Nup2-mediated gene recruitment to the nuclear periphery , and functions to promote reactivation by altering the rate of nucleosome loss or local histone modifications . This model is consistent with several observations in the literature . Tethering of Nup2 to DNA promotes “boundary activity , ” insulating euchromatin from the spread of heterochromatin [43 , 44] . Intriguingly , one of the most dramatic phenotypes of mutants lacking either Nup2 or H2A . Z is the spread of silenced heterochromatin [43 , 45] . Thus , it is possible that tethering genes to the nuclear periphery through Nup2 leads to the incorporation of H2A . Z nucleosomes , which functions as a boundary . Furthermore , it is possible that boundary elements normally associate with the NPC . We find that H2A . Z is involved in both the activation of recently repressed genes and their retention at the nuclear periphery . Thus , a second model for the importance of H2A . Z is that H2A . Z nucleosomes promote reactivation of recently repressed genes by retaining them in the optimal environment for transcriptional activation . These models are not mutually exclusive , and we favor the possibility that H2A . Z incorporation is promoted by localization and that , once incorporated , H2A . Z affects localization . Transcriptional memory is employed extensively during development in multi-cellular organisms . In Drosophila , Hox gene expression throughout development is determined early in embryogenesis by transcriptional regulators that control segmentation [46] . The initial expression states defined by the segmentation genes are maintained by the action of either polycomb group proteins ( generally repressive ) or trithorax group proteins ( generally activating ) through a number of chromatin-based mechanisms such as nucleosome positioning and histone modification [47] . Similarly , the variant histone H3 . 3 is incorporated selectively into transcriptionally active parts of the genome , which may promote the epigenetic maintenance of an activated state [20 , 48] . Like these forms of transcriptional memory , the transcriptional memory described here is mediated by chromatin-based changes that mark recently repressed genes and distinguish them from long-term repressed genes . However , unlike these forms of memory , which serve to maintain a previously established transcriptional state , the transcriptional memory described here serves an informational role , revealing previous transcriptional activity and altering the transcriptional potential of previously expressed genes . Previous work has hinted that transcriptional activity of GAL1 can alter the degree of methylation of histone H3 , marking the chromatin for hours after repressing transcription [19] . However , in this case , the mark was lost after cell division . Our data suggest that the past experiences of microbial organisms can affect their cellular organization and their physiology for many generations . The efficiency of inheritance of the memory state was different for the two genes we examined , suggesting that there are different timing mechanisms for each . In the case of the GAL1 gene , after exposure to galactose , logarithmically growing cells appeared to undergo an indefinite switch to the recently repressed state . It will be fascinating to determine if there are conditions or stimuli that can reset the GAL1 gene to the long-term repressed state . In contrast , the transcriptional memory of INO1 activation was relatively short lived . The previous transcriptional state of INO1 is imprinted in its chromatin and its subnuclear localization for 6 h or more ( two to three cell doublings ) , but this information is eventually lost . Why do cells optimize reactivation of genes ? We speculate that rapid reactivation of certain genes confers an adaptive , and therefore an evolutionary , advantage to cells . This might be particularly important in the case of stress-responsive genes such as INO1 or genes involved in metabolizing non-glucose hexose sugars . Also , epigenetic mechanisms may be useful in allowing cells to alter their transcriptional output rapidly under highly variable environmental conditions or under physiological circumstances in which they rapidly undergo reversible changes in physiology [49] . It will be interesting to see if this mechanism is also operative in metazoan organisms , perhaps to establish epigenetically “primed” states for dynamically regulated genes in response to transient physiological or environmental cues . Unless stated otherwise , chemicals were from Sigma ( St . Louis , Missouri , United States ) , oligonucleotides were from Operon ( Huntsville , Alabama , United States ) , restriction enzymes were from New England Biolabs ( Ipswitch , Massachusetts , United States ) , yeast media components were from Q-Biogene ( Irvine , California , United States ) , antibodies against GFP and myc were from Invitrogen/Molecular Probes ( Carlsbad , California , United States ) , and antiserum against Htz1 was from Abcam ( Cambridge , Massachusetts , United States ) . Yeast strains used in this study are listed in Table 1 . Except for BY4741 , BY4741 htz1 , Δ and BY4741 swr1Δ [50] , all strains were constructed from CRY1 ( ade2–1 can1–100 his3–11 , 15 leu2–3 , 112 trp1–1 ura3–1 MATa ) [51] . Strain JBY451 is the product of a cross between JBY376 ( ade2–1 can1–100 his3–11 , 15 leu2–3 , 112 trp1–1 ura3–1 INO1:LacO128:URA3 HIS3:LacI-GFP MATa ) and BY4742 nup2Δ mutant strain ( his3Δ1 leu2Δ0 lys2Δ0 ura3Δ0 nup2Δ::Kan̂r MATα ) from the genome-wide null mutant collection [50] . Random spores JBY451-r1 and JBY451-r7 were selected . For JBY451-r7 , the identity of the ura3 allele was confirmed to be ura3–1 by transforming JBY451-r7 with StuI-digested pRS306 [52] . Strain JBY462 was created by transforming JBY451-r1 with pRS304-Sec63-myc digested with NheI . Strain JBY467 was created by transforming JBY451-r7 with p6LacO128GAL1 and pRS304-Sec63-myc . Finally , strains JBY451-r1 , JBY451-r7 , JBY462 , and JBY467 were confirmed to be nup2Δ by PCR from genomic DNA . Strain JBY461 is the product of a cross between JBY397 [6] and JCY218 [53] . Random spores were selected that were Ura+ Trp+ His+ and temperature sensitive for growth ( JBY461-r2 ) . These were then visually scored for expression of Lac I-GFP . Strain DBY50 is the product of a cross between DBY49 ( htz1Δ::His5+ ade2–1 can1–100 his3–11 , 15 leu2–3 , 112 trp1–1 ura3–1 MATα ) and JBY397 . The resulting diploid was sporulated , and tetrads were dissected to generate DBY50 . Plasmids p6LacO128 [6] , p6LacO128-INO1 [6] , pAFS144 [21] , and pAFS144-FFAT [6] have been described . To create the plasmid p6LacO128-GAL1 to mark the GAL1 gene with the lac repressor–binding site array , the 3′ end of the GAL1 gene , and downstream sequences were amplified by PCR using the following primers ( 5′ to 3′ ) : GAL1up , GTTCAAACCGCAGTTGAAGG and GAL1down , CCGAAAGATCTTCTCTATGGGG . The resulting PCR product was cloned into the TOPO4 vector ( Invitrogen ) . This was then cloned into p6LacO128 as a SpeI fragment . The plasmid was integrated downstream of GAL1 by digestion with NruI . Plasmid pRS304-Sec63-myc was created by amplifying SEC63-13myc from JBY397 genomic DNA using the following primers ( 5′ to 3′ ) : SEC63up: GTATTTCGGAGAGGGGGC; Pringledown: ACTATACCTGAGAAAGCAACCTGACCTACA . The resulting PCR product was TOPO cloned into pCR2 . 1 ( Invitrogen ) . The insert was then cloned into pRS304 [52] as a NotI-KpnI fragment . The plasmid was digested with NheI to target integration at SEC63 . Unless noted otherwise , all experiments were performed on cells grown in synthetic complete medium at 30 °C . For experiments involving INO1 , cells were grown in medium lacking inositol or supplemented with 100 μM myo-inositol . For experiments involving GAL1 , cells were grown in media with either 2% glucose or 2% galactose . RNA was prepared as described [54] . A total of 2–4 μg of DNAse-treated total RNA was reverse transcribed using 5 μM Oligo dT and 20 units of Superscript III reverse transcriptase ( Invitrogen ) at 42 °C for 1 h . The reaction was diluted 5-fold , and 1/20th was used for Q-PCR . The sequences of the primers used for real-time PCR were ( 5′ to 3′ ) : INO1CDS F , TAGTTACCGACAAGTGCACGTACAA; INO1CDS R TAGTCTTGAACAGTGGGCGTTACAT; ACT1CDS F , GGTTATTGATAACGGTTCTGGTATG; ACT1CDS R , ATGATACCTTGGTGTCTTGGTCTAC; GAL1CDS F , GTTCGATTTGCCGTTGGACGG; GAL1CDS R , GGCAAACCTTTCCGGTGCAAG . The relative concentration of cDNA templates for both the target gene ( INO1 or GAL1 ) and the control gene ( ACT1 ) were calculated for each sample using standard curves for each primer set that were defined by linear regression analysis of Ct values using a series of 5-fold dilutions of yeast genomic DNA covering a 3 , 125-fold range . Long-term repressed cells were harvested at an optical density ( OD600 ) of 0 . 8–1 . 0 from 1 l of SDC + inositol . Short-term repressed cells were grown in 1 l of SDC − inositol to an OD600 of 0 . 7 , and inositol was added to 100 μM . After 1 h of repression , cells were harvested by filtration . Cell permeablization and micrococcal nuclease digestion were performed as described , except that DNA was not size selected [55] . Q-PCR analysis on digested DNA was performed using the oligonucleotides listed in Table S1 . To map the protected sequences onto the INO1 promoter , we used the experimentally determined transcriptional start site and initiation codon [56 , 57] . Chromatin immunoprecipitation experiments were performed using anti-Htz1 antiserum ( Abcam ) as described [37] , with the following modifications: 2 μg of anti-Htz1 were used to immunoprecipitate Htz1 from 4 . 8 mg of chromatin , and immunoprecipitates were recovered using Protein G-dynabeads ( Invitrogen ) . Immunoprecipitated DNA was recovered and analyzed by Q-PCR as described [6] . Recovered INO1 promoter was expressed relative to recovered ACT1 coding sequence .
Eukaryotic cells control the spatial arrangement of chromosomes; the localization of genes can both reflect and contribute to their transcriptional state . A number of genes in the simple eukaryote brewer's yeast are “recruited” to the nuclear periphery through interactions with the nuclear pore complex when they are expressed . The functional significance of peripheral recruitment is unclear . Here , we show that recruited genes are actively retained at the periphery for generations after transcription is repressed . This suggests that localization at the nuclear periphery represents a novel inherited state that might allow simple eukaryotic organisms to “remember” previous transcriptional activation . This type of memory allows for more robust reactivation of genes , suggesting that it is adaptive . Finally , both retention at the nuclear periphery and rapid reactivation require a variant form of histone H2A . Adaptive memory is distinct from other types of transcriptional memory . In developmental memory , transcriptional states established by transcriptional regulators early in embryogenesis are propagated long after these regulators have disappeared . Adaptive memory does not propagate a state , but represents a novel state that serves as a source of information . In this way , it resembles a rudimentary form of cellular learning that allows cells to benefit from recent experience .
You are an expert at summarizing long articles. Proceed to summarize the following text: Clinical reports of Zika Virus ( ZIKV ) RNA detection in breast milk have been described , but evidence conflicts as to whether this RNA represents infectious virus . We infected post-parturient AG129 murine dams deficient in type I and II interferon receptors with ZIKV . ZIKV RNA was detected in pup stomach milk clots ( SMC ) as early as 1 day post maternal infection ( dpi ) and persisted as late as 7 dpi . In mammary tissues , ZIKV replication was demonstrated by immunohistochemistry in multiple cell types including cells morphologically consistent with myoepithelial cells . No mastitis was seen histopathologically . In the SMC and tissues of the nursing pups , no infectious virus was detected via focus forming assay . However , serial passages of fresh milk supernatant yielded infectious virus , and immunohistochemistry showed ZIKV replication protein associated with degraded cells in SMC . These results suggest that breast milk may contain infectious ZIKV . However , breast milk transmission ( BMT ) does not occur in this mouse strain that is highly sensitive to ZIKV infection . These results suggest a low risk for breast milk transmission of ZIKV , and provide a platform for investigating ZIKV entry into milk and mechanisms which may prevent or permit BMT . Zika virus ( ZIKV ) is an enveloped virus with a positive-sense , single-stranded RNA genome [1] . For over half a century , this flavivirus was regarded as an arbovirus leading to self-limiting , febrile disease . However , confirmation of or association with new syndromes , including teratogenesis , adult Guillain Barre Syndrome , genital persistence , and sexual transmission , have begun to emerge since the 2015–2016 Brazil ZIKV outbreak . Due to devastating outcomes associated with infection of the developing brain and ZIKV’s apparent ability to cross intact mucosae [2–4] , a key question arises: can ZIKV be transmitted by breast milk ? Reports of ZIKV RNA detection in breast milk are accumulating [5–10] . Although no epidemiologic data regarding ZIKV in lactating women are currently available , ZIKV RNA has been reported in breast milk from 3 [5 , 9] to 33 [6] days after maternal onset of fever . Reports conflict as to whether isolated ZIKV RNA represents infectious virus [7] . In one study , cytopathic effect ( CPE ) could not be demonstrated in cells cultured with either of the breast milk samples from two mothers who nursed infected infants [9] . In two separate reports , CPE was seen upon culturing of cells with breast milk of mothers with uninfected nursing children [8 , 10] . In another study , CPE was demonstrated in cells cultured with milk from a ZIKV-infected mother , and the nursing child was infected with an isolate with ZIKV genome identity of more than 99% between the infected mother and child [5] . Historically , the epidemiology and mechanisms of flavivirus breast milk transmission ( BMT ) have posed somewhat of a scientific enigma . Hepatitis C virus or Japanese encephalitis virus BMT has not been documented , whereas West Nile virus [11] and yellow fever vaccine strain [12] BMT have been reported . Dengue virus ( DENV ) infects approximately 390 million people annually and DENV RNA has been detected in breast milk [13] , but reports of BMT are rare . Furthermore , in the 1970s , two studies also demonstrated that DENV and Japanese encephalitis virus were neutralized by the lipid fraction of breast milk [14 , 15] . In this study , we explored a mouse model for BMT of ZIKV using AG129 mice that are deficient in both type I and II interferon ( IFN ) receptors , and represent a highly sensitive animal model of ZIKV challenge [16] . Following infection of AG129 dams with ZIKV on the date of parturition , viral RNA was detected in pup stomach milk clots ( SMC ) as early as 1 day post maternal infection ( dpi ) and as late as 7 dpi . In contrast , ZIKV NS2B immunofluorescent immunohistochemistry ( IHC ) and examination for CPE of inoculated Vero cells and focus forming assay did not demonstrate infectious virus in fresh milk or in nursing pups . Enzyme IHC provided evidence of intracellular viral replication ( i . e . ZIKV NS2B expression ) in cells morphologically consistent with epithelial cells , myoepithelial cells , and macrophages within the mammary gland . ZIKV NS2B expression was observed also in the SMC , and infectious particles were observed in fresh milk samples after 3 serial passages in Vero cells . The detection of potentially infectious ZIKV in the milk of this mouse model suggest that infectious virus may be present in human breast milk . However , BMT did not occur in this highly stringent ZIKV challenge system . These results suggest a low risk for human BMT of ZIKV , and set the stage for investigating ZIKV entry into milk and mechanisms by which BMT are prevented or permitted . 129/Sv mice deficient in type I and type II IFN receptors ( AG129 ) were bred and maintained at the La Jolla Institute for Allergy & Immunology ( LJI ) under standard pathogen free conditions . LJI has established an animal care and use program in compliance with The Public Health Service Policy on the Humane Care and Use of Laboratory Animals and maintains an animal welfare assurance with the Office of Laboratory Animal Welfare ( OLAW ) . The animal care and use program is guided by the US Government Principles for the Utilization and Care of Vertebrate Animals Used in Testing , Research and Training and by the 8th edition of the Guide for the Care and Use of Laboratory Animals . As such , all research involving animals is reviewed and approved by the IACUC in accordance with The PHS policy on the Humane Care and Use of Animals and the 8th edition of The Guide . In addition , LJI’s animal care and use program is accredited by AAALAC International . All experiments involving these mice were approved by the Institutional Animal Care and Use Committee under protocol no . AP028-SS1-0615 . Samples sizes: Fig 1 ( 1A to 1D: 3 pups per group from 3 separate mothers , 1E: 3 pups per group from 3 other separate mothers ) , Fig 2 ( 3 mothers per group ) , Fig 3 ( 3 mothers per group ) , Fig 4 ( 4A and 4B: 6 pups per group from 3 separate mothers , 4C and 4D: 3 pups per group from 3 separate mothers , 4E to 4F: other 3 pups per group from 3 separate mothers ) . Fig 5 ( 5A to 5C: images representative from 3 independent experiments , 5D: 3 mothers per group , 5E: 10 pups per group from 3 separate mothers ) . Animal experiments were not randomized or blinded . ZIKV strain FSS13025 ( Cambodia , 2010 ) was obtained from the World Reference Center for Emerging Viruses and Arboviruses ( WRCEVA ) . This strain was isolated from a pediatric case [17] . ZIKV was cultured using C6/36 Aedes albopictus mosquito cells as described previously [18] . Viral titers were determined by using baby hamster kidney ( BHK ) -21 cell-based focus forming assay ( FFA ) [19] . Eight-week-old female mice were infected retro-orbitally ( r . o . ) with 1 x 102 focus forming units ( FFU ) of ZIKV FSS13025 in 200 μl 10% FBS/PBS . African green monkey kidney-derived Vero E6 cells were purchased from ATCC . Vero cells were grown in Dulbecco's modified Eagle's medium ( DMEM , GIBCO ) supplemented with 1% HEPES , 1% penicillin/streptomycin ( GIBCO ) and 10% fetal bovine serum ( FBS , Gemini's BenchMark ) at 37°C , 5% CO2 . Mouse organs were collected in 800 μl RNA later ( Ambion ) and the tissues were transferred to 1% BMe/RLT buffer . Maternal mammary gland , brain , spleen , and the pup body minus the head and stomach were each placed in 800 μl . Before analysis , the skin of the head and rest of the body tissues were removed to avoid contamination from the mother’s saliva . SMC were removed from the pup’s stomach for separate analysis . The pups heads and stomachs tissues were processed in 250 μl ( stomach was washed twice with PBS in order to remove remnant milk ) . The tissues were next homogenized for 3 minutes using Tissuelyser II ( Qiagen Inc . ) and centrifuged for 1 minute at 6 , 000 g . Tissue samples , SMC , and serum from ZIKV-infected mice were extracted with the RNeasy Mini Kit ( tissues ) or Viral RNA Mini Kit ( serum and SMC ) ( Qiagen Inc . ) . Real-time qRT-PCR was performed using the qScript One-Step qRT-PCR Kit ( Quanta BioSciences ) and CFX96 TouchTM real-time PCR detection system ( Bio-Rad CFX Manager 3 . 1 ) . A published primer set was used to detect ZIKV RNA ( Lanciotti , 2008 ) . Fwd , 5’-TTGGTCATGATACTGCTGATTGC-3’; Rev , 5’-CCTTCCACAAAGTCCCTATTGC-3’ and Probe , 5’-6-FAM-CGGCATACAGCATCAGGTGCATAGGAG-Tamra-Q-3’ . Cycling conditions were as follows: 45°C for 15 min , 95°C for 15 min , followed by 50 cycles of 95°C for 15 sec and 60°C for 15 sec and a final extension of 72°C for 30 min . Viral RNA concentration was determined based on an internal standard curve composed of five 100-fold serial dilutions of an in vitro transcribed RNA based on ZIKV FSS13025 . The mammary gland was collected at 5 days post-infection ( dpi ) and was fixed in PFA for 24 hr at 4°C . ZIKV-infected tissues and mock-infected tissues were obtained . Tissues were processed and stained according to standard Visikol HISTO process ( protocol . visikol . com ) for antibody labeling . Tissues were immersed in Visikol Permeabilization Buffer at room temperature overnight . The following day , 2 mm thick tissue sections were processed through a series of washing steps of increasing methanol concentrations ( 50% , 80% , 100% ) , followed by permeation with 20% DMSO in methanol , and subsequently decreasing concentrations of methanol and back into PBS 1X . Tissues were then incubated in Visikol Penetration Buffer for 12 hr , washed with PBS , and incubated at 37°C in Visikol Blocking Buffer™ for 12 hr . Tissues were then transferred to microcentrifuge tubes for antibody labeling . The primary antibodies Smooth Muscle Actin ( αSMA ) ( Invitrogen; goat polyclonal ) and anti-ZIKV NS2B ( GeneTex; rabbit polyclonal ) were diluted at 1:100 in Visikol Antibody Buffer , and tissues were incubated at 37°C for 7 days . Tissue sections were washed in 1X Visikol Washing Buffer and then transferred to the secondary labeling solution . Secondary antibodies ( DyLight 488 conjugated anti-goat and Alexa 594 conjugated anti-rabbit IgG-Invitrogen ) were diluted at 1:200 in Antibody Buffer and the samples were incubated for another 3 days along with DAPI counterstain at 1:1000 dilution ) . Tissues were washed and cleared for imaging using ( LSCM ) . For clearing , both control and infected tissues were dehydrated with sequentially increasing concentrations of methanol ( i . e . 50% in PBS , 80% in H2O , 100% ) for 30 min in each step , followed by incubation in Visikol HISTO-1 for 12 hours , and then into Visikol HISTO-2 . Tissues were mounted in Visikol HISTO-2 and imaged using a Leica SP5 LSCM ( laser scanning confocal microscope ) using DAPI , Argon-488 , and 594 nm lasers with 10X and 20X magnification objectives . The mammary gland was collected at 5 , 7 , 9 and 11 dpi; SMC were collected at 5 dpi; and mock-infected samples were prepared . Tissues were fixed in zinc formalin for 24 hr at room temperature . Tissues were processed for paraffin embedding , and sections for slides were cut at 4 μm thickness . For histopathologic evaluation , slides were stained with hematoxylin and eosin . For IHC , slides were microwaved in Antigen Unmasking Solution ( Vector Laboratories ) , endogenous peroxidase activity was blocked by incubation in Bloxall ( Vector Laboratories ) , and nonspecific protein binding was blocked by incubation in 10% goat serum . Slides were then incubated in rabbit anti-ZIKV NS2B antibody ( Genetex ) diluted at 1:100 . ZIKV NS2B protein is a cofactor of the NS2B-NS3 protease which cleaves the viral polyprotein and is thus present during viral replication . Therefore , detection of NS2B serves as a marker of replicating virus as opposed to incomplete , phagocytosed , or degraded virions . Slides were then incubated sequentially in ImmPRESS HRP anti-rabbit IgG ( Vector Laboratories ) , and NovaRED HRP Substrate ( Vector Laboratories ) . IHC slides were also counterstained with hematoxylin . For each slide , the anti-ZIKV NS2B antibody staining was controlled with a slide using nonspecific Rabbit IgG ( Vector Laboratories ) substituted for the anti-ZIKV NS2B antibody , and control tissues from known infected and uninfected mice were included for each batch . A board-certified veterinary pathologist , who was blinded to each slide’s experimental conditions , read and scored each slide for immunoreactivity . Mammary gland slides were examined for mastitis by a pathologist . Bright field imaging was performed with a Zeiss Axio Scan . Z1 microscope and the images were acquired using Zen 2 software ( Carl Zeiss ) . SMC were frozen on dry ice and sent to the Texas A&M Veterinary Medical Diagnostic Laboratory for transmission electron microscopy . To detect viral NS2B protein expression , Vero E6 cells were grown to 70% confluency on glass coverslips . Cells were either mock-infected or inoculated with ZIKV FSS13025 at a MOI of 0 . 001 or with SMC supernatant . The SMC was collected from the pup’s stomach on d3 after birth from AG129 dams that had been previously infected retro-orbitally with 1 x 102 FFU of ZIKV FSS13025 . The SMC was collected at day 3 post infection because this time point was the peak RNA viral burden in the SMC . At day 5 after SMC treatment , Vero cells were fixed in 4°C methanol and permeabilized with 0 . 1% Triton X-100 . Protein blocking was performed with 10% goat serum , followed by incubation with anti-ZIKV NS2B antibody ( Genetex ) at 1:400 dilution . Coverslips were incubated with Alexa Fluor 594 ( Invitrogen ) at 1:300 dilution and then inverted onto glass slides for mounting . Imaging was performed by confocal microscopy . All data were analyzed with Prism software , version 7 . 0 ( GraphPad Software ) and expressed as means ± SEM . For viral burden and focus forming assay data , Krustal-Wallis test was used to compare more than two groups . This test was performed only in ZIKV-infected samples . Mock was not considered in the analysis . p<0 . 05 was considered a significant difference . To begin evaluating whether ZIKV could infect the mammary gland and be transmitted to breastfed infants , 8-week-old female AG129 mice were infected with ZIKV strain FSS13025 . Viral burdens in several tissues were first assessed at 5 , 7 , 9 and 11 dpi via qRT-PCR . ZIKV RNA levels in the mammary glands were similar at the four time points ( Fig 1A ) . As expected , high levels of ZIKV RNA were present in the brain , spleen , and serum with no significant difference among the four time points . With the exception of serum , there was a slight reduction on 5 dpi compared with 11 dpi ( Fig 1B–1D ) , indicating ZIKV dissemination into tissues . To test for the presence of infectious virus in the mammary gland , we measured viral titers by FFA ( Fig 1E ) . High levels of infectious ZIKV were present in the mammary gland at 5 dpi with a slight reduction in the subsequent days analyzed , demonstrating that ZIKV establishes productive infection in the AG129 mouse mammary glands . To localize ZIKV replication within the mammary gland of AG129 mice , 8-week-old female AG129 mice were mock-infected or infected with ZIKV strain FSS13025 , followed by visualization of ZIKV infection via laser scanning confocal microscopy . After clearing with Visikol HISTO , 800–1000 μm thick portions of the mammary gland were imaged under laser scanning confocal microscopy . Immunofluoresence staining was performed to assess expression of ZIKV NS2B , a marker for viral replication [22] , and alpha smooth muscle actin ( αSMA ) , present in myoepithelial cells . At 5 dpi , strong expression of NS2B and αSMA was detected in the mammary gland of ZIKV-infected AG129 mice ( Fig 2A and 2B ) . The 3D images from this tissue ( S1 and S2 Figs ) and staining for ZIKV NS2B and DAPI ( Fig 2C ) showed similar results . Thus , ZIKV NS2B colocalizes with αSMA-expressing cells within the mammary glands of AG129 mice , suggesting myoepithelial cells as one of the cellular hosts of ZIKV in the mammary gland . To confirm ZIKV replication within the mammary gland of AG129 mice , tissues were fixed in Zinc formalin and then stained for expression of ZIKV NS2B at 5 , 7 , 9 and 11 dpi . No difference was observed among all times points , and we show 5 and 9 dpi as representative ( Fig 3 ) . NS2B expression was detected in cells morphologically consistent with mammary epithelial cells , myoepithelial cells , and interstitial macrophages ( Fig 3A ) . NS2B expression in cells in the stroma surrounding the teat canal on a nipple cross section ( Fig 3B ) and teat Langerhans cells was also observed ( Fig 3C ) . Additionally , histopathologic evaluation of these tissues revealed an absence of mastitis . Thus , ZIKV replicates locally in the mammary gland , and these enzyme IHC results in combination with z-projection images suggest that myoepithelial cells are major cellular hosts of ZIKV . Having established the presence of ZIKV RNA and infectious viral particles in the mammary gland , we proceeded to examine whether ZIKV was transmitted from infected mothers to neonates through breastfeeding . Neonatal heads , stomach tissues , SMC , and the rest of the bodies ( without skin to avoid contamination from the mother’s saliva ) were examined for the presence of ZIKV RNA by qRT-PCR . No ZIKV RNA was detected in the head and the rest of body in the neonates 1 to 7 days after birth ( Fig 4A and 4B ) . However , viral RNA was present in SMC and stomach tissues at almost all time-points tested from 1 to 7 days after birth ( Fig 4C and 4D ) . As ZIKV RNA does not necessarily indicate production of infectious virus , we next assessed for the presence of infectious ZIKV in SMC and stomach tissues via FFA . No infectious ZIKV particles were detected in SMC and stomach ( Fig 4E and 4F ) . Thus , breastfeeding does not appear to be a significant route of ZIKV transmission into neonates in this mouse model . To further assess the lack of infectious ZIKV in SMC , we inoculated SMC supernatant onto Vero cells . Infectivity of the SMC supernatant was assessed by immunofluorescence staining for ZIKV NS2B expression and CPE in the Vero cells , and plaque assay of the Vero culture supernatants . ZIKV NS2B expression and CPE were observed in the positive control cells infected with ZIKV . However , Vero cells inoculated with SMC supernatant did not show any NS2B protein expression or CPE ( Fig 5A and 5B ) , and plaque assay confirmed the absence of infectious virus in the culture supernatant of SMC supernatant-treated Vero cells ( Fig 5C ) . To assess whether ZIKV NS2B expression is observed in the breast milk were present in the SMC and might also infect the stomach tissue , 8-week-old female AG129 mice were infected with ZIKV strain FSS13025 , followed by sacrificing of pups on d5 after postpartum and examination for the expression of ZIKV NS2B on the pup SMC and stomach tissue by IHC . Of 10 sampled pup stomachs , ZIKV NS2B expression surrounded nuclear material in SMC from 3 pup stomachs ( Fig 5D ) . However , no ZIKV NS2B expression was detected in the full thickness of the gastric walls . These results suggest that replicating ZIKV may be passed in milk and is likely cell-associated; however , breast milk does not contain sufficient replication-competent ZIKV to initiate infection in cell culture and in IFN receptor-deficient mice . Finally , we determined whether there is infectious virus present in fresh breast milk by FFA . To increase the sensitivity of infection in these samples , we performed serial passages in Vero cells of fresh breast milk collected at 5 and 7 days postpartum . Only one fresh milk sample collected at d5 showed a low infectivity in the first passage . However , we observed an increase of infectious particles at the second and third passage in both times points ( Fig 5E ) . In this study , we were able to detect ZIKV RNA in pup stomach milk clots and maternal mammary glands , and within the latter , ZIKV NS2B antigen localized to cells morphologically consistent with glandular epithelial cells , myoepithelial cells , and macrophages . ZIKV-permissive cells were also identified in the teat stroma and epidermis . Further , low levels of replicating virus were detected in fresh milk and ZIKV NS2B expression was detected in SMC samples . These results provide a framework for investigating ZIKV entry into the milk and raise the additional question of whether normal nursing-associated ingestion of maternal epidermal cells and blood may also play roles in ZIKV transmission . We propose that infectious ZIKV may enter human breast milk but may be subsequently inactivated by endogenous or exogenous factors such as lipid , antimicrobial proteins , or gastric acid . Several studies have shown that the acidic pH and the digestive enzymes present in the stomach inactivate virus [23–25] , and combine with mucus to form a chemical barrier to infection . Because dams were infected on the day of parturition , milk in this experiment should not have contained any ZIKV-neutralizing antibody . Recently in a rhesus macaques model , ZIKV RNA was present in saliva , another potential route of mucosal exposure [3] , but no infectious virus was detected . Another study demonstrated that human breast milk inactivates ZIKV after prolonged storage [26] . Additionally , human breast milk has been reported to reduce the infectivity of HIV , HCV , and dengue virus . Thus , antiviral properties of breast milk may reduce BMT [21 , 27] . Human viruses with known clinically relevant risk of BMT are cytomegalovirus ( CMV ) [28] and HIV-1 [27 , 29–31] . Although mastitis is a risk factor for BMT of both viruses , most cases of BMT occur in the absence of mastitis . Further , most cases of CMV and HIV BMT involve a seroconverted mother rather than infection of a naïve mother in the nursing period . Infectious CMV has been isolated from up to 80% of infected breast milk samples , whereas infectious HIV has been extremely difficult to isolate from breast milk . DNA and RNA from other human viruses including herpesviruses , parvovirus , rubella virus , arboviral flaviviruses , and hepatitis viruses A , B and C have been detected in milk [32] . However , perhaps owing to low clinical relevance of BMT of these viruses , it is largely unknown whether detected nucleic acids were non-infectious viral genetic material or derived from neutralized virions . After over two decades of research , the pathogenesis of HIV BMT remains poorly understood . It is estimated that BMT causes approximately 40% of mother-to-child transmission case of HIV . However , isolation of infectious virus from breast milk is rarely successful . HIV RNA , and rarely infectious virus , have been isolated from whey and cellular fractions of milk [33] . In contrast to CMV , viral loads in cellular fractions of milk correlate to transmission whereas loads in cell-free fractions do not . These findings have suggested that an intracellular location shields HIV from immune defenses such as lactoferrin , tenascin-C , defensins , and mucin [34] . Meanwhile , factors such as antibodies and HIV-gag-specific cytotoxic T lymphocytes may reduce cell-associated virus loads . Our early findings with ZIKV showed nursing mouse pups were not infected following ingestion of milk from infected dams . Therefore , the data are not sufficient to conclude that ZIKV infection can be passed via breastfeeding , and support early data suggesting the same for humans [8] . High ZIKV susceptibility of AG129 mice , which globally lack type I and type II IFN receptors , is often cited as a pitfall for many virology studies . However , in the current state of ZIKV science , in which it is unknown whether BMT is a clinical reality and there are no animal models of ZIKV entry into milk , a highly susceptible dam represents an excellent starting point to begin mechanistic manipulations which may reduce entry of viral RNA into milk . Furthermore , neonates , which are also deficient in IFN receptors , are a highly sensitive detection system for arranging conditions that may enable BMT . Indeed , the absence of infection in neonates in this study provides an early suggestion that infectious ZIKV is not easily transmitted through breast milk or other maternal-neonatal contact . It should also be noted that in humans , the tonsil is one of the first potential entry sites for orally ingested ZIKV [3] , whereas mice do not have tonsils . Because ZIKV is already known to have devastating consequences on the developing brain and there are both benefits to and substitutes for breastfeeding [35] it is imperative to fully understand the mechanisms which enable or prevent BMT . The results of this study provide a mouse model for investigating entry of ZIKV RNA into breast milk , and the pups provide a sensitive system for testing modulations which might permit BMT .
Can Zika virus be transmitted from nursing mothers to their children via breast milk ? Only 4 years have passed since the Zika virus outbreak in Brazil , and much remains to be understood about the transmission and health consequences of Zika infection . To date , some case reports have detected Zika virus RNA in the breast milk of infected mothers , but the presence of a virus’ RNA does not mean that intact virus is present . Milk also contains many natural defense components against infection , so even intact virus carried in breast milk may not be infectious to a child . Here we used a mouse that is genetically engineered to be highly susceptible to Zika infection , and tested whether 1 ) we could find intact virus in mouse breast milk and 2 ) infection was passed from mother to pups . We found very low levels of intact Zika virus in mouse breast milk , and found none of the nursing pups to be infected . The model of Zika virus breast milk infection developed in this study establishes a system by which we may learn whether Zika RNA in human breast milk is truly infectious to children , and how Zika virus may enter the milk .
You are an expert at summarizing long articles. Proceed to summarize the following text: The Wolbachia strategy aims to manipulate mosquito populations to make them incapable of transmitting dengue viruses between people . To test its efficacy , this strategy requires field trials . Public consultation and engagement are recognized as critical to the future success of these programs , but questions remain regarding how to proceed . This paper reports on a case study where social research was used to design a community engagement framework for a new dengue control method , at a potential release site in central Vietnam . The approach described here , draws on an anthropological methodology and uses both qualitative and quantitative methods to design an engagement framework tailored to the concerns , expectations , and socio-political setting of a potential trial release site for Wolbachia-infected Aedes aegypti mosquitoes . The process , research activities , key findings and how these were responded to are described . Safety of the method to humans and the environment was the most common and significant concern , followed by efficacy and impact on local lives . Residents expected to be fully informed and engaged about the science , the project , its safety , the release and who would be responsible should something go wrong . They desired a level of engagement that included regular updates and authorization from government and at least one member of every household at the release site . Results demonstrate that social research can provide important and reliable insights into public concerns and expectations at a potential release site , as well as guidance on how these might be addressed . Findings support the argument that using research to develop more targeted , engagement frameworks can lead to more sensitive , thorough , culturally comprehensible and therefore ethical consultation processes . This approach has now been used successfully to seek public input and eventually support for releases Wolbachia-infected mosquitoes , in two different international settings - Australia and Vietnam . The Wolbachia strategy aims to ‘manipulate mosquito populations to make them incapable of transmitting dengue viruses between people’ ( www . eliminatedengue . com ) . Its potential emerged following the successful transference of the insect bacterium Wolbachia pipientis from the fruit fly Drosophila melanogaster into the Aedes aegypti mosquito [1] , [2] , [3] . Later studies showed that the bacterium spread effectively into wild populations , had a life-shortening effect on the mosquito , blocked the development of some dengue viruses and some strains had a life-shortening effect on the mosquito [4] , [5] . These properties would , in all likelihood , greatly reduce the mosquito's capacity to transmit the virus . To trial its effectiveness in real world conditions , required a series of field release through which Wolbachia-infected mosquitoes would be released into wild populations the aim being to replace these . The Wolbachia method is one of several strategies to emerge in recent year that use a range of new technologies to combat dengue fever . While some focus on genetic modification , others , like Wolbachia , use biological control [1] , [5] , [6] . However , these strategies are very different from their predecessors , notably source reduction and insecticide use , and are not without controversy . Moreover , many require open field releases to test their efficacy and potential uses . Significantly , these need to occur in the locations where dengue vectors are found , most commonly the homes , and places of work , education , worship and leisure of local residents at a release site . Most commentators recognize that the political and ethical complexities of community field trials are considerable and that public and government approval in conjunction with high quality science are of central importance . It is also widely acknowledged , that given the spread and increasing prevalence of dengue fever throughout the tropics , field trials will need to be undertaken in a variety of locales , regions and countries , both so called developed and developing . While public engagement is also recognized as critical to the use and future success of these strategies , many questions remain regarding how to proceed in ways that are ethical , and comprehensible to those being asked to trial these strategies in their homes and backyards . In 2008 an approach to engagement drawing on anthropological methodologies and insights was developed for the Wolbachia strategy . It was implemented in Cairns , Australia from 2008–2010 [6] and in January 2011 the first field release of Wolbachia-infected Ae . aegypti commenced . Drawing on anthropological methodologies and insights , this approach recognizes that different communities will have divergent expectations , knowledge , concerns , political structures and cultural sensibilities , that need to be understood and taken into account , if one is to engage sensitively , ethically and effectively [6] , [7] , [8] , [9] , [10] , [11] [12] . The most reliable way to do this , is to talk with residents at a potential release site about the new dengue control methods and ask what their concerns are , how they want to be engaged and what would constitute authorization [6] , [11] . From this research , an engagement framework is developed that is sensitive to local needs , expectations , knowledge and concerns . So , rather than simply adopting an engagement strategy that was developed elsewhere and implementing it in another setting , this approach uses social research to design an engagement framework and communication materials that are tailored specifically to potential release sites . In brief , it begins by undertaking systematic social research to: ( a ) document the socio-political context and identify the various publics and stakeholders at the potential release site , ( b ) determine how they want or expect to be engaged and the forms this should take , ( c ) explore what would constitute authorization , ( d ) identify any questions or concerns they might have about the Wolbachia strategy , ( e ) identify lay knowledge of the disease , its transmission , vectors , perceived risk , etc . and ( f ) develop responses to these . The results of this research are then used to design a community engagement framework tailored specifically to the sociopolitical setting , and the requirements and expectations of a given population [6] . This paper describes the use of this approach from June 2009 to September 2010 at the second potential Wolbachia release site - Tri Nguyen Island , in central Vietnam . It outlines the process , research activities , outcomes and key findings from the Vietnamese field site . It also highlights key public concerns and expectations about engagement and authorization and shows how these were used to develop a more targeted , culturally appropriate and comprehensible engagement framework and communication materials . Most significantly , the paper demonstrates the viability of this approach to community engagement for new dengue control strategies , in a ‘developing’ country context . It is hoped that by reporting on the methodology , process and results , that readers will be able to see the steps taken and assess the capacity of this approach to reflect and address local requirements and expectations , as well as its potential applicability to other programs . Dengue fever has a long history in Vietnam and continues to represent a major public health problem [13] . Disease transmission occurs throughout the year in the south of the country but is limited to the warmer months in the northern and highland areas . Two vectors are active in disease transmission , the Ae . albopictus and Ae . aegypti mosquitoes [12] [14] , [15] . Historically , dengue control in Vietnam has focused on source reduction , container management , insecticides and community mobilization – the later relying on household visits by collaborators and the management of water storage containers [15] . Since 1989 , community-based biological control initiatives using Mesocyclops spp . to control mosquito breeding in household water containers have also been introduced [15] , [16] , [17] , [18] . These have also included successful community mobilization around the management of water storage containers and the presence of Mesocyclops spp . Tri Nguyen Island ( TNI ) or Hon Mieu ( ‘Island Shrine’ ) , as it was known historically , is located to the southeast of the city of Nha Trang ( NT ) in Khanh Hoa province , central Vietnam ( Figure 1 ) . It was selected as a potential release site for the Wolbachia strategy for a number of reasons . These include its physical isolation , its proximity to the Pasteur Institute in Nha Trang , famous for its work on infectious diseases , and residents' previous involvement in mosquito ecology and vector studies . Since the late 1940s and during the war with France , people from other provinces such as Quang Nam , Quang Ngai , Binh Dinh , Phu Yen moved to TNI . Today the island is stratified into 3 hamlets each with its own leader , which together represents one sector of the Vinh Nguyen ward of Nha Trang city , in Khanh Hoa province . In 2009 the population of TNI was 3253 residents , living in 710 households spread across three hamlets , each of which had its own political leaders [19] . The social research activities described here were undertaken over 16 months ( June 2009—September 2010 ) and included six weeklong fieldtrips to Tri Nguyen Island . Research activities centered on two key groups: a ) Residents of Tri Nguyen island and b ) health providers , government officials and scientists with responsibilities at the local , regional and national levels ( hereafter , Leaders ) . It is widely established that qualitative research methods are the most appropriate for assessing the views of a population , in part because of their emphasis on context and their documentation of knowledge and attitudes in a given geopolitical setting . In this study , key or recurring themes from the qualitative research were explored further using quantitative measures ( a household survey and anonymous questionnaire ) and results were triangulated ( compared , challenged or confirmed ) across different methods: interviews , observations , questionnaires and a series of community meetings and workshops - styled on a focus group . Importantly , the findings presented here should not be seen as isolated research activities , but as a body of interconnected data developed over time using iterative processes and then contextualized , triangulated and crosschecked . An overview of the research activities undertaken at each phase , the issues they explored , how participants were recruited and the outputs they produced , is provided in Table 1 . In the following section we describe the methods used at each step in the research process and how the key results were used to design an engagement framework and communication materials tailored to this potential release site . We do so on the assumption that successful engagement leading to a release using new dengue control methods is still somewhat rare and that it is the process as much as the results that will be of interest to others looking to engage communities around new disease control strategies . The first step in the process was to immerse the two social science staff in the science of dengue and the Wolbachia strategy and to identify any information about the history and demographics of the potential release site . This included an extensive literature review on dengue fever , bio-control , GM food and organisms in Vietnam and internationally , and the development of a database ( Table 1 ) . In June 2009 a PowerPoint presentation was developed ( Table 2 ) . It used the same slides and followed the same narrative structure as the presentation used at the Australian field site , to which Vietnam specific information was then added . Graphics with small amounts of text were used to communicate key messages around the following themes: increasing prevalence of dengue ( local , national , international ) ; disease transmission and vectors; current control measures in Vietnam; the Wolbachia strategy; the Australian pilot release; a potential release on TNI . A discussion was then facilitated to identify any questions or concerns and seek guidance on how to engage , whom to engage , what would constitute authorization ( Table 2 ) . In July 2009 this presentation was used at the first of three leaders workshops , with thirty national , provincial , district and commune leaders , scientists and local health providers in attendance . Participants were chosen purposefully , because of their roles as leaders or officials and formally invited to attend . They included Ministry of Health leaders and scientists , members of the Khanh Hoa People's Committee and Khanh Hoa Health Department , and community and union leaders from TNI and NT . Project scientists and social scientists from Vietnam and Australia were present at the workshop . At the first Leaders Workshop ( Hanoi , July 2009 ) project employees were introduced to participants , and presentations delivered on the impact of dengue fever in Vietnam , the science behind the Wolbachia method , the potential release strategy in Vietnam and progress at the Australian release site ( scientific and engagement ) . The presentation was approximately 20 minutes long , after which a discussion was facilitated while the second social scientist made observations on body language; interactions between participants and audio recorded the entire event - presentation and discussion . Participants were asked if they had any questions , thoughts or concerns and what their expectations around the strategy , engagement and authorization might be . Input was also sought to identify key stakeholders as well as feedback on the presentation and project communication materials . An anonymous questionnaire was distributed at the end of the leaders workshop . It asked participants to identify any concerns or questions , evaluate how acceptable the Wolbachia strategy was , how they wished to be engaged and what would constitute authorization . This questionnaire provided baseline data for evaluating responses to the Wolbachia strategy through time , and was an important mechanism for tracking responses to the project among the leaders group and later , local residents . This process was also used at the Australian field site [6] . In early September 2009 , a senior entomologist working for the Wolbachia project , who was well known to the local community , introduced project staff to Tri Nguyen ( TNI ) residents . Limited information on the history and demographics of TNI was publically available so a purposive sample of 10 in-depth interviews on the history , socio-political structure , social demographics and dengue history of TNI was undertaken with local residents and leaders . Purposive sampling involves the deliberate selection of individuals because of the crucial information they can provide – in this case local leaders with a detailed knowledge of the history and socio-political make up of the TNI community . These interviews , alongside informal discussions with local health and mosquito control staff and results from the Leaders workshop , were used to develop a detailed stakeholder contact list , which was added to over time . It categorized individuals and groups according to: level of influence ( local , national , international ) ; local expectations around engagement; marginality; and accessibility . This helped to determine who was engaged and when . In addition , results from the interviews were also used to improve the PowerPoint presentation and communication materials to be used at future community meetings and workshops . In the next stage of the process , the results from these interviews were used to develop a Household Survey that examined the following: political structure ( leaders , groups , organizations ) ; social demographics of TNI ( name , age , gender , occupation , education level , religion , family structure ) ; knowledge of dengue , its vectors , control methods and perceptions of risk; and local health issues of concern to residents . The survey provided a brief introduction to the Wolbachia strategy and sought to identify early responses and advice on engagement and authorization for a release . The survey was piloted with 10 residents , reviewed and later administered to 100 households randomly selected from a list of 710 provided by local authorities - approximately 14% of all households . The second Leaders Workshop was held in the mainland city of Nha Trang , and attended by 33 participants representing local ( TNI ) and district leaders , government representatives , scientists , local health providers and mosquito control staff . An update on the progress of the science , the Australian risk assessment and the release was provided and further advice sought on stakeholders , forms of engagement , authorization and the presentation and communication materials . As noted above , a discussion was facilitated and any questions or concerns were noted . The event was also audio-recorded for later transcription and analysis and the anonymous questionnaire distributed . During the next phase of the project , 46 community meetings , attended by 661 local residents , were held in TNI during four , one-week trips in January ( T1 ) , March ( T2 ) , May ( T3 ) and July ( T4 ) 2010 ( Table 1 ) . The aim of these meetings was to gauge the range of views on the Wolbachia strategy , the science , potential release , engagement and authorization using the same focus group style format as the Leadership Workshops . Discussion was facilitated around the following themes: questions raised , concerns , acceptability , how and whom to engage and authorization . The meeting was audio-recorded and the anonymous questionnaire distributed at the end ( Table 1 ) . During the second visit ( March 2010 ) local residents who had contracted dengue attended the meeting and spoke of their experiences during the presentation . In addition , new results from the independent Australian Risk Assessment and new experiments showing Wolbachia was not transmitted to predators who ingested the infected mosquitoes were added to the presentation . During the third ( May 2010 ) and fourth visit ( July 2010 ) , results of Vietnamese experiments indicating that ingesting infected mosquitoes did not affect or lead to transmission of Wolbachia among local predatory species was included . By this time we also had more information about government approval processes ( following the final Leaders Workshop ) and the likely time frame for this , so this to was incorporated into presentation . Other than these additions , the presentation was the same at each visit . For the community meetings on TNI , a small number of participants were approached directly and sampled purposefully ( i . e . health staff , hamlet and local union leaders and members ) based on the stakeholder list we had begun developing . However , the majority of participants were sourced through flyers , posters and announcements over the community loudspeaker prior to each visit . As such the sample was broadly representative , with participants self-selecting to be involved . We aimed to reach at least one person from every TNI household ( Table 1 ) . During the second ( March 2010 ) and third ( May 2010 ) visits , 20 in-depth interviews were also undertaken with residents from TNI and NT ( aged 18–60 years ) who could not attend the meetings . We approached marginalized or harder-to-reach groups identified during the Leaders Workshops and early interviews ( n = 10 ) with local leaders . This included fishermen who were often away from the island , women with domestic and employment duties and minority religious or ethnic groups who it was thought might otherwise not have been engaged . These interviews began with the PowerPoint presentation and explored the same issues as the workshops and meetings . They were audio recorded for transcription purposes . The third and final Leaders Group Meeting was held in Nha Trang and attended by 33 local , district and national leaders , local health and mosquito control staff and scientists . Presentations on the results of both the social and scientific research were provided , and further advice sought on regulatory pathways and approval processes in Vietnam . The anonymous questionnaire was also distributed . Two social scientists and at least one senior entomologist attended every meeting or workshop . Prior to any research or engagement , an extensive and detailed list of questions and answers posed by the public at the Australian field site , was made available to Vietnamese project staff . It was posted to the project's website in June 2009 ( see http://www . eliminatedengue . com/faqs for the current version ) and later , on the Vietnamese language version and developed into flyers provided to participants . As the research progressed , it was clear that this extensive list covered almost every question posed by participants in the Vietnam research . When new questions or issues did arise , they were answered , if possible . If it was not possible to answer a question , it was recorded so that a response could be sought from appropriate staff and later provided back to the person asking the question and the community . This practice helped to ensure that information across the field sites , project staff and research activities - meetings , workshops , interviews etc . - was accurate and consistent . Results from the Household survey ( n = 100 ) indicated that residents were well versed on prevention activities and current control methods , i . e . covering water containers , insecticide use , bed nets etc . [20] . Although 65% of those surveyed correctly identified key domestic breeding sites , there was also a strong and recurring association between ‘dirty places’ , namely sewers , forested areas , and refuse and the mosquitoes thought to transmit dengue . Although 65% were able to identify the mosquito primarily responsible for dengue transmission in TNI , only 35% were able to explain the transmission cycle or describe symptoms – both of which were central to understanding the Wolbachia strategy ( for more details see Huong and McNaughton 2012 . The Household Survey ( n = 100 ) revealed that most residents ( 93% ) identified dengue fever as a dangerous disease within their community . The main reasons cited were that it can be fatal ( 83 . 9% ) and can spread very fast ( 40 . 9% ) . Residents looked first to local health workers ( 95% ) , followed by television ( 55% ) and local officials ( 41% ) as trusted sources of information on dengue and health . These and other results were used to develop a more targeted PowerPoint presentation on the Wolbachia strategy that focused on symptoms , the transmission cycle and the habitats of the vectors , three key gaps in local understandings . This presentation was used at 46 focus-group style meetings with 661 residents ( Table 1 ) . The most prominent and recurring issue for respondents across the residents' and leaders' meetings and interviews was the safety of the method for people , animals and the environment . Relatedly , participants wanted to know if it was safe to be ‘bitten’ by a Wolbachia-infected Ae . aegypti mosquito , if was safe to drink water with these mosquitoes , their larvae or pupae in it , and if this would lead to Wolbachia being transmitted into other organisms , especially people . For example , a member of the youth union asked “Is it a problem if we are bitten by Wolbachia-infected mosquitoes ? Can Wolbachia be transmitted into our body ? ” Some also expressed concerns that Wolbachia-infected mosquitoes might become susceptible to or able to transmit other diseases: “After releasing the Wolbachia-carrying mosquitoes , the dengue fever may be reduced , but how about other diseases; will it cause any other disease to come to our Island ? ” Responses to questions relating to the potential transmission of Wolbachia to humans , other organisms or the environment included but were not limited to the following: A discussion about the role of many project staff in blood feeding large numbers of these mosquitoes in the caged trials and laboratories ( including photos ) often ensued . Alongside safety , considerable discussion centered on why TNI had been chosen as a potential release site , if it would be the first to trial this strategy and who would be responsible if anything should go wrong . For example , “I heard many people who participated in your discussions ask each other why this method was not applied somewhere else but on Tri Nguyen Island . Is it safe if it is applied here ? ” ( Male , 25 years , member of the Youth union ) . Another resident expressed concerns about safety and responsibility as follows: For many participants , assurances were sought that Australia rather than Vietnam would be the first place to release these mosquitoes . In addition , residents wanted clear pathways of responsibility outlined so they knew whom to speak to should something go wrong . Several residents asked directly , “Which agency will be in responsibility in case the release strategy will cause additional impacts ? ” ( CM , T3 ) . Local government and health officials also wished to know who would be responsible in the event of any problems and sought greater clarity from each other and project staff and leaders , regarding their specific responsibilities during a pilot release . Clear lines of responsibility had been established and these were relayed to residents with responses like the following: Another common concern centered on the efficacy of the strategy , especially in the long term . One resident attending the group asked “…does it [Wolbachia] have any side effects after being introduced into mosquitoes ? It is a bacterium , so it must be harmful to some extent” . ( CM , T2 ) . Many participants were also concerned that the life shortening effect of Wolbachia would impact on the success of the strategy , “How can Wolbachia-infected mosquitoes help prevent the disease when they die early after being released ? ” ( CM , T1 ) . “I am concerned that it may be difficult for Wolbachia-infected mosquitoes to find another mosquito to copulate with , or that they may die before they can lay their eggs” ( CM , T2 ) . Many participants were interested in eliminating all mosquitoes or why current control methods were no longer as viable: “Why don't you try to kill all mosquitoes ? Why don't you spray chemicals to kill them all ? ” ( CM , T3 ) . The 2009 Household Survey ( n = 100 ) had indicated that while 86% found the Wolbachia strategy acceptable , the use of insecticides either inside ( 67% ) or outside ( 74% ) their homes was also viewed positively ( see Table 3 ) . Responses to questions relating to efficacy , focused in part on the role of the trials in determining the effectiveness of this strategy , and that results from the Australian releases would be reported back to the community during future engagement . They also included , but were not limited to , the following ( for more details http://www . eliminatedengue . com/faqs ) : The nature and scale of the pilot release were also prominent , recurring issues from the community meetings and interviews ( n = 20 ) . Respondents commonly sought a high level of detail regarding the release , its timing and scale . Questions focused on further details regarding how many mosquitoes would be released , if this would be in all or only some houses , and how long it would take for wild mosquitoes to be infected . There was a lot of discussion about what residents should do to assist the effectiveness of the strategy and what impact this might have on people's lives . For example: This question was answered as follows: During the Residents meetings ( n = 46 ) and interviews ( n = 20 ) assurances were often sought that the release would not negatively affect or inhibit local lives and livelihoods and that householders would be made aware of any activities they needed to undertake before or during a release . There was strong support for being advised and informed well in advance of a release “so that we are well prepared for it ? ” ( CM , T2 ) . In general , we responded to these questions as follows: The anonymous questionnaire , handed out at the end of each meeting included the question , “Do you have any concerns about the Wolbachia method ? ” which was used to track residents' perceptions of the project through time . As indicated in Figure 2 , the number of concerned participants declined significantly as the Residents' Meetings and interviews continued . During the final two visits to TNI in May and July 2010 , no participants objected to a release ( Figure 2 ) . Participants were asked at the Leaders workshops ( n = 3 ) , Residents' Meetings ( n = 46 ) and interviews ( n = 20 ) how they would like to be engaged about the Wolbachia strategy . There was a strong desire for public consultation across all groups , consistent support for in-community presentations and a strong preference for face-to-face interaction with the project team and senior health officials . There was much less support for the use of media , posters , brochures and leaflets . One of the most common requests related to the scale of the engagement . At the local level , participants consistently indicated that well before a release the project team should engage with every community member and provide ongoing information on the safety and benefits of the project well before a release . For example , “More people , all people should be invited . A small group of participants like this is not representative enough to make a decision . It is perfect if 100% of people agree” ( CM , T3 ) . Others suggested that , at minimum , one person from each household should be engaged . For example , “One person from every household should be invited . The main income earner in every household should be invited so that they can remember what they have heard and tell others . If you invite those who are too old , they may not have a good memory to tell others about what they have heard” ( CM , T3 ) . Participants were also asked what would be the best format to engage people on TNI about the strategy and in the lead up to a release if regulatory approval was given . There was an expectation of ongoing consultation about the strategy among residents , leaders and health staff , where updates on the science , safety , risk assessment , regulatory approval , pilot release strategy , results from the Australian release and a well-defined structure around roles and responsibilities would be provided . Some were also concerned that without this , people might forget what they had learned about the strategy and how to respond to a release . Community leaders and health professionals suggested that residents would come to them for information and guidance , especially if things did not go to plan . As such they sought to have clear pathways on any future roles and responsibilities they might have negotiated , outlined and communicated to residents well before a release . As well as calling for regular updates , participants consistently identified the importance of a large meeting attended by at least one representative from each household as well as local and provincial leaders – essentially a forum where people could raise their ideas , discuss benefits and concerns and make a collective decision ( Table 4 ) . There was also a strong preference for voting at such a forum , as one resident expressed it “Voting can be used . Those who agree will raise their hand . If the majority raises hands that means it is supported” ( CM , T1 ) ( Table 5 ) . As such a large public meeting held in the community or a vote was identified as a mechanism through which the project and the release would gain final and collective approval from the TNI community , alongside support of government officials ( regulators , Ministry of Health and scientists ) ( Table 5 ) . The anonymous questionnaire also asked whether Resident's would support a pilot release if ( a ) the Ministry of Health undertook a risk assessment and approval process , and ( b ) scientific data from the Australian release site proved to be positive . During the first phase of social research and engagement in January 2010 , 80 . 2% were in favor of the pilot release . By the final phase in July 2010 , this had risen to 99 . 4% ( Figure 3 ) . Of course , participants can and do change their minds and they could react differently when a release happens , and this is a limitation of this study . However , results from the Australian research did allow us to predict quite successfully how people would react and there was no last minute call to stop the release in Australia . Although a release has not yet occurred in Vietnam , the most recent engagement with TNI residents ( 2013 ) - where one person from every household was interviewed - 99% of householders were still in favor of the release , only a few months shy of its eventuality ( data not shown ) . The approach described here produced a number of critical insights that helped determine the nature , scale , style and form of an engagement framework tailored specifically to the needs and wishes of officials and residents and the potential release site in Vietnam . It used systematic social research and consultation to ( a ) identify , inform and involve the public; ( b ) listen to their responses , questions and concerns; ( c ) examine the deeper cultural assumptions that underwrite these responses , including lay knowledge of dengue; ( d ) explore ways of responding to these issues i . e . scientifically , through education , the media , schools programs or new forms of participation; and ( e ) explore and enact suggestions regarding future engagement , participation , communication and authorization . Through this process we found that residents at the potential release site in Vietnam expected to be fully informed and fully engaged about the science , the project , its safety , risk assessments , the nature of the release and who would be responsible should something go wrong . Along with key health and government officials and representatives they provided advice on how best to engage their community and wanted the opportunity to meet with and ask questions of scientists involved in these programs and to have their concerns taken seriously and answered respectfully . This approach thus afforded the development of a more culturally appropriate and comprehensible engagement framework and communication materials that empowered those being asked to assess , critique and support a field trial or release . It has now been implemented at three socially and politically diverse and complex field sites ( seven in Australia , one in Vietnam ) in two countries , demonstrating its capacity to reflect local requirements and its potential for use in other programs and other regions .
In recent years , a number of new strategies using novel technologies for the control of dengue fever control have emerged . These strategies are notably different from their predecessors and not without controversy . Many also require open release field trials to test their efficacy . Public consultation and engagement are recognized as critical to the future success of these programs , but questions remain regarding how to proceed . In this paper we describe an approach to public engagement that uses social research to design an engagement framework and communication materials tailored to the concerns , expectations , and socio-political setting of potential trial release sites . This approach was developed and implemented in Australia ( 2008–2010 ) where the first publicly supported field trials occurred January 2011 . We report here on the implementation of this approach in Vietnam ( 2009–2010 ) where the second release will occur in 2014 . This paper describes the process , research activities , outcomes and key findings from the Vietnamese field site . It highlights key public concerns and expectations about engagement and authorization and shows how these were used to develop a more targeted , culturally appropriate and comprehensible engagement framework and communication materials . The paper demonstrates the viability of this approach to community engagement for new dengue control strategies , in a ‘developing’ country context .
You are an expert at summarizing long articles. Proceed to summarize the following text: Entry of human immunodeficiency virus type 1 ( HIV-1 ) commences with binding of the envelope glycoprotein ( Env ) to the receptor CD4 , and one of two coreceptors , CXCR4 or CCR5 . Env-mediated signaling through coreceptor results in Gαq-mediated Rac activation and actin cytoskeleton rearrangements necessary for fusion . Guanine nucleotide exchange factors ( GEFs ) activate Rac and regulate its downstream protein effectors . In this study we show that Env-induced Rac activation is mediated by the Rac GEF Tiam-1 , which associates with the adaptor protein IRSp53 to link Rac to the Wave2 complex . Rac and the tyrosine kinase Abl then activate the Wave2 complex and promote Arp2/3-dependent actin polymerization . Env-mediated cell-cell fusion , virus-cell fusion and HIV-1 infection are dependent on Tiam-1 , Abl , IRSp53 , Wave2 , and Arp3 as shown by attenuation of fusion and infection in cells expressing siRNA targeted to these signaling components . HIV-1 Env-dependent cell-cell fusion , virus-cell fusion and infection were also inhibited by Abl kinase inhibitors , imatinib , nilotinib , and dasatinib . Treatment of cells with Abl kinase inhibitors did not affect cell viability or surface expression of CD4 and CCR5 . Similar results with inhibitors and siRNAs were obtained when Env-dependent cell-cell fusion , virus-cell fusion or infection was measured , and when cell lines or primary cells were the target . Using membrane curving agents and fluorescence microscopy , we showed that inhibition of Abl kinase activity arrests fusion at the hemifusion ( lipid mixing ) step , suggesting a role for Abl-mediated actin remodeling in pore formation and expansion . These results suggest a potential utility of Abl kinase inhibitors to treat HIV-1 infected patients . HIV-1 enters cells in a pH-independent manner by fusion at the plasma membrane or from within endosomes [1]–[3] . HIV-1 entry requires multiple conformational changes in the HIV-1 glycoprotein , and rearrangement of the actin cytoskeleton . These events are triggered by binding of the viral envelope ( Env ) surface subunit gp120 to the primary receptor CD4 and one of two chemokine coreceptors , CCR5 or CXCR4 [1] , [4] . This interaction activates signaling events in the cell , similar to those initiated by natural ligands , such as Ca2+ mobilization , activation of RhoGTPases , and phosphorylation of tyrosine kinases , pyk2 , Zap70 and p56lck [4]–[6] . Rho family GTPases , which include the Cdc42 , Rac , and Rho subfamilies , are responsible for regulating signaling from membrane receptors to the actin cytoskeleton . The Rho sub-family stimulates myosin based contractility , and drives the formation of stress fibers and focal adhesions . The Rac sub-family stimulates lamellipodia and membrane ruffles , and the Cdc42 subfamily stimulates the formation of filopodia [7]–[9] . We showed that HIV-1 Env binding to target cells induces activation of Rac , stimulates membrane ruffles and lamellipodia , and fusion is inhibited by dominant negative Rac [4] , [10] . Furthermore , HIV-1 Env-induced Rac activation depends on activation of Gαq , phospholipase C ( PLC ) , Ca2+ mobilization , protein kinase C ( PKC ) , pyk2 and the GTPase Ras [5] . In the current study we identified the fusion-specific effectors of Rac required for actin cytoskeleton rearrangements that mediate membrane fusion and entry . Guanine nucleotide exchange factors ( GEFs ) activate GTPases , facilitating the GDP to GTP switch , and regulate their downstream effects by participating in scaffolding protein complexes , thereby linking GTPase activity to specific effectors [7]–[9] . HIV-1 Env-induced Rac activation is mediated by a specific Rac GEF , either Tiam-1 or Trio [10] , [11] . There are multiple effectors of Rac , including serine/threonine kinases , lipid kinases , actin-binding proteins , and adaptor/scaffold molecules [7] , [12] . PAK is a downstream effector of Rac and Cdc42 that promotes stabilization of actin networks . Another downstream effector of Rac that nucleates actin polymerization is the Arp2/3 complex . The Arp2/3 complex is activated by the Wave2 complex through IRSp53 , an adaptor protein that binds Rac and Wave2 [7] . The Wave2 complex includes Rac-associated protein 1 , Nck-associated protein , Abl-interacting protein 2 , and heat shock protein C300 . Wave2 also associates with Abl , and Abl-mediated phosphorylation of Wave2 promotes its activation [13] , [14] . In addition to determining which Rac effectors are critical for membrane fusion , we studied the steps in the membrane fusion process affected by these signaling molecules . These data demonstrate that the Wave2 signaling complex and Abl are required for Env-mediated membrane fusion , entry , and infection and that Abl kinase inhibitors arrest the fusion process at hemifusion . To determine whether Abl , Trio , or Tiam-1 were required for HIV-1 Env-mediated cell-cell fusion , expression of these proteins was down regulated by RNA interference ( RNAi ) in U87 . CD4 . CCR5 cells . Cells expressing siRNA were then mixed with BSC40 cells expressing different Env subtypes and Env-dependent cell-cell fusion was measured . Transfection of target cells with siRNA to Tiam-1 and Abl decreased levels of Env-mediated cell-cell fusion by an average of 79±5% and 74±5% respectively for both HIV-1 R5 and dual-tropic Env-subtypes ( Figure 1A , left ) . There was no significant fusion observed with CCR5 expressing target cells and X4 Env expressing cells with or without siRNA , as expected . The decrease in the levels of fusion correlated well with the decreased steady-state level of Tiam-1 , and Abl as detected by immunoblot ( Figure 1C ) . A siRNA directed against Trio had no effect on Env-induced cell-cell fusion despite a 70% reduction in expression of the Trio protein ( Figure 1A and C ) . To determine whether Tiam-1 and Abl are acting exclusively upstream of Rac , a constitutively active Rac mutant , RacV12 was expressed in siRNA transfected cells . Expression of RacV12 in cells expressing siRNA to Tiam-1 reversed the effects of this siRNA on fusion , suggesting that Tiam-1 is functioning upstream of Rac . In contrast , levels of fusion in cells expressing RacV12 and siRNA to Abl were only 53±1% that of cells expressing RacV12 and control siRNA , suggesting a role for Abl upstream and downstream of Rac ( Figure 1A , right ) . Tiam-1 binds to the Rac and Cdc42 effector IRSp53 , enhancing IRSp53 binding to Rac and activation of the Wave2 scaffolding complex [15] . To determine the role of these Rac effectors in Env-mediated membrane fusion , their expression was down regulated by RNAi in U87 . CD4 . CCR5 cells . The siRNA expressing cells were mixed with Env-expressing cells and cell-cell fusion was measured . Expression of siRNA to IRSp53 , Wave2 , and Arp3 decreased fusion by 74±5% 77±4% and 78±4% , respectively . The decrease in fusion with these siRNAs was not overcome by expression of RacV12 , suggesting that these proteins are required downstream of Rac ( Figure 1B ) . The decrease in levels of fusion correlated with the decrease in protein expression in cells expressing these siRNAs , as seen by immunoblot ( Figure 1C ) , and each siRNA was specific for its target protein ( Figure S1A ) . Treatment of cells stably expressing siRNA resistant Arp3 , with Arp3 targeted siRNA had no effect on Env-mediated cell-cell fusion ( Figure 1D , E ) . In contrast , with untransfected cells , and cells stably expressing siRNA resistant Arp3 , treatment with siRNA to Rac decreased fusion by 75±5% and 76±3% respectively ( Figure 1D ) . These results show that the effects of RNAi on fusion were specific to inhibition of their target molecules . To demonstrate the role of Tiam-1 , Abl , Rac , IRSp53 , Wave2 and Arp3 in virus-cell fusion , their expression was down regulated by RNAi in TZM-BL cells , a derivative of HeLa cells that express CD4 , CCR5 , and CXCR4 , and these cells were then used in a Vpr-Blam assay [16] , [17] . In this assay siRNA expressing cells were mixed for 90 min with HIV-1 strains with cores carrying a β-lactamase ( BlaM ) -Vpr chimera , and pseudotyped with Env from ADA ( R5 ) , YU2 ( R5 ) or HXB2 ( X4 ) , and fusion was quantified by measuring the cytosolic activity of viral core-associated BlaM [18] . Expression of siRNA to Tiam-1 , Abl , Rac , IRSp53 , Wave2 , and Arp3 decreased virus-cell fusion by an average of 80±4% , 83±1% , 76±4% , 82±6% , 77±3% and 82±6% , respectively , for HIV-1 R5 and X4 Env subtypes ( Figure 1F ) . These results show that activation of the Wave2 signaling complex is required for Env-dependent cell-cell fusion and virus-cell fusion . Since treatment of cells with Abl targeted siRNA led to a decrease in Env-dependent cell-cell fusion and virus-cell fusion we wanted to determine whether treatment of target cells with commercially available Abl kinase inhibitors , imatinib ( IMB ) , nilotinib ( NIL ) , and dasatinib ( DAS ) , block fusion . IMB is a relatively specific inhibitor of Bcr-Abl , Abl , Arg , and class III receptor tyrosine kinases . NIL is an Abl kinase inhibitor 20–50 fold more potent than IMB at inhibiting Abl . DAS , originally designed as a Src family kinase inhibitor , antagonizes Abl , ephrin and platelet-derived growth factor receptor kinases , and kit . DAS is 300 fold more potent than IMB at inhibiting Abl [19] , [20] . To determine the concentrations of these Abl kinase inhibitors that inhibit Abl kinase activity and Env-mediated cell-cell fusion , without non-specific effects , Abl kinase activity , trypan blue analysis , vaccinia virus infection , and T7 polymerase activity were measured in addition to Env-dependent cell-cell fusion ( Figure S1B , S2 , and data not shown ) . Treatment of U87 . CD4 . CCR5 cells with 10 uM IMB , 500 nM NIL , and 300 nM DAS for 1 h prior to and during 3 h incubation with Env-expressing cells decreased Env-mediated cell-cell fusion by an average of 95±2% , 92±5% , and 92±6% , respectively , and Abl kinase activity by 85–87% ( Figure 2A and S1B ) . The CCR5 inhibitor TAK-779 , which completely blocks Env-mediated cell-cell fusion and infection of CCR5 expressing cells , was included as a control , and it decreased Env-dependent cell-cell fusion by 99±1% and Env-mediated Abl kinase activation by 98% ( Figure 2A and S1B ) . Similar results were observed with U87 . CD4 . CXCR4 cells treated with CXCR4 inhibitor AMD3100 and Abl kinase inhibitors and incubated with cells expressing HIV-1 X4 or dual-tropic Env subtypes ( Figure S3A ) . There was no decrease in T7 polymerase activity , or localization of CD4 and CCR5 on the cell surface ( Figure S4 and data not shown ) . Expression of RacV12 in U87 . CD4 . CCR5 cells treated with IMB , NIL and DAS increased the level of fusion by an average of 3 . 5-fold ( * , P<0 . 05 ) compared to treated cells without RacV12 , suggesting a role of Abl kinase activity upstream of Rac ( Figure 2B ) . To determine the effect of these Abl kinase inhibitors on Env-induced Rac activation , U87 . CD4 . CCR5 cells were treated with inhibitors for 1 h prior to mixing with BSC40 cells expressing no HIV-1 Env , HIV-1 X4 Env , or HIV-1 R5 Env for 30 minutes in the presence of inhibitor . The mismatched X4 Env , that does not induce Rac activation in CCR5 expressing cells , and the CCR5 inhibitor TAK-779 , which completely blocks Env-mediated Rac activation in CCR5 expressing cells , were included as controls [5] . Env-induced Rac activation was abolished in cells treated with TAK-779 , and all three of the Abl kinase inhibitors ( Figure 2C ) . To validate these effects in a relevant HIV-1 target cell , peripheral blood lymphocytes ( PBLs ) , which express CD4 , CCR5 and CXCR4 , were used as the target cell population in an Env-dependent cell-cell fusion assay . Treatment of PBLs with IMB , NIL , and DAS decreased fusion by an average of 92±1% , 92±3% , and 99 . 5±1% , respectively , for HIV-1 R5 , dual-tropic and X4 Env subtypes ( Figure 2D ) . The CCR5 inhibitor TAK-779 , as expected , completely blocked fusion mediated by R5 Env-expressing cells , inhibited fusion mediated by dual-tropic Env by 56±2% , and had no effect on fusion mediated by X4 Env ( Figure 2D ) . A long term infection assay was also performed where PBLs were infected with 150 ng of the X4 HIVHXB2 virus after 1 h preincubation with no inhibitor , DMSO , 10 µM IMB , 250 nM NIL , or 75 nM DAS . After 3 h , virus and inhibitors were washed off , inhibitors were added back and the plate was incubated at 37° for 21 days with addition of the inhibitors every 24 h . After 21 days the samples were assayed for cell viability and p24 antigen content . Treatment with IMB , NIL , and DAS decreased cell viability of HIVHXB2 infected cells by 17±4% , 8±5% , and 8±3% respectively and decreased infection by 52% , 51% and 94% compared to DMSO treated cells ( Figure S5 ) . To validate the specificity of these effects , we performed an Env-dependent cell-cell fusion assay with cells stably expressing two different drug resistant Bcr-Abl mutants ( Y253F and T315I ) , or expressing wild type ( WT ) Bcr-Abl [21] . Expression of the drug resistant Bcr-Abl mutants but not WT Bcr-Abl resulted in recovery of fusion ( Figure 2E ) , demonstrating that the effects of these inhibitors on Env-dependent cell-cell fusion are specific to inhibition of Abl . To confirm these results using virus particles with relevant levels of virus-associated glycoprotein , we used a virus-dependent cell-cell fusion assay based on the ability of virus particles to bridge two cells and allow transfer of cytoplasmic contents , and we also used the Vpr-BlaM assay described above [4] , [10] . For the virus-dependent cell-cell fusion assay we used two populations of U87 . CD4 . CCR5 cells , one expressing the T7 polymerase and the other expressing the β-galactosidase ( β-gal ) gene under the T7 promoter . Both populations were incubated with inhibitors for 1 h prior to 3 h incubation with R5 virus HIVYU2 . In this assay , controls included untreated and inhibitor treated cells that were not incubated with virus , the CCR5 inhibitor TAK-779 , and T-20 which blocks entry by inhibiting the conformational change in HIV-1 gp41 required for fusion [17] . R5 Virus-dependent cell-cell fusion was reduced by an average of 94±3% in cells treated with IMB , DAS , and NIL compared to cells treated with DMSO alone , and treatment with TAK-779 and T-20 completely inhibited fusion ( Figure 2F ) . Treatment of U87 . CD4 . CXCR4 cells incubated with the X4 virus HIVHXB2 with AMD3100 IMB , NIL , and DAS decreased virus-dependent cell-cell fusion by 88±7% , 98 . 6±1% , 87±5% , and 96±17% , respectively ( Figure S3B ) . For the Vpr-BlaM assay , TZM-BL cells were treated with 1 µM AMD3100 , 1 µM TAK-779 , 10 µM IMB , 500 nM NIL and 150 nM DAS for 1 hr prior to and during the 90 min incubation with HIV-1 Vpr-BlaM viruses expressing R5 and X4-tropic Env . AMD3100 treatment decreased X4-Vpr-BlaM activity by 84±1% , but had no effect on R5-Vpr-BlaM activity . TAK-779 treatment decreased R5-Vpr-BlaM activity by an average of 89±2% , but had no effect on X4-Vpr-BlaM activity , as expected . However , treatment of TZM-BL cells with IMB , NIL , and DAS decreased virus-cell fusion by an average of 81±4% , 89±5% , and 90±1% , respectively , for both HIV-1 R5 and X4 Env subtypes ( Figure 2G ) . These results together with the results of the Env-dependent and virus-cell fusion assay demonstrate that Abl kinase is required for HIV-1 entry mediated by CXCR4 and CCR5 . To determine whether the Wave2 signaling complex and Abl are required exclusively for HIV-1 entry , or virus-induced fusion and infection in general , we examined infection with HIV-1 versus A-MLV Env ( A-MLV-ENV-HIV-1 ) or VSV-G pseudotyped HIV-1 ( VSV-G-HIV-1 ) using the TZM-BL assay . HIV-1 Env induces pH independent virus-cell fusion to facilitate entry , whereas viruses pseudotyped with VSV-G or A-MLV Env induce pH-dependent clathrin mediated endocytosis or caveolin-mediated endocytosis , respectively [22]–[25] . TZM-BL cells , a derivative of HeLa cells that express CD4 , CCR5 , CXCR4 , and luciferase ( luc ) under the control of the HIV-1 LTR , were pretreated with the 10 µM IMB , 500 nM NIL and 150 nM DAS for 1 h prior to incubation with virus for 3 h , and a subsequent 24 h incubation with inhibitor only [16] , [17] . The CCR5 inhibitor TAK-779 , the CXCR4 inhibitor AMD3100 , and ammonium chloride ( NH4Cl ) which inhibits endosomal acidification required for VSV-G mediated entry , were included as controls [22] , [23] , [26] . The top two panels of Figure 3A shows that treatment with IMB , NIL , and DAS decreased infection with R5 HIVYU2 virus and X4 HIVHXB2 virus by an average of 91±7% , 88±4% , and 91±5% , respectively , comparable to the reductions observed with Env-dependent cell-cell fusion , virus-dependent cell-cell fusion and virus-cell fusion ( Figure 2 ) . The Abl kinase inhibitors had no effect on infection of TZM-BL cells with A-MLV-ENV-HIV-1 or VSV-G-HIV-1 , but treatment of cells with NH4Cl blocked infection with VSV-G-HIV-1 as expected ( Figure 3A , bottom two panels ) . These data show that Abl-kinase inhibitors were able to block HIV-1 Env-mediated fusion specifically and had no effect on infection via pH-dependent clathrin-mediated or caveolin-mediated endocytosis , and post-entry steps were not affected by these inhibitors . To test the effect of Wave2 complex targeted siRNAs on infection , TZM-BL cells were transfected with 200 nM control siRNA or siRNA directed towards Tiam-1 , Trio , Abl , IRSp53 , Wave2 and Arp3 . These cells were incubated with virus for 3 h , and media alone for 24 h . The decreased levels of HIV-1YU2 and HIV-1HXB2 infection of TZM-BL cells expressing siRNA targeted to Tiam-1 , Abl , IRSp53 , Wave2 , and Arp3 were comparable to levels of Env-mediated cell fusion with U87 . CD4 . CCR5 cells expressing these siRNAs , whereas siRNA to Trio had no effect ( Figure 3B , top two panels ) . Steady state levels of target proteins in cells expressing targeted siRNAs were decreased to similar levels as in U87 cells ( Figure 1C and data not shown ) . Infection of TZM-BL cells with A-MLV-ENV-HIV-1 or VSV-G-HIV-1 was not affected by expression of the targeted siRNAs , suggesting that Tiam-1 , Abl , IRSp53 , Wave2 , and Arp3 are required for HIV-1 Env-mediated entry and are not necessary for post-fusion steps in the virus life cycle ( Figure 3B , bottom 2 panels ) . HIV-1 Env-induced fusion , and release of the viral capsid into the cytosol is a multistep process . First , gp120 binds to CD4 inducing conformational changes in gp120 , and actin cytoskeletal rearrangements in the target membrane that bring the coreceptor CCR5 or CXCR4 into close proximity with CD4 . Next , coreceptor binding to gp120 triggers conformational changes in gp41 to produce a prebundle conformation that inserts into the target cell membrane , allowing lipid mixing or hemifusion , and then pore formation . Additional conformational changes induce formation of the gp41 6-helix-bundle which prevents pore closure and facilitates pore enlargement and full fusion [2] , [27] , [28] . To determine which step ( s ) in the membrane fusion process are blocked by the Abl kinase inhibitors , we examined the effect on infection of membrane curving agents . Oleic acid ( OLA ) , chlorpromazine ( CPZ ) , and trifluoperazine ( TFP ) are lipid analogs that insert into the inner leaflet of the cell membrane . OLA induces negative curvature in the membrane that promotes formation of a hemifusion intermediate ( i . e . lipid mixing ) , but cannot induce pore formation if there is a block at hemifusion . CPZ and TFP are membrane-permeable weak bases that partition into inner leaflets of cell membranes , induce positive curvature , and relieve a block at hemifusion [29]–[32] . To determine the effect of inhibitors and lipid analogs on HIV-1 infection , TZM-BL cells were treated with 1 µM AMD3100 , 1 µM TAK-779 , 10 µM IMB , 500 nM NIL , and 150 nM DAS for 1 h , prior to and during 1 h incubation with no virus , HIVΔENV , R5 HIVYU2 , X4 HIVHXB2 , A-MLV-ENV-HIV-1 , or VSV-G-HIV-1 . After 1 h , cells were treated with CPZ or TFP for 1 min or OLA for 5 min , followed by 2 h incubation with inhibitor and virus , and subsequent 24 h incubation with inhibitor only . Addition of CPZ and TFP to cells treated with Abl kinase inhibitors and infected with HIVYU2 or HIVHXB2 resulted in an 8 fold increase in infection compared to inhibitor treated cells infected in the absence of lipid analogs ( Figure 4A ) , The exogenous cone shaped lipid OLA , which induces negative curvature of the membrane resulting in lipid mixing , had no affect on infection ( Figure 4A ) . TAK-779 mediated inhibition of HIVYU2 infection and AMD3100 mediated inhibition of HIVHXB2 infection was not affected by these lipid analogs . No increase in luc activity was observed with lipid analog treatment of cells infected with HIVΔENV versus no virus , indicating that Env is required to observe an increase in infection ( Figure S6A ) . Treatment of A-MLV-ENV-HIV-1 and VSV-G-HIV-1 infected cells with CPZ and TFP decreased overall infection by 2 fold and had no effect on cells treated with Abl kinase inhibitors , indicating that the increase in HIV-1 infection observed with Abl kinase inhibitor treated cells was specific ( Figure 4A , lower panels ) . CPZ also partially reversed the inhibitory effects of nilotinib as measured by the Vpr-BlaM assay ( Figure S6B ) . Similar increases in virus-dependent cell-cell fusion were observed when U87 . CD4 . CCR5 cells were treated with inhibitors and lipid analogs and HIVYU2 mediated fusion was measured after 3 h ( Figure S6C ) . Cells were also incubated with the lipid analogs in the absence of HIVYU2 to account for the effects of these agents on the cells and on T7 polymerase activity . Addition of OLA did not increase fusion in cells treated with any of the inhibitors ( Figure S6B ) . To confirm the results obtained with the Abl kinase inhibitors we incubated TZM-BL cells transfected with Tiam-1 , Abl , Rac , IRSp53 , Wave2 , and Arp3 targeted siRNA , for 1 h with no virus , HIVΔENV , R5 HIVYU2 , or X4 HIVHXB2 . After 1 h cells were treated with CPZ for 1 min or OLA for 5 min , followed by 2 h incubation with virus , and subsequent 24 h incubation with media alone . As with the Abl kinase inhibitors , treatment of siRNA transfected cells with CPZ increased infection by an average 8 . 4 fold compared to untreated cells , and OLA had no effect ( Figure 4B ) . These results suggest that inhibition of Tiam-1 , Abl , Rac , IRSp53 , Wave2 or Arp3 arrests fusion at hemifusion , preventing pore formation , pore enlargement and content mixing . To confirm that Abl kinase inhibitors cause arrest at hemifusion , we used a modification of a fusion assay described previously [33] . CHO-K1 cells that lack expression of the lipid ganglioside GM1 , were engineered to express GFP and the HIV-1ADA ( R5 ) Env protein . U87 . CD4 . CCR5 cells were used as the target cell , and lipid mixing was detected when GM1 , detected by a TRITC-conjugated form of cholera toxin β-subunit ( CTX ) , was transferred from the target cell to CHO-K1-GFP cells . Complete fusion is detected when cells express GM1 , GFP , and are multinucleated . Quantification was performed for three independent experiments and the percentage of hemifused GFP+ , GM1+ cells with single nuclei and the percentage of multinucleated fully fused cells was enumerated for 68 cells from each condition ( Figure 5 , S7 , and Table S1 ) There were 83 . 1±10 . 9% hemifused cells with IMB-treated cells mixed with HIVADA-expressing CHO-K1 cells ( Figure 5 ) , compared to DMSO treated cells with 22 . 3±4 . 9% hemifused cells and 75 . 5±6 . 2% fully fused cells . With no HIV-1 Env or with the addition of TAK-779 there was little or no hemifusion or full fusion ( Figure 5 ) . To demonstrate the effects of the lipid analog CPZ on HIV-1 Env mediated cell-cell fusion and to observe the effect of CPZ and the Abl kinase inhibitors on A-MLV Env or VSV-G induced cell-cell fusion we treated U87 . CD4 . CCR5 cells with DMSO , TAK-779 , or IMB for 1 hr prior to incubation with CHO-K1 cells expressing no Env , HIVADA , A-MLV Env or VSV-G for 1 hr . After 1 h cells were treated with CPZ for 1 min and OLA for 5 min , then washed and incubated with inhibitor for an additional 2 h prior to fixation and GM1 staining . Incubation of IMB treated cells with HIVADA and CPZ promoted the transition from hemifusion to full fusion as expected ( Figure S8 ) . Fusion of A-MLV Env and VSV-G Env expressing cells with U87 . CD4 . CCR5 cells was unaffected by treatment with IMB or CPZ ( Figure S9 ) and all Env-mediated fusion was unaffected by OLA treatment ( data not shown ) . These results confirm that Abl kinase activity is required at a post-hemifusion step for HIV-1 Env mediated fusion and entry . Dynamic regulation of the actin cytoskeleton is required for fusion of biological membranes . Multiple reports have demonstrated that actin remodeling is required for HIV-1 mediated fusion and entry [4] , [5] , [10] , [11] , [34]–[36] . Some studies showed that treatment of target cells expressing physiologically relevant levels of receptor and coreceptor with the actin filament capping drug cytochalasin D prevented the formation of the gp120-CD4-coreceptor complex [35] , [37] , [38] . Another more recent study , demonstrated a role for CD4 and coreceptor-mediated filamin-A interactions in receptor clustering that is dependent on RhoA and ROCK mediated phosphorylation of ADF/cofilin [34] . Previous work from our lab with the actin filament stabilizing drug jasplakinolide and the actin monomer sequestering drug latrunculin A ( LA ) suggested a role for actin remodeling at a post binding step in fusion [4] . To further substantiate the role of actin polymerization in HIV-1 entry , we treated cells with 1 µM LA and 5 µM latrunculin B ( LB ) . Both drugs blocked HIV-1 fusion for multiple cell types , as measured by the Env-dependent cell-cell fusion assay , the virus-dependent cell-cell fusion assay , the virus-cell fusion assay , and infection ( Figure S10 ) . Our previous data demonstrated that the GTPase Rac was activated by HIV-1 Env ligation of CCR5 , resulting in membrane ruffles and lamellipodia in the target cell membrane . Inhibition of this activation by dominant negative Rac or by a Rac GEF inhibitor completely abolished Env-dependent cell-cell fusion , virus dependent cell-cell fusion and infection [4] , [5] , [10] , [11] . Our lab went on to show that Env-induced Rac activation is mediated by Gαq and its downstream effectors , including Ras . Other studies showed that Ras promotes Rac activation via direct interaction with Tiam-1 , or by phosphatidylinositol 3-kinase ( PI3K ) -mediated activation of Tiam-1 [39] . Env-dependent Rac activation likely occurs through the first mechanism , since treatment of target cells with PI3K inhibitors had no effect on Env-dependent cell-cell fusion [40] . The nonreceptor tyrosine kinase , Abl , modulates actin upstream and downstream of Rac [41] , [42] . In the current study , we used siRNAs and specific inhibitors to show that the activity of Abl kinase is required both upstream and downstream of Rac for Env-induced membrane fusion . Upstream of Rac , Abl phosphorylation of the Ras GEF complex promotes the activity of the Rac GEF Tiam-1 , which was shown in the current study to be required for HIV-1 fusion . Downstream of Rac , Abl promotes phosphorylation and activation of Wave2 and its interaction with the Arp2/3 complex , events also demonstrated here to be critical for HIV-1 infection , but not VSV-G or A-MLV Env-mediated infection . These results suggest that these signaling mediators are important for HIV-1 Env mediated entry , are not necessary for pH dependent clathrin or caveolin-mediated endocytosis , and are not required at post-entry steps in the virus life cycle . There is some conflict in the literature as to the location and mechanism of virus cell fusion . A recent report used microscopic imaging to track HIV-1 Env-pseudotyped MLV virus particles and observed virus-membrane fusion in endosomes [3] . This study also showed that virus-cell fusion and infection were inhibited in the presence of the dynamin inhibitor dynasore ( DYN ) which is known to block both clathrin and caveolin-mediated endocytosis [3] . The results in our current study suggest that fusion is occurring via a mechanism that is distinct from that of VSV ( clathrin-mediated endocytosis ) or A-MLV ( caveolin-mediated endocytosis ) . In order to address this conundrum , we treated cells with the dynamin inhibitor DYN , and then used these cells for the Env-dependent cell-cell fusion assay , the virus-dependent cell-cell fusion assay , the virus-cell fusion assay and the TZM-BL infection assay . DYN treatment decreased HIV-1 Env-mediated infection and virus-cell fusion by an average of 58±7% and 50±3% respectively ( Figure S10 and Figure S11 ) . However treatment with DYN decreased A-MLV-Env-HIV-1 infection and VSV-G-HIV-1 infection by 75±5% and 89±1% respectively , showing that the affect on HIV-1 Env-mediated infection was not as significant ( Figure S11 ) . DYN treatment also decreased Env-dependent cell-cell fusion and virus-dependent cell-cell fusion by 53±8% and 50±10% , respectively which was unexpected since these assays both measure cell-cell plasma membrane fusion . Dynamins are a group of large GTPases that are involved in multiple processes in addition to endocytosis , such as vesicle transport , cytokinesis , organelle division , cell movement and cell signaling [43]–[45] . Therefore , the inhibition observed with the dynamin inhibitor DYN could be due to nonspecific effects on cellular processes . In support of this conclusion , a recent study used the Rev-dependent indicator cell line Rev-CEM to study the effects of DYN on HIV-1 replication and VSV-G-HIV-1 infection [44] . Using this assay they observed a dosage dependent decrease in VSV-G-HIV-1 infection with DYN treatment but did not see any decrease in HIV-1 infection [44] . These results as well as the results in Figure 3 show a clear distinction between HIV-1 Env-mediated entry and VSV-G- and A-MLV-mediated entry . The current study also showed that the block in fusion caused by inhibition of Tiam-1 , Abl , Rac , IRSp53 , Wave2 and Arp3 occurs after hemifusion and before cytoplasmic mixing . This conclusion was based on the 1 ) confocal microscopy demonstration that addition of IMB to the fusion reaction allowed membrane but not cytoplasmic mixing , and 2 ) observation that lipid analogs that overcome a block at hemifusion overcame inhibition of HIV-1 virus dependent cell fusion , virus-cell fusion and infection caused by Abl kinase inhibitors and siRNA expression . These results support a model whereby HIV-1 Env binding to CCR5 stimulates activation of Gαq resulting in activation of Rac and activated Rac interacts with IRSp53 . IRSp53 promotes Rac activation of the Wave2 complex , which is also activated by Abl , and activated Wave2 induces subsequent activation of Arp2/3-mediated actin rearrangements which facilitate pore formation , pore enlargement , and entry of HIV-1 . Many microbial pathogens depend on Abl family kinases to mediate efficient infection of their targeted host , including Shigella flexneri , enteropathogenic Escherichia coli , Helicobacter pylori , Anaplasma phagocytophilum , coxsackievirus , poxvirus , and murine AIDS virus . Abl kinases are involved in pathogen entry , intracellular movement , and exit from target cells; proliferation of target cells; and phosphorylation of microbial effectors . Many of these processes involve reorganization of the target cell actin cytoskeleton and depend on the same signaling pathways as HIV-1 [4] , [5] , [46] . Discovery of these signaling mediators as fundamental components of microbial pathogenesis provides new targets for therapeutic intervention . The clinical application of IMB , NIL , and DAS , which block deregulated Abl kinases in leukemia patients , demonstrate that inhibition in vivo is possible with manageable side effects [19] , [20] . In addition IMB has been shown to be an effective inhibitor of anti-apoptotic pathways induced by HIV-1 in macrophages [47] . Most current antiviral therapies target viral proteins and mutation of the virus leads to therapy resistance . Therefore , using inhibitors that target host signaling proteins essential for HIV-1 entry may be an efficient new strategy for treatment of infected patients . U87 . CD4 . CCR5 cells are astroglioma cells expressing CD4 , CCR5-GFP or HA-CCR5 . U87 . CD4 . CXCR4 cells are astroglioma cells expressing CD4 and CXCR4-GFP . CHO-K1 cells ( ATCC ) were grown in F-12K media with 10% serum and other cells maintained as described [48] . pMSCVneo-WT , Y253F , and T315I Bcr-Abl were gifts from Dr . R . Van Etten [21] . The siRNA resistant mutations were generated in Arp3 based on sequences obtained from Santa Cruz Biotechnology , Inc ( SCBT , Santa Cruz , CA , ) , by PCR-mediated mutagenesis of a sub fragment that was sequenced to confirm the presence of mutations before sub cloning into the corresponding cDNA . WT and mutant cDNAs were cloned in pcDNA3 . 1+zeo for expression by transduction . IMB , NIL , and DAS were from LC Laboratories and were used at 10 uM , 500 nM , and 300 nM respectively unless indicated; CPZ ( 0 . 5 mM ) , TFP ( 0 . 3 mM ) , OA ( 100 nM ) , OLA ( 50 uM ) and NH4Cl ( 50 mM ) were from Sigma; TAK-779 ( 1 uM ) , and T-20 ( 10 ug/ml ) were from the AIDS Research and Reference Reagent Program . The control siRNA constructs ( non-targeting 20–25 nt siRNA designed as a negative control ) , the siRNA constructs and antibodies used for Western blots were from SCBT [5] . The siRNA constructs were transfected using GeneEraser siRNA Transfection Reagent or Lipofectamine RNAiMAX Transfection Reagent according to the manufacturer's instructions ( Stratagene , La Jolla , CA , Invitrogen , Carlsbad , CA ) . Wild-type ( WT ) vaccinia ( WR strain ) and recombinant vaccinia viruses expressing β-galactosidase ( vCB21R ) , T7 polymerase ( vPT7-3 ) , constitutively active Rac GTPase ( vRacV12 ) , or HIV-1 Env proteins were described [48] . HIV with R5 YU2 or X4 HXB2 Env in HIVNL4-3 backbone were generated from 293T cells; some were pseudotyped with amphotropic murine leukemia virus ( MLV ) or vesicular stomatitis virus ( VSV ) glycoproteins [5] . TZM-BL assays were performed as described [5] . For the BlaM assay pseudoviruses were produced by co-transfecting 293T cells with HIVNL4-3ΔVpr expressing YU2 , ADA , or HXB2 Env and BlaM-Vpr expressing pMM310 vector . Transfected 293T cell supernatants were harvested 48 h postlipofection , filtered , and assayed for p24 antigen content by enzyme-linked immunosorbent assay . Viruses were resuspended in culture media , aliquoted and stored at −80°C . TZM-bl cells were serum starved for 24–36 h then plated ( 4×104 cells/well ) in 96-well plates in complete media overnight . Cells were treated with indicated concentrations of inhibitors for 1 hr prior to and during 90 min incubation with DEAE-dextran ( 20 µg/ml ) alone or DEAE-dextran ( 20 µg/ml ) and 150 ng p24 HIVYU2Vpr-BlaM , HIVADAVpr-BlaM , or HIVHXB2Vpr-BlaM . After 90 min virus and media were aspirated off cells and 100 ul 1X Lysis and Detection Solution was added to wells ( LyticBlazer-BODIPY FL , Invitrogen ) . The plate was incubated at room temperature in the dark overnight . The BlaM activity was quantified using TECAN fluorescence plate reader ( Tecan , Switzerland ) . The extent of virus-cell fusion was measured with excitation centered at 485 nm and emission centered at 535 nm . The green signal for samples incubated with no inhibitors or inhibitors and no virus was subtracted as background from their respective virus treated samples . TZM-BL cells were serum starved for 12–24 h then plated overnight in complete media in 96 well plate at 2×104 cells per well . Cells were treated for 1 h with indicated concentrations of inhibitors prior to addition of media alone or 150 ng p24 of HIVYU2 HIVHXB2 or VSVG or A-MLV-pseudotyped HIV in the presence of 20 ug/ml DEAE-dextran for 3 h at 37°C . After 3 h cells were washed 3 times with PBS and inhibitors were added in fresh media . Following a 24 h incubation cells were lysed and luciferase ( luc ) units determined . Infected wells and uninfected wells with inhibitor were compared to wells with no inhibitor . For the TZM-Bl assay with lipid analogs serum starved TZM-BL cells were treated with inhibitors for 1 h , then 150 ng of indicated virus was added for 1 h prior to treatment with CPZ or TFP for 1 min or OLA for 5 min . Cells were washed three times with PBS and virus and inhibitors were added back . After 2 h cells were washed two times with PBS and incubated in inhibitor overnight and luc activities were measured . PBMCs that were isolated and stimulated as previously described [5] . They were plated at 5×105 cells per well in 96 well plate and were treated with 10 µM IMB , 250 nM NIL , or 75 nM DAS for 1 h prior to addition of 150 ng p24 of HIVHXB2 in the presence of 20 ug/ml DEAE-dextran for 3 h at 37°C . After 3 h cells were washed three times with PBS and incubated in inhibitor for 24 h . Inhibitors were added back at the same concentration every 24 h for three weeks . 100 ul of supernatant was collected every fourth day and all samples were assayed for p24 antigen content by enzyme-linked immunosorbent assay . Two separate plates were set up under the exact same conditions and one plate was used for p24 measurement and the other was incubated with 20 ul cell viability substrate per 100 ul of sample ( Promega , Madison , WI ) . Envelope-mediated and virus-dependent fusion assays were described . Average fusion compared to untreated control reactions were detected by β-galactosidase activity ± standard deviation [5] . To account for any effect of inhibitors on vaccinia virus infection and/or on T7 polymerase function , vCB21R and vPT7-3 co-infected cells were similarly treated with inhibitors . Concentration curves were performed with all of the inhibitors to determine the concentration that resulted in the maximum decrease in fusion without altering vaccinia virus infection or T7 polymerase activity . Hemifusion assays were performed with 2×106 CHO-K1 cells nucleofected with a GFP expression plasmid , and after 24 h infected with vaccinia virus expressing HIVADA Env or no Env . After 16 h , 4×105 U87 . CD4 . CCR5 . HA cells were added for 3 h , fixed with paraformaldehyde , stained with TRITC-conjugated CTX-555 ( EMD ) , and analyzed on a 510 Meta LSM confocal microscope . Fusion and infectivity results were compared using a two-tailed t-test . All p values , unless indicated , were <0 . 03 .
Patients infected with HIV-1 are currently treated with highly active antiretroviral therapy ( HAART ) that efficiently suppresses the virus but does not cure the infection . HIV-1 envelope activates Rac-mediated actin cytoskeleton rearrangements in the target cell that promote membrane fusion and entry . We discovered that these rearrangements require activation of the actin polymerization machinery including the tyrosine kinase Abl . We also showed that Abl kinase inhibitors imatinib , nilotinib , and dasatinib , current drug therapies for chronic myeloid leukemia , block HIV-1 entry and infection . These results suggest that these inhibitors might be appropriate drugs for treatment of HIV-1 . This strategy of using inhibitors that disable host signaling proteins rather than viral proteins , essential for pathogen survival , may have a general efficacy in developing drugs to combat HIV-1 and other pathogens that acquire drug resistance .
You are an expert at summarizing long articles. Proceed to summarize the following text: Centrosome amplification ( CA ) is a common feature of human tumours and a promising target for cancer therapy . However , CA’s pan-cancer prevalence , molecular role in tumourigenesis and therapeutic value in the clinical setting are still largely unexplored . Here , we used a transcriptomic signature ( CA20 ) to characterise the landscape of CA-associated gene expression in 9 , 721 tumours from The Cancer Genome Atlas ( TCGA ) . CA20 is upregulated in cancer and associated with distinct clinical and molecular features of breast cancer , consistently with our experimental CA quantification in patient samples . Moreover , we show that CA20 upregulation is positively associated with genomic instability , alteration of specific chromosomal arms and C>T mutations , and we propose novel molecular players associated with CA in cancer . Finally , high CA20 is associated with poor prognosis and , by integrating drug sensitivity with drug perturbation profiles in cell lines , we identify candidate compounds for selectively targeting cancer cells exhibiting transcriptomic evidence for CA . The centrosome , an organelle composed of two centrioles surrounded by a pericentriolar protein matrix , is the major microtubule-organising centre of animal cells , hence being pivotal for several fundamental cellular processes , including signalling , cell polarity , division and migration [1–4] . Each centrosome duplicates once per cell cycle to ensure bipolar spindle assembly and successful chromosome segregation [5 , 6] . Centrosomes are thus implicated in the maintenance of genome stability . Centrosome amplification ( CA ) –the presence of more than two centrosomes—is a common feature in cancer [7] . Supernumerary centrosomes generate multipolar mitosis and consequent genome instability [6 , 8–10] , they can accelerate and promote tumourigenesis in vivo [11–13] and promote cellular invasion and metastatic behaviour [14–17] . However , CA’s pan-cancer prevalence , molecular role in tumourigenesis and therapeutic value remain poorly understood , largely due to the technical challenges associated with profiling such small cellular structures in human cancer tissues . For instance , quantifying centrosome numbers and abnormalities is often hampered by the limited thickness of formalin-fixed and paraffin-embedded tissue sections , preventing the imaging of entire cells [18] . In addition , three-dimensional imaging and analysis are mandatory , but cumbersome and time consuming [19] . To at least partially circumvent those challenges , we propose the estimation of CA based on the expression levels of CA-associated genes . Recently , proof-of-principle gene-expression-based CA signatures have been developed [20–23] , the most comprehensive one being CA20 , based on the expression of TUBG1 , which encodes the most abundant centrosomal protein , and 19 other genes whose overexpression has been experimentally shown to induce CA [23] . This signature was proposed to reflect CA levels in tumour samples and shown to have a prognostic value in two independent breast cancer cohorts [23] . In the present study , we used CA20 to estimate relative CA levels across 9 , 721 tumour and 725 matched-normal samples of 32 cancer types from The Cancer Genome Atlas ( TCGA ) , thereby revealing the first pan-cancer landscape of CA-associated gene expression . We show the association of CA20 with distinct breast cancer clinical and molecular features . We also break down the independent associations of CA20 with different sorts of genomic instability across cancer types . Finally , we show that high CA20 is associated with poor clinical outcome in different cancer types , having identified candidate compounds for selectively targeting cancer cells exhibiting transcriptomic evidence for this hallmark of cancer . To estimate relative CA levels in human samples , we used CA20 , a score based on the expression of 20 genes experimentally associated with CA [23] , as a surrogate . We quantified CA20 across the transcriptomes ( profiled by RNA-seq ) of 9 , 721 tumour and 725 matched-normal samples spanning 32 cancer types from TCGA ( Fig 1a , S1 Table ) . CA20 correlates with the predicted proliferation rates of TCGA tumour samples [24] ( Spearman’s correlation coefficient , r = 0 . 4 , p-value < 2 . 2e-16; S1a Fig ) , as expected , given that some of the CA20 genes encode for proteins involved in cell proliferation . Cervical ( CESC ) , testicular ( TGCT ) and oesophageal ( ESCA ) cancers show high CA20 , contrasting with lower scores in kidney ( KIRP , KICH and KIRC ) and prostate ( PRAD ) cancers ( Fig 1b ) . Some cancer types , such as low-grade glioma ( LGG ) and breast invasive carcinoma ( BRCA ) , exhibit high variability of CA20 , concordantly with previous observations that the proportion of cells with CA in breast tumours ranges from 1 to 100% [7 , 25] depending on the tumour subtype [26] . We also observed significant differences in CA20 between specific cancer types with the same tissue of origin . Although all kidney cancers have low CA20 scores , kidney renal papillary cell carcinoma ( KIRP ) shows a lower score than the other types ( p-value < 0 . 0001 , Wilcoxon rank-sum test; S1b Fig ) . Similarly , glioblastoma multiforme ( GBM ) , skin cutaneous melanoma ( SKCM ) and lung squamous cell carcinoma ( LUSC ) show higher CA20 than low-grade glioma ( LGG ) , uveal melanoma ( UVM ) and lung adenocarcinoma ( LUAD ) , respectively ( p-value < 0 . 0001 for all comparisons , Wilcoxon rank-sum test; S1b Fig ) . We note that squamous cell carcinomas have higher CA20 within cervical ( CESC ) and oesophageal ( ESCA ) cancers ( p-value < 0 . 001 and < 0 . 01 , respectively , Wilcoxon rank-sum test; S1c Fig ) , suggesting that the observed differences are indeed associated to the different cell types of origin and not only to differences between tissue of origin . Since CA has been considered a hallmark of tumour cells [7] , we tested the difference of CA20 between tumour and matched-normal samples . Indeed , tumour samples have higher CA20 levels in all 15 cancer types with both sample types available ( at least 10 samples of each type; False Discovery Rate ( FDR ) < 0 . 0001 , Wilcoxon rank-sum test; Fig 1c ) . In addition , using linear regression analyses with proliferation rate as an additional covariate , we found that CA20 is higher in tumour samples , either when considering all cohorts together ( linear regression p-value < 0 . 0001 , using cohort as an additional covariate; S2 Table ) or per individual cohort ( FDR < 0 . 0001 for all cohorts; S2 Table ) , independently of proliferation rate , discarding the suggestion of CA20 being its mere surrogate . These results emphasise CA as a hallmark of cancer . Breast cancer is one of the best studied cancer types , with large cohorts of clinically annotated tumour samples available [27 , 28] , and where the CA20 score was developed [23] . In addition , CA has been frequently correlated with aggressive features in breast cancer [6 , 25 , 26 , 29] . Given that we observed high variability of CA20 in TCGA breast tumour samples , we sought to investigate in more detail the relationship between CA20 and different breast cancer molecular features in that cohort . CA20 is higher in tumours than in normal breast samples ( p-value < 0 . 0001 , Wilcoxon rank-sum test; Fig 2a ) and we found higher levels of CA20 in invasive tumours from ductal histologic subtype ( the most common , accounting for 90% of tumours ) [30] when compared with lobular ones ( p-value < 0 . 0001 , Wilcoxon rank-sum test; Fig 2b ) . The difference between ductal and lobular subtypes is consistent in non-triple negative breast tumours ( p-value < 0 . 0001 , Wilcoxon rank-sum test; S2d Fig ) , as well as in samples from tumour stages II and III ( p-value < 0 . 0001 and < 0 . 01 , respectively , Wilcoxon rank-sum test; S2e Fig ) . We also tested the differences in CA20 between the different PAM50 molecular subtypes , derived based on a 50-gene classifier [31] . Basal-like breast tumours have the highest CA20 scores ( p-value < 0 . 0001 , p-value < 0 . 0001 , and p-value < 0 . 001 for contrasts with luminal A , luminal B , and HER2-enriched , respectively , Wilcoxon rank-sum test; Fig 2c ) . This is in line with our recent work experimentally showing that basal-like breast cancers have indeed more CA than luminal ones [26] . We also observed a strong difference between luminal subtypes , with higher CA20 in luminal B samples ( p-value < 0 . 0001 , Wilcoxon rank-sum test; Fig 2c ) . Moreover , we tested the association between CA20 and tumour stage , having found a significant CA20 increase from stage I to stage II ( p-value < 0 . 0001 , Wilcoxon rank-sum test; Fig 2d ) , but no significant changes between subsequent stages ( Fig 2d ) . All associations between CA20 and breast cancer histology , PAM50 molecular subtypes and tumour stage remain significant within both low and high proliferating tumours ( samples divided by the median of estimated proliferation rates; S2a–S2c Fig ) . All the aforementioned associations were validated in an independent cohort ( Fig 2e–2h , S2f and S2g Fig and S3 Table ) , comprising 144 normal and 1 , 992 tumour breast samples from the Molecular Taxonomy of Breast Cancer International Consortium ( METABRIC ) [28] . We still tested the association between CA20 and the METABRIC integrative clusters , 10 molecular subgroups defined based on joint clustering of copy number and gene expression data [28] . CA20 varies across integrative clusters ( p-value < 0 . 0001 , Fligner-Killeen test ) and is particularly enriched in cluster 10 ( FDR < 0 . 0001 , Wilcoxon rank-sum test , for comparisons with each of all the other clusters; S2h Fig ) , characterized by high proportion of basal-like tumours , high genomic instability , high rate of TP53 mutations , chromosome arm 5q deletions and very poor prognosis in the short term [28] . We complementarily analysed the frequency of CA in human breast carcinomas from the different PAM50 molecular subtypes , comprising 29 luminal A , 3 luminal B , 3 HER2 and 13 basal-like tumours ( Fig 2i and S4 Table ) . Concordantly with TCGA and METABRIC results , we observed a higher percentage of cells with supernumerary centrioles in luminal B ( average of 27% ) than in luminal A carcinomas ( 7%; p-value < 0 . 05 , Wilcoxon rank-sum test; Fig 2j and S3 Fig ) . Moreover , basal-like ( 25% ) display higher levels of CA than luminal A tumours ( p-value < 0 . 0001 , Wilcoxon rank-sum test ) . Despite the reduced number of luminal B samples , our patient data support CA20 as a good surrogate of CA levels and the suggestion that CA is more frequent in luminal B than in luminal A human breast carcinomas . CA and consequent multipolar mitoses have been associated with aneuploidy , genomic instability and tumourigenesis for more than a century [32 , 33] . Using the available quantitative characterization of aneuploidy in TCGA [34] , we found that CA20 is higher in samples with genome doubling ( p-value < 0 . 0001 , Wilcoxon rank-sum test; Fig 3a ) and positively correlated with their aneuploidy score ( measured as the total number of altered—gained or lost—chromosome arms; Spearman’s correlation coefficient , r = 0 . 44 , p-value < 2 . 2e-16; Fig 3b ) . Although CA20 is positively correlated with both chromosomal deletions and amplifications ( Spearman’s correlation coefficient , r = 0 . 41 and 0 . 36 , p-value < 2 . 2e-16 , respectively; S4a and S4b Fig ) , it is more strongly associated with chromosomal deletions ( p-value < 2 . 2e-16 , t-test for z-transformed coefficients; see also S4c Fig ) . Given the known association between loss of p53 and CA [6 , 7 , 35] and the recent observation that p53 null cells tend to have an enrichment of chromosome losses over gains [36] , we tested the hypothesis that the observed association between CA20 and chromosomal deletions could be linked to TP53 mutations . However , the increase in the proportion of deletions per sample from low to high CA20 samples is consistent within both TP53 wild-type and mutated samples ( p-value < 0 . 0001 and < 0 . 05 , respectively , Wilcoxon rank-sum test; S4d and S4e Fig ) , showing it is independent of TP53 mutations ( two-way ANOVA p-value for interaction = 0 . 6; S4d Fig ) . Investigating the hypothesis that CA20-associated aneuploidy levels could vary between chromosomes , we identified 20 chromosome arms whose deletion ( 10 arms ) or amplification ( 10 arms ) was enriched in tumour samples with higher CA20 ( linear regression , FDR < 0 . 05; Fig 3c and S2 and S5 Tables ) . The strongest associations were with loss of 5q , 16p and 7p . Interestingly , 5q deletion was previously associated with CA20-high basal-like breast tumours [27 , 37–40] and METABRIC integrative cluster 10 [28] ( Fig 2c and 2g and S2h Fig ) . The association between CA20 and 5q deletion remains when removing the breast cancer cohort ( linear regression p-value < 2 . 2e-16; S5 Fig and S2 Table ) . This observation raises the question if matched-normal samples of the analysed tumour samples have a CA20 signal predictive of those 5q , 16p and 7p deletions . We tested this hypothesis by comparing the CA20 levels between normal samples ( with intact tested chromosomal arms ) whose matched tumours lost 5q , 16p or 7p , with those with tumours with amplifications or no alterations in those chromosomal arms . We found that normal samples whose matched tumours lost 5q or 16p exhibit higher CA20 scores ( p-value < 0 . 01 and < 0 . 05 , respectively , Wilcoxon rank-sum test; S6 Fig ) , therefore suggesting that a CA20 increase may precede those chromosomal abnormalities . In addition to tumour aneuploidy , CA20 is positively correlated with mutation burden , number of somatic Copy Number Alterations ( CNA ) and number of clones per tumour ( Spearman’s correlation coefficient , r = 0 . 48 , 0 . 47 and 0 . 43 , respectively , p-value < 2 . 2e-16 for all; Fig 3d–3f ) . All these associations are independent of cell proliferation ( linear regression p-values < 1e-8 for all; S2 Table and S7 Fig ) . We found that the correlation with mutation burden holds for different types of mutations ( silent , missense , splice site and nonsense ) , as well as for mutations shown to be pathogenic ( data from ClinVar https://www . ncbi . nlm . nih . gov/clinvar/ ) in all diseases and particularly in cancer ( S8 Fig ) . Since these genomic instability features are likely correlated between each other , we applied multiple linear regression analyses across 1050 tumour samples ( from 12 different cancer types; minimum of 30 and average of 88 samples per cohort ) with information for those 4 covariates ( S6 Table ) . We identified independent positive associations between CA20 and all genomic instability features , with stronger association for CNAs ( linear regression p-values = 1 . 3e-5 , 7 . 2e-4 , 5 . 3e-10 and 6 . 4e-3 for aneuploidy , mutation burden , CNA and number for clones , respectively; Fig 3g and S2 Table ) . These associations remain significant when proliferation rate is used as an additional covariate in the regression ( p-values = 2 . 3e-5 , 7e-4 , 2 . 4e-9 and 0 . 03 for aneuploidy , mutation burden , CNA and number for clones , respectively; S2 Table ) . We performed similar analyses per TCGA cohort and identified a group of cancer types where CA20 is mostly associated with CNA and aneuploidy ( prostate adenocarcinoma , glioblastoma multiforme , bladder urothelial carcinoma , and brain low-grade glioma; Fig 3g and S9 Fig; S2 Table ) . Although CA has been globally associated with genomic instability , these results highlight CNA as the main associated feature and show that these associations differ between cancer types . Point mutations are one of the most common types of mutational events that impact the stability of a cancer genome . We examined the pan-cancer association between CA20 and somatic mutations in 14 , 589 genes and found 752 whose mutations are associated with CA20 ( FDR < 0 . 05; Fig 4a and S2 and S7 Tables ) . Most significant associations of mutated genes with the CA20 score are positive , consistently with its correlation with higher mutation burden ( Fig 3d ) , and enriched in cancer driver genes ( Gene Set Enrichment Analysis ( GSEA ) [41 , 42] p-value < 0 . 001 , using a list of 299 cancer driver genes derived from TCGA’s PanCancer analysis [43]; S10a Fig ) . TP53 shows the strongest association ( linear regression p-value < 0 . 0001; Fig 4a ) , with positive correlations for the majority of cancer types surveyed ( 10 out of 17 cancer types with at least 20 mutated samples; FDR < 0 . 05; Fig 4b ) , therefore putatively extending the reported association between loss of p53 and CA [6 , 7 , 35] to 10 different cancer types . The second strongest positive association is with tumour suppressor pRb ( RB1 ) , whose acute loss has been found to induce CA [44] . Unexpectedly , the strongest negative association is with E-cadherin ( encoded by CDH1 ) , meaning CDH1-mutated samples have lower CA20 levels . Given its tumour suppressor role in cancer and the fact that its mutations mostly induce loss of function [45] , this result suggests loss of E-cadherin is associated with lower CA in human tumours , which is contrary to what have been reported in epithelial cancer cells [46] . GSEA on genes whose mutations are associated with CA20 found that they are enriched in cancer-associated pathways and Wnt/β-catenin signalling ( S10c–S10f Fig ) . As only a small fraction of somatic mutations represent driver events , we repeated the pan-cancer analysis of association between CA20 and somatic mutations using likely driver mutations from the Cancer Genome Interpreter ( https://www . cancergenomeinterpreter . org/mutations ) [45] . Within the tested 33 genes with at least 10 mutated samples , we found three ( TP53 , PIK3CA and EGFR ) whose driver mutations are associated with CA20 ( FDR < 0 . 05; S10b Fig and S2 and S8 Tables ) , TP53 being again the strongest association . Overall , we show that CA20 is associated with both passenger and driver mutational spectra in cancer , with particular enrichment in cancer driver genes and Wnt/β-catenin signalling . CA has still been proposed as a driver of genomic instability [11] . We thus wondered if the DNA mutation spectrum associated with CA was similar to specific signatures of somatic mutations caused by different mutational processes in cancer [47] . We therefore retrieved the contribution of the 30 published mutational signatures for each TCGA tumour sample from mSignatureDB [48] and uncovered three of them positively associated with CA20: signature 3 , associated with BRCA1/2 mutations; signature 13 , attributed to APOBEC activity; and signature 4 , characteristic of smoking’s mutational pattern ( FDR < 0 . 05; S11 Fig ) . As these signatures are likely confounded with genomic instability , we performed multiple linear regression on CA20 including , as independent variables , the mutational signature and the four aforementioned genomic instability features: aneuploidy , mutation burden , CNA and number of clones per tumour ( S2 Table ) . Signature 1 , linked with ageing and characterised by C>T substitutions ( S12a Fig ) , and its “reverse” ( T>C substitution bias ) Signature 12 , found mainly in liver cancer ( S12b Fig ) , are respectively positively and negatively associated ( FDR < 0 . 05 ) with CA20 ( Fig 4c ) , independently of other types of genomic instability and even when proliferation rate is added as a variable ( FDR = 0 . 051 for both signatures ) . To evaluate the putative causality of CA20-associated mutations ( Fig 4a ) , we interrogated the Connectivity Map ( CMap ) database of signatures [49] about the impact of each of the 3 , 799 gene knock-downs on the CA20 gene set in human cancer cell lines . The resultant connectivity scores ( S9 Table ) , ranging from 100 ( CA20 up-regulation ) to -100 ( CA20 down-regulation ) , were compared with the pan-cancer association between somatic mutations in the cognate genes and CA20 ( Fig 4d ) . We thereby identified 6 genes with a putative causal effect on CA20 scores ( |connectivity score| > 80; Fig 4d ) : P2RY12 , RB1 , ITSN1 and MYCBP2 are putative inhibitors of CA ( their knock-down up-regulate CA20 genes ) , whereas ABCC5 and COPA are putative promoters of CA ( their knock-down down-regulate CA20 genes ) . Although acute loss of pRb ( encoded by RB1 ) has been found to induce CA [44] , confirming pRb as a CA inhibitor , to our knowledge none of the remaining genes identified herein has been previously associated with CA . They are therefore interesting candidates for future functional studies . Genes from a manually curated list of centriole duplication factors ( 93 genes , including only 10 from the CA20 signature; S10 Table ) are enriched in negative CMap knock-down scores ( GSEA p-value < 0 . 001; Fig 4e ) , suggesting they are indeed needed for cells to express CA-associated genes . Using the MSigDB’s Hallmark Gene Sets library [50] , we identified unfolded protein response and mitotic spindle as significantly enriched in genes whose knock-down showed negative scores , i . e . CA20 down-regulation ( GSEA FDR < 0 . 05; Fig 4f ) . This association suggests that mitotic spindle components activate CA-associated genes and/or that cells highly expressing CA-associated genes may be less likely to survive when their mitotic spindle is perturbed . CA has been associated with poor patient prognosis in a variety of cancer types [7] . We therefore tested CA20’s association with overall patient’s survival across 31 TCGA cancer types with more than 40 samples each , finding high CA20 significantly associated with worse prognosis in 8 different cancer types ( FDR < 0 . 05 , log-rank test; Fig 5a and S11 Table ) . This result supports the potential of CA20 for prognostic-based patient stratification . Hypoxia is a potent microenvironmental factor promoting genetic instability and malignant progression [51–53] . Given that hypoxia has been shown to enhance centrosome aberrations in breast cancer [54 , 55] , we investigated whether CA20 is associated with the relative hypoxia levels in TCGA tumour samples , given by a previously established surrogate metagene expression signature [56] . We found a positive correlation between CA20 and the hypoxia score ( Spearman’s correlation coefficient , r = 0 . 61 , p-value < 2 . 2e-16; Fig 5b ) that is independent of genomic instability ( linear regression p-value = 7 . 8e-9; S2 Table ) . We further confirmed that this association is independent of estimated proliferation rates ( linear regression p-value = 5 . 6e-7 when proliferation rate is added as a covariate to the regression; S13a Fig and S2 Table ) . We also performed this linear regression analysis for each of the 12 TCGA cohorts with information for all covariates and identified three cancer types ( glioblastoma multiforme , lung adenocarcinoma and bladder urothelial carcinoma ) where hypoxia is positively associated ( FDR < 0 . 05 ) with CA20 ( Fig 5c; S2 Table ) . Although a tumour is also composed by stromal and immune cells [57] , the association between CA and tumour cellular composition has not been addressed yet . CA20 is associated with lower stromal ( Spearman’s correlation coefficient , r = -0 . 52 , p-value < 2 . 2e-16; Fig 5d ) and immune ( Spearman’s correlation coefficient , r = -0 . 34 , p-value < 2 . 2e-16; S13c Fig ) cell infiltration in TCGA . However , pan-cancer linear regression analyses revealed that only the negative association with stromal infiltration is independent of genomic instability ( linear regression p-value = 2 . 7e-6 and 0 . 24 , for stromal and immune , respectively; S2 Table ) . The same was observed when including proliferation rate as an additional covariate ( linear regression p-value = 1 . 2e-4 and 0 . 21 , respectively; S13b and S13d Fig and S2 Table ) . We have also performed similar analyses for each of the 5 TCGA cohorts with information for all covariates and found that CA20 is significantly associated ( FDR < 0 . 05 ) with lower stromal infiltration in head and neck and lung cancers ( Fig 5e ) , with lower immune infiltration in glioblastoma , and with higher immune infiltration in head and neck cancer ( S13e Fig ) , all independently of genomic instability ( S2 Table ) . CA is a hallmark of cancer cells and hence an appealing target in cancer therapy . In order to identify compounds that could target cancer cells with such abnormality , we have employed CA20 to estimate relative CA levels in 823 human cancer cell lines from the Cancer Therapeutics Response Portal ( CTRP ) [58] ( S12 Table ) , for which both transcriptomic and drug-sensitivity profiles are publicly available . Correlation analyses between CA20 and drug-sensitivity ( in Area Under the dose-response Curve , AUC ) for 354 compounds revealed 81 negatively correlated with CA20 ( FDR < 0 . 05 , Spearman’s correlation; Fig 6a and S13 Table ) , i . e . higher CA20 was associated with lower drug AUC and , therefore , higher drug activity . The enrichment of negative correlations ( S14 Fig ) may reflect the bias for cancer-targeting compounds in CTRP . These results suggest several candidate compounds to selectively kill cancer cells with CA , such as 3-CI-AHPC , CD-437 , STF-31 , methotrexate , BI-2536 and clofarabine ( Fig 6b ) . The first three are probes , methotrexate and clofarabine are U . S . Food and Drug Administration ( FDA ) -approved drugs for several cancer types ( https://www . cancer . gov/about-cancer/treatment/drugs/methotrexate ) and paediatric acute lymphoblastic leukemia ( https://www . cancer . gov/about-cancer/treatment/drugs/fda-clofarabine ) , respectively . Interestingly , BI-2536 has been in clinical trials for several solid and liquid tumours ( https://clinicaltrials . gov/ct2/results ? cond=&term=bi+2536&cntry=&state=&city=&dist ) and is an inhibitor of polo-like kinase 1 ( PLK1 ) , whose inhibition has already been associated with CA suppression [59 , 60] . Complementarily , we mined the CMap database to identify compounds that could impact the CA20 score and therefore putatively reduce/increase CA levels . We calculated the impact of 2 , 837 compounds on the CA20 transcriptomic levels in human cancer cell lines ( S14 Table ) and identified some whose activity drove CA20 up-regulation ( putative CA promoters; S15 Fig ) , such as VEGFR2-kinase-inhibitor-IV , dienestrol ( oestrogen receptor agonist ) and sulforaphane ( anticancer agent in clinical trials for Bladder , Breast , Lung and Prostate cancers; https://clinicaltrials . gov/ct2/results ? cond=sulforaphane&Search=Apply&recrs=d&age_v=&gndr=&type=&rslt= ) . We also identified compounds that down-regulated CA20 , such as two CDK inhibitors ( purvalanol-a and aminopurvalanol-a ) , JAK3-inhibitor-VI , etoposide ( topoisomerase and cell cycle inhibitor ) and CD-437 ( agonist of RARG , retinoic acid receptor gamma; Fig 6c ) . For the 164 drugs tested in both datasets , we observed a positive correlation between their CA20/sensitivity correlations in CTRP and their CMap scores ( Spearman’s correlation coefficient , r = 0 . 26 , p-value = 8 . 3e-4; Fig 6d ) , indicating that drugs selectively targeting cells with higher CA20 are reducing the expression of these genes , possibly by killing the abnormal cells in the tumour cell population . These complementary approaches uncovered RARG’s agonist CD-437 as the strongest candidate for targeting CA . Moreover , drugs targeting coagulation factor II ( F2R ) , farnesyltransferase ( FNTA and FNTB ) , ubiquitin isopeptidases ( USP13 and USP5 ) , DNA topoisomerase II alpha ( TOP2A ) and cyclin-dependent kinases ( CDKs ) are also promising candidates ( Fig 6d ) . Given cell proliferation’s association with CA20 ( S1a Fig ) , we have tested the association between its estimated rates across TCGA primary tumour samples and the expression of the 164 compounds’ predicted target genes ( merging this information from the CTRP ( S13 Table ) and CMap datasets ( S14 Table ) ) , using linear regression analyses with cohort as additional covariate ( S2 Table ) . The resultant coefficients ( S15 Table ) are not correlated with CMap’s average scores of the respective compounds ( Spearman’s correlation coefficient , r = 0 . 016 , p-value = 0 . 84; S16a Fig ) , but are correlated with their CTRP’s Spearman correlation coefficients ( Spearman’s correlation coefficient , r = -0 . 26 , p-value = 9e-04; S16b Fig ) , i . e . compounds selective for cells with high CA20 are predicted to target genes positively associated with proliferation in TCGA tumour samples . Nevertheless , predicted target genes of several compound candidates from our analyses do not show strong association with proliferation ( S16c and S16d Fig ) . These results need to be considered when prioritizing candidate compounds for further experiments aiming to target cancer cells through CA . CA is known to promote tumourigenesis but its molecular role therein remains elusive and , although it is also suggested to be a promising target for cancer therapy , CA’s prevalence in different types of cancer and therapeutic value in the clinic are still pretty much unprobed . Using the CA20 signature and TCGA RNA-seq data , we characterise the landscape of CA-associated gene expression in a broad range of cancer types , thereby demonstrating the potential of using gene expression-based signatures in multi-omic and clinical data integrative approaches to investigate the biological and medical relevance of their respective cellular and molecular processes . Despite the lack of a full direct experimental validation of CA20 as a surrogate of CA levels , our observations are very consistent with known CA’s features , namely CA20’s upregulation in cancer [7] and in basal-like breast tumours [26] , and its association with the knock-down of centriole duplication factors , genomic instability [11] , loss of p53 [6 , 7 , 35] and pRB [44] , hypoxia [54 , 55] and worse patient’s prognosis [7] . In addition , we found that luminal B breast tumours have higher prevalence of CA than luminal A ones , concordantly with the observed differences in the CA20 score between the two molecular subtypes in two independent cohorts . Finally , we have analysed two transcriptomic datasets of multiciliogenesis , where cells escape centriole number regulation to generate hundreds of centrioles during differentiation [61] , and found that CA20 increases during the centriole overduplication stage , resuming basal levels afterwards ( S17 Fig ) , suggesting CA20 as a marker of active amplification . These observations vouch for the present proof-of-concept study to pave the way for more in-depth and bona fide findings when CA’s transcriptomic signature is experimentally refined . Moreover , here we already propose novel hypotheses that will trigger studies aiming at a more comprehensive understanding of the role of CA in cancer . We observed higher CA-associated gene expression in cancer samples of squamous cell origin than in adenocarcinomas , suggesting that their different cell types of origin can have different CA’s prevalence and/or ways to cope with this abnormality . Previous work has indeed shown that CA triggers spontaneous squamous cell carcinomas , lymphomas and sarcomas , but not adenocarcinomas , in mice [11] . We also show that breast invasive carcinoma samples have high variability on CA20 , concordantly with previous observations [7 , 25] , that is related to their distinct clinical and molecular features . We had recently shown that basal-like breast carcinomas have higher CA than luminal tumours [26] , but here we report for the first time an upregulation of CA-associated genes in tumours from both invasive ductal histologic subtype and luminal B molecular subtype . We validated the CA20-based predictions by quantitatively analysing centrosome numbers in human breast carcinoma samples , where we found that indeed CA is more prevalent in luminal B than luminal A tumours , providing a novel insight into the differences between these two hormone-receptor positive molecular subtypes . Given the limited number of luminal B samples in our cohort , more extensive analyses are necessary to confirm this association . Our data show that centrosome amplification is associated with breast cancer clinical features and endorses the potential of using a gene-expression-based signature for patient stratification . CA-associated gene expression upregulation is positively correlated with different types of genomic instability , like aneuploidy , mutation burden , CNA and tumour heterogeneity . In particular , CA20 is more strongly associated with chromosomal deletions than amplifications , independently of TP53 mutations . We speculate that this association may be due to the impact of CA in cellular genomic stability having non-random genomic “hot spots” . In fact , through a more detailed analysis , we found an association with alterations in specific chromosomal arms , that may be due to the localisation of genes encoding for regulators of CA20 genes therein and/or to those arms’ higher susceptibility to the genomic instability triggered by centrosome abnormalities . The latter is supported by recent work showing that human chromosome mis-segregation is not random and can be biased by inherent properties of individual chromosomes [62] , and also by our observation that normal samples whose matched tumours lost 5q or 16p have higher CA20 predictive of those deletions ( S6 Fig ) . Moreover , we characterised the DNA mutation spectrum associated with CA20 and found it to be enriched in C>T mutations , a signature characteristic of ageing , with which centrosome aberrations have also been associated [63–67] . Genes whose mutations are associated with CA20 are enriched in cancer driver genes , and particularly in Wnt/β-catenin signalling . Wnt/β-catenin signalling components interact with the centrosome [68] and a previous study has demonstrated that mutant β-catenin induces centrosome aberrations in normal epithelial cells and is required for CA in cancer cells [69] . Our results extend this previous association to human cancer samples , suggesting mutations in β-catenin might contribute to the observed CA in cancer . Finally , we show the usefulness of a novel approach whereby we integrated information on genes whose somatic mutations are associated with CA20 in TCGA tumour samples with the impact of their knock-downs on the CA20 expression in human cancer cell lines , aiming at unveiling candidate molecular players in CA in cancer . Concordantly with previous work on CA [7] , we observed that high CA20 is associated with poor patient’s survival in several cancer types . Furthermore , we found a positive correlation between CA20 and hypoxic levels in glioblastoma multiforme that is particularly interesting , due to its highly hypoxic microenvironment and HIF-1α levels [70] , also shown to enhance migration and invasion of its tumour cells [71 , 72] . Given the observed association between CA and invasion of tumour cells [15 , 17] , an exciting hypothesis is hypoxia-induced invasion being mediated through CA . When looking at the tumour cellular composition , we found that tumours with high CA20 have lower stromal and immune cell infiltration , although the latter is not independent of tumour genomic instability and proliferation rate . Detailed studies aiming to decouple these effects could provide relevant molecular insights when considering immunotherapy , alone or in combination with genotoxic and/or anti-proliferative therapeutic approaches . Moreover , by pioneering the integration of drug sensitivity with drug perturbation profiles in human cancer cell lines , we identify candidate compounds for selectively targeting cancer cells exhibiting transcriptomic evidence for CA . These compounds could be particularly useful in the treatment of cancer types we identified as having high CA and to whose current therapy patients respond poorly . For instance , their potential in specifically targeting basal-like and luminal B breast tumours could be assessed by taking advantage of resources like patient-derived tumour xenografts [73] . The observed ability of cells carrying extra centrosomes to manipulate the surrounding tumour cells and promote their invasiveness [15 , 17] suggests that targeting the former may be clinically more impactful . Given CA’s cancer-specificity , the compounds identified herein could underlie the development of novel targeted cancer therapeutic options . The study with human samples was conducted under the national regulative law for the handling of biological specimens from tumour banks , with samples being exclusively used for research purposes in retrospective studies , and was approved by the ethics committee of the Hospital Xeral-Cies , Vigo , Spain . Informed consent was obtained from all human participants . Publicly available RNAseqV2 ( quantified through RNA-seq by Expectation Maximization ) [74] and clinical data for 9 , 721 tumour and 725 matched-normal samples from The Cancer Genome Atlas ( TCGA; https://cancergenome . nih . gov/ ) were downloaded from Firebrowse ( http://firebrowse . org/ ) . Gene expression ( read counts ) data were quantile-normalized using voom [75] . For each sample , the CA20 score was calculated as the sum of the across-sample ( including both tumours and matched-normal samples ) normalized ( log2 median-centred ) expression levels of the CA20 published signature genes [23]: AURKA , CCNA2 , CCND1 , CCNE2 , CDK1 , CEP63 , CEP152 , E2F1 , E2F2 , LMO4 , MDM2 , MYCN , NDRG1 , NEK2 , PIN1 , PLK1 , PLK4 , SASS6 , STIL and TUBG1 ( Fig 1a ) . Predicted proliferation rates of each TCGA tumour sample were retrieved from [24] ( n = 9 , 568 ) . Whole genome doubling ( corresponding to 0 , 1 and ≥ 2 genome doubling events in the clonal evolution of the cancer ) , aneuploidy ( both aneuploidy score—number of altered chromosome arms—and alterations per chromosome arm ) and mutation burden characterizations were retrieved from [34] ( n = 9 , 166 ) . Since the chromosomal arm status was not available for TCGA normal samples , we have selected only those with no CNA in the chromosomal arms tested , to make sure they are intact . CNA ( n = 8 , 879; copy number levels were derived with the GISTIC algorithm [76] and considered as CNA if having a score lower than -1 ( loss ) or higher than 1 ( gain ) ) and mutation ( n = 7 , 120; including classification as silent , missense , splice site or nonsense ones ) processed data were downloaded from Firebrowse ( http://firebrowse . org/ ) . Mutations were classified as likely pathogenic and pathogenic based on ClinVar database’s ( https://www . ncbi . nlm . nih . gov/clinvar/ ) variant summary annotation ( ftp://ftp . ncbi . nlm . nih . gov/pub/clinvar/tab_delimited/variant_summary . txt . gz; accessed in November 12th 2018 ) , and 5 , 601 likely driver mutations were obtained from the Cancer Genome Interpreter ( https://www . cancergenomeinterpreter . org/mutations; accessed in November 12th 2018 ) [45] . The list of 299 cancer driver genes was retrieved from [43] . Intra-tumour heterogeneity data , measured by the number of clones per sample , were retrieved from [77] ( n = 1 , 080 ) . The mutational signature profiles were retrieved from mSignatureDB [48] ( n = 9 , 004 ) . The predicted fraction of stromal ( stromal score ) and immune ( immune score ) cells in TCGA tumour samples ( n = 2 , 463 ) was retrieved from [78] . We used the scores calculated based on RNASeqV2 expression levels . Importantly , no CA20 gene was used by the authors to infer those cell proportions [78] . TCGA tumour samples were analysed for hypoxic status based on expression of 95 genes included in the hypoxia 99-metagene signature [56] . The four missing genes are three ( LOC149464 , LOC56901 and TIMM23 ) for which expression levels were not available and NDRG1 , excluded for being part of the CA20 gene signature . The hypoxia score was calculated like the CA20 score . Additional clinical information for TCGA breast tumour samples was retrieved from [27] . Normalized gene expression data for 1992 primary breast tumours and 144 normal breast tissue samples from the Molecular Taxonomy of Breast Cancer International Consortium ( METABRIC ) [28] were retrieved from European Genome-Phenome Archive ( EGAC00001000484 ) . Gene expression was profiled with Illumina HT-12 v3 microarrays , with probe-level intensity values being mean-summarised per gene . The CA20 score was calculated as for the TCGA dataset . Clinical information for the same samples was downloaded from cBioPortal ( http://www . cbioportal . org/ ) [79] . Quantification of CA in breast cancer samples was performed as described in [26] . Briefly , formalin-fixed and paraffin-embedded human breast carcinoma samples were consecutively retrieved from the files of the Department of Pathology , Hospital Xeral-Cies , Vigo , Spain . This series comprises 29 luminal A , 3 luminal B , 3 HER2 and 13 basal-like tumours . Some of these samples had already been used in one of our recent studies [26] . The status of the oestrogen receptor ( ER ) , progesterone receptor ( PR ) , epidermal growth factor receptor 2 ( HER2 ) , antigen Ki67 , and the basal markers epidermal growth factor receptor , cytokeratin 5 , cytokeratin 14 , P-cadherin and Vimentin was previously characterized for all tumour cases . According to their immunoprofile , breast tumour samples were classified as luminal A ( ER+ , PR+ , HER2− and Ki67− ) , luminal B ( ER+ , PR+ , HER2 overexpressing or Ki67+ ) , HER2 ( ER- , PR- , HER2 overexpressing ) or basal-like carcinomas ( ER− , PR− , HER2− , basal marker+ ) . Representative tumour areas were carefully selected and at least two tissue cores ( 0 . 6 mm in diameter ) were deposited into a tissue microarray . This study was conducted under the national regulative law for the handling of biological specimens from tumour banks , with samples being exclusively used for research purposes in retrospective studies . Informed consent was obtained from all human participants . For immunofluorescence staining , 3 μm-thick tissue sections were deparaffinised in Clear-Rite-3 ( Thermo Scientific , USA , CA ) and rehydrated using a series of solutions with decreasing concentrations of ethanol . High temperature ( 98 °C , 60 min ) antigenic retrieval with Tris-EDTA pH = 9 . 0 ( LeicaBio systems , UK ) was performed , followed by incubation with UltraVision protein block ( Thermo Scientific ) for 30 min at room temperature . The slides were , afterwards , incubated with mouse anti-GT335 ( 1/800 dilution , Adipogen Ref . AG- 20B-0020-C100 ) and rabbit anti-pericentrin ( 1/250 dilution , Abcam AB4448 ) in UltraAb diluent ( Thermo Scientific ) overnight at 4 °C . The sections were then washed three times , 5 min per wash , with 1× PBS + 0 . 02% Tween20 before a 1 h room temperature incubation with the secondary antibodies , anti-IgG rabbit coupled to Alexa 488 and anti-IgG mouse coupled to Alexa-594 ( Invitrogen ) , diluted at 1/500 in PBS . Finally , sections were washed extensively with 1× PBS + 0 . 02% Tween20 and then counterstained and mounted with Vectashield containing DAPI ( VectorLabs , CA , USA ) . Imaging was performed on a Zeiss Imager Z1 inverted microscope , equipped with an AxioCam MRm camera ( Zeiss ) and ApoTome ( Zeiss ) , using the ×100 1 . 4 NA Oil immersion objective . Images were taken as Z-stacks in a range of 10–14 μm , with a distance between planes of 0 . 3 μm , and were deconvolved with AxioVision 4 . 8 . 1 software ( Zeiss ) . Only the structures positive for GT335 ( centriolar marker ) and pericentrin ( PCM marker ) were analysed and scored . Between 5 and 107 cells were analysed for each patient and cells with more than 4 centrioles were considered as having CA ( S4 Table ) . Normalized gene-level expression and drug sensitivity ( n = 481 compounds ) data for 823 human cancer cell lines from the Cancer Therapeutics Response Portal ( CTRP ) v2 were retrieved from [58] . The CA20 score was calculated as for the aforementioned datasets . Compounds with more than 20% of missing data ( n = 127 ) were removed from the analyses . Area Under the dose-response Curve ( AUC ) was used as the metric of cell line’s drug sensitivity , measured over a 16-point concentration range . Note that lower AUC means higher drug activity . The Connectivity Map ( CMap ) database of signatures [49] was interrogated using CA20 genes as an individual query in the CLUE L1000 tool ( https://clue . io/l1000-query#individual , login required; CA20 genes were used as putative UP-regulated genes ) . For each of the 9 human cancer cell lines profiled within the Touchstone dataset ( PC3 , VCAP , A375 , A549 , HA1E , HCC515 , HT29 , MCF7 and HEPG2 ) , a connectivity score was computed per perturbation ( gene knock-down , gene overexpression , small molecule administration ) [49] , reflecting its effect on the expression of CA20 genes ( except for SASS6 , not profiled in this dataset ) . We calculated an average connectivity score per perturbation by averaging the 9 cell lines’ connectivity scores in order to have a more robust connectivity score that can be used across different cell types and tissues . Two types of perturbations were analysed: 3 , 799 gene knock-downs and 2 , 837 compounds . The Broad compound ID was used to match the 164 compounds tested by CMap and CTRP , so that the results of the analyses of the two datasets could be combined . Normalized gene expression data for adult mouse airway epithelial cells during multiciliogenesis ( triplicates for three different time points: days 0 , 2 and 4 ) was retrieved from [80] ( GEO dataset accession GSE73331 ) . The CA20 score was calculated as for the TCGA dataset . The transcriptomic alterations between non-ciliating mouse tracheal epithelial cells and those undergoing differentiation , through transition to an air-liquid interface culture ( ALI ) , and harvested at four ( ALI+4 ) or twelve ( ALI+12 ) days , were retrieved from [81] . Those probe-level transcriptomic alterations were mean-summarised per gene . Spearman’s correlations were performed using the cor . test R function ( method = ‘‘spearman” ) [82] . The difference between two Spearman’s correlations was tested using the paired . r function from R package psych [83] . Wilcoxon rank-sum tests were performed using the wilcox . test R function [82] . Multiple linear regression modelling was implemented using the lm function from R package limma [84] . Covariate collinearity was tested using the corvif function from [85] , in which all covariates had a variance inflation factor below 2 . All equations and respective statistics are shown in S2 Table . We have normalised the genomic instability covariates using z-scores ( number of standard deviations from the mean ) to account for differences in the prevalence of aneuploidy , mutation burden , CNA and number of clones per cohort . Fligner-Killeen test was implemented using the fligner . test R function [82] . Proportions tests were performed using the prop . test R function [82] . Two-way ANOVA was done using the aov R function [82] . Unsupervised hierarchical clustering of the multiple linear regression results per cancer type was performed using the heatmap . 2 function from R package gplots [86] . Genes ranked according to the knock-down connectivity score were analysed for pathway enrichment using Gene Set Enrichment Analysis [41 , 42] with default parameters . We used a list of 299 cancer driver genes from [43] , a manually curated list of centriole duplication factors ( 93 genes , including 10 from the CA20 signature; S10 Table ) , gene sets retrieved from the KEGG pathway database ( https://www . kegg . jp/ ) and the MSigDB’s Hallmark Gene Sets library [50] . Those with a False Discovery Rate ( FDR ) lower than 5% were considered significant . Dividing patients into two subgroups by CA20 median value , the significance of differences in prognostic was estimated using Kaplan−Meier plots and log-rank tests , per cancer type , through R package survival [87] . To calculate the expected Spearman’s correlation coefficients and p-values used in the quantile-quantile ( Q-Q ) plot ( S14 Fig ) , we permutated 1000 times the drug-sensitivity ( in AUC ) of all compounds across cell lines and , for each permutated dataset , we calculated the respective CA20-AUC Spearman’s correlations . The expected values were obtained by median-summarizing the ranked 1000 permutations’ results .
Centrosome amplification , i . e . an increased number of centrosomes—structures that exist inside cells , is a hallmark of cancer cells and therefore an Achilles' heel for the development of innovative therapies that specifically target tumour cells , sparing healthy ones . To exploit centrosome amplification’s clinical potential , it is crucial to understand its role in cancer development and to identify compounds for its selective targeting . These are challenging tasks due to the technical difficulty of profiling centrosome amplification in cells . In this study , we circumvent those challenges by computationally analysing the expression of 20 genes known to promote centrosome amplification across nearly 10 , 000 tumours of over 30 cancer types , thereby estimating their relative centrosome amplification levels . We found that those genes are indeed highly active in tumours and associated with prognosis in different cancer types . We also show that those genes’ expression is associated with instability in the structure of cancer cells’ chromosomes and identify candidate drugs for selectively targeting those cells . Our work therefore demonstrates the potential of computational analyses of large volumes of cancer molecular and clinical data to elucidate cellular and molecular mechanisms of tumour development and propose novel therapeutic options in oncology .
You are an expert at summarizing long articles. Proceed to summarize the following text: Bartonella species are emerging infectious organisms transmitted by arthropods capable of causing long-lasting infection in mammalian hosts . Among over 30 species described from four continents to date , 15 are known to infect humans , with eight of these capable of infecting dogs as well . B . bacilliformis is the only species described infecting humans in Peru; however , several other Bartonella species were detected in small mammals , bats , ticks , and fleas in that country . The objective of this study was to determine the serological and/or molecular prevalence of Bartonella species in asymptomatic dogs in Peru in order to indirectly evaluate the potential for human exposure to zoonotic Bartonella species . A convenient sample of 219 healthy dogs was obtained from five cities and three villages in Peru . EDTA-blood samples were collected from 205 dogs , whereas serum samples were available from 108 dogs . The EDTA-blood samples were screened by PCR followed by nucleotide sequencing for species identification . Antibodies against B . vinsonii berkhoffii and B . rochalimae were detected by IFA ( cut-off of 1∶64 ) . Bartonella DNA was detected in 21 of the 205 dogs ( 10% ) . Fifteen dogs were infected with B . rochalimae , while six dogs were infected with B . v . berkhoffii genotype III . Seropositivity for B . rochalimae was detected in 67 dogs ( 62% ) , and for B . v . berkhoffii in 43 ( 40% ) of the 108 dogs . Reciprocal titers ≥1∶256 for B . rochalimae were detected in 19% of dogs , and for B . v . berkhoffii in 6 . 5% of dogs . This study identifies for the first time a population of dogs exposed to or infected with zoonotic Bartonella species , suggesting that domestic dogs may be the natural reservoir of these zoonotic organisms . Since dogs are epidemiological sentinels , Peruvian humans may be exposed to infections with B . rochalimae or B . v . berkhoffii . Bartonella species are gram-negative bacteria associated with an increasing array of disease manifestations in humans and animals . They are small , obligate intracellular organisms that adhere and invade erythrocytes and endothelial cells of mammalian hosts , causing long lasting bacteremia [1] , [2] . These zoonotic organisms are mainly transmitted by blood-sucking arthropod vectors , including fleas , body lice , ticks , sandflies and others [1] . To date , 15 species of Bartonella are known to infect humans . Among these , nine species have been documented in dogs , based on culture isolation or DNA-based methods: B . clarridgeiae , B . elizabethae , B . henselae , B . koehlerae , B . quintana , B . rochalimae , B . vinsonii subsp . berkhoffii ( hereafter B . v . berkhoffii ) , B . volans ( including volans-like ) and B . washoensis [3] , [4] . B . bacilliformis is the most frequent species of Bartonella in Peru . Humans are considered the reservoir host and infection in animals has not been reported [1] . No other species of Bartonella have been detected from Peruvian humans to date . However , a new bacteria , B . rochalimae , was isolated in 2007 from an American woman who became sick 16 days after returning from a 3-week trip to Peru , where she received numerous insect bites [5] . Since this first report , B . rochalimae has been detected by culture and/or molecular techniques from three asymptomatic rural dogs in California [6] , one stray dog in Colombia [7] , one sick dog in Greece [8] and one dog with endocarditis in California [9] . In addition , an experimental infection of dogs , cats , and guinea pigs with B . rochalimae demonstrated that only dogs became highly bacteremic without any disease expression , suggesting that dogs could be the natural reservoir for this species [10] . Domestic dogs may represent excellent epidemiological sentinels for Bartonella infection in humans due to several factors: exposure to similar household and recreational environments of humans , potential parasitism by the same vectors , wide diversity of Bartonella species identified in canines , development of a strong organism-specific antibody response to many vector-borne pathogens; and accessibility for safe handling and sample collection [11] , [12] . Therefore , this study aimed to determine the potential for human exposure to zoonotic Bartonella species by defining the serological and molecular prevalence of these pathogens in asymptomatic domestic dogs in various geographic regions of Peru . Additionally , this study sought to define the genetic relationship among Bartonella species , subspecies and strains detected from Peruvian dogs and previously described Bartonella species from humans and other hosts from Peru and other countries . We have demonstrated that Peruvian dogs are exposed to zoonotic Bartonella species , and this study may suggest that the human population is at risk of infection with the same species detected by DNA amplification and genetic characterization . All animals were humanely treated during sample collection . Dogs were manually restrained during blood withdraw , in accordance with the rules of the Medical Ethics and Animal Care Committee of the Universidad Nacional Mayor de San Marcos ( UNMSM ) , Lima , Peru ( no protocol number was assigned ) , which adheres to the Public Health Service ( PHS ) policies of the Office of Laboratory Animal Welfare , Office of Extramural Research , National Institutes of Health ( OLAW-OER-NIH ) , USA ( PHS approved assurance number A5934-01 ) . In addition , this study was also approved by the Medical Ethics and Animal Care Committee of the Universidad Peruana Cayetano Heredia ( UPCH ) , Lima , Peru ( study protocol number 60501 ) , in accordance with the guidelines of the PHS policies , OLAW-OER-NIH , USA ( PHS number A5146-01 ) . The purpose of the study was explained to each individual and they were informed that participation was voluntary and data collected were confidential . Informed consent was obtained from all owners of enrolled dogs in the oral format , because some participants were illiterate . In a pilot study of blood cultures of five dogs where Bartonella DNA was detected ( data not shown ) either lack of isolation of Bartonella or major bacterial contamination was observed . Therefore , the present study only tested canine Peruvian blood samples by DNA-based methods . This was a cross-sectional molecular and serology survey of a convenient sample of dogs from several geographically-diverse areas of Peru naturally exposed to Bartonella species . It was conducted in five cities and three small communities located in four distinct districts of Peru ( Figure 1 ) . Locations were selected as part of a study on canine echinococcosis in rural underserved communities in the Central Peruvian highlands ( Figure 2 ) , as well as another study on canine ehrlichiosis in underserved communities of Lima ( the capital ) and in other regions of the country . San Juan de Miraflores ( 12°9′5″S , 76°58′12″W ) , a district of the Lima province , is located at the center of the coast at an altitude of 141 m ( 463 ft . ) with a population of approximately 336 , 000 people . Paita ( 5°5′6″S , 81°5′58″W ) , in the Piura region , is located 1 , 089 km ( 677 miles ) northwest of Lima , at the sea level with a population of approximately 98 , 000 people . Huaraz ( 9°31′42″S , 77°31′46″W ) , in the Ancash region , is located 414 km ( 257 miles ) northeast of Lima , at an altitude of 3 , 052 m ( 10 , 013 ft . ) with a population of approximately 53 , 000 people . Caraz ( 9°2′54″S , 77°48′54″W ) , also located in the Ancash region , is located 460 km ( 285 miles ) northeast of Lima , at an altitude of 2 , 256 m ( 7 , 402 ft . ) with a population of approximately 20 , 000 people . Ondores ( 11°5′11″S , 76°8′46 . 53″W ) , in the Junín region , is located 230 km ( 143 miles ) east of Lima at an altitude of 4 , 105 m ( 13 , 541 ft . ) with an estimated population of 2 , 571 people . Canchayllo , Pachacayo , and San Juan de Pachacayo are three small villages ( approximately 11°47′56″S , 75°32′37″W ) , also in the Junín region , located approximately 200 km ( 124 miles ) from Lima at an altitude of 3 , 671 m ( 12 , 043 ft . ) with a combined estimated population of 1 , 774 people [13] . From the four regions where this study was conducted , autochthonous cases of human bartonellosis caused by B . bacilliformis were frequently reported , representing over 80% of the all notified human cases in that country between 1950 and 2000 [14] . A convenient sample of 219 domestic dogs was included in this study . In December 2009 , 122 dogs from Ondores , Canchayllo , Pachacayo , and San Juan de Pachacayo were enrolled in this study , whereas in December 2010 , 97 dogs from San Juan de Miraflores , Paita , Huaraz , and Caraz were enrolled . Healthy dogs were volunteered by their owners for the study . Aggressive dogs , small puppies , and dogs exhibiting clinical signs of disease were not included in this study . The number of dogs per city and village is provided in Table 1 . EDTA blood and/or whole-blood samples to obtain serum were aseptically collected from the jugular or cephalic veins , aliquoted , and stored at −20°C until analysis . EDTA blood and serum samples were not available from all dogs enrolled in this study ( Table 1 ) . Concomitant EDTA blood and serum samples were available from 94 dogs . Age information was available from 178 dogs , whereas gender information was documented from 202 dogs . The average age was 2 . 5 years ( SD: 2 . 75 years , 95% CI: 2 . 1–2 . 9 years , median: 1 . 5 years , range: 2 months–14 years ) . Males represented 58 . 4% ( 118/202 ) of the dogs , whereas females represented 41 . 6% ( 84/202 ) . Samples were shipped frozen on dry ice to the College of Veterinary Medicine at Western University of Health Sciences , Pomona , CA , USA ( WesternU ) , under the import permit number 2009-12-105 from the Center for Diseases Control and Prevention , USA . DNA of samples from Ondores , Canchayllo , Pachacayo , and San Juan were purified using a column-based method ( Quick-gDNA Blood MiniPrep , Zymo Research , Irvine , CA , USA ) at WesternU . DNA of samples from San Juan de Miraflores , Paita , Huaraz , and Caraz were purified at UPCH using a phenol-chloroform method . A conventional PCR assay designed to amplify a fragment ( approximately 700 bp ) of the 16S–23S ribosomal RNA ( rRNA ) intergenic transcribed spacer ( ITS ) of Bartonella species was performed as previously described [15] . The negative control consisted of molecular-grade water . In order to prevent contamination , sample extraction , reaction setup , PCR amplification , and amplicon detection were performed in separate areas . Quantified genome equivalents ( GE ) of the DNA from B . quintana strain ND1 ( DQ648598 ) [16] were serially diluted 10-fold from 1 , 000 , 000 to 1 GE/uL and used as positive control to determine the limit of detection of the PCR assay . This conventional ITS PCR assay was able to detect 50 , 25 , 10 and 5 GE of B . quintana per reaction tube 100% of the time ( 10/10 times ) . Canine samples with DNA amplification at the expected size were further genetically characterized by the amplification and DNA sequencing of a fragment ( approximately 610 bp ) of the heat-shock protein gene ( groEL ) as previously described [17] . Amplicons were purified from PCR products or from specific bands on the gel ( MiniElute kit , Qiagen , Valencia , CA , USA ) and sequenced with a fluorescence-based automated sequencing system ( Eurofins MWG Operon , Huntsville , AL , USA ) . Chromatogram evaluation , primer deletion and sequence alignment were performed ( Vector NTI Suite 10 . 1 , Invitrogen Corp . , Carlsbad , CA , USA ) . Bacteria species and strain were defined by comparing similarities with other sequences deposited in the GenBank database using BLAST [18] . Phylogenetic analysis of the data was carried out by using the Maximum Likelihood method based on the Kimura 2-parameter model [19] with the MEGA5 software [20] . Bootstrap replicates were performed to estimate the node reliability of the phylogenetic trees , with values obtained from 1 , 000 randomly selected samples of the aligned sequence data . DNA amplification and sequencing analysis were performed at the WesternU . Antibodies against Bartonella vinsonii subsp . berkhoffii genotype I ( ATCC 51672 [21] ) and B . rochalimae ( isolate Hoopa Fox8 , University of California , Davis , CA , USA [9] ) were detected using an indirect immunofluorescent antibody assay ( IFA ) , as previously described [6] . All IFA slides were prepared the same way by infecting Vero cells with one of the strains listed above . Samples with detectable fluorescence at a dilution of 1∶64 were considered positive , with endpoint titers being determined thereafter . Negative and positive control samples were included on each slide . Serology assays were performed at the Veterinary Public Health Laboratory , School of Veterinary Medicine , University of California , Davis , CA , USA . Molecular prevalence of Bartonella species and seroprevalence against B . v . berkhoffii and B . rochalimae are described as absolute frequencies , percentages and 95% confidence intervals ( computed using score method ) . Contingency table analyses were performed to evaluate association with age or gender and any difference in prevalence by geographical region by using the Fisher's exact test for 2×2 comparisons and the Fisher-Freeman-Halton test for row×column comparisons . Contingency table analysis was also performed to evaluate the correlation between serology and PCR results by Cohen's Kappa agreement test . A p-value<0 . 05 was considered statistically significant . Data analysis was performed using JMP Pro 10 ( SAS Institute Inc . , Cary , NC , USA ) . Multiple DNA sequence alignment and phylogenetic analysis of ITS region from Peruvian dogs ( Figure 5 ) demonstrated that DNA sequences from 15 dogs were 100% homologous ( 534/534 bp ) to the original B . rochalimae isolate obtained from the sick woman ( ATCC isolate BAA-1498 [5] ) . This ITS sequence from Peruvian dogs ( HQ185696 ) was also 100% homologous ( 534/534 bp ) to a previously reported DNA sequence from a flea ( Pulex sp . ) collected in 1998 from a person in the city of Cusco , Peru [22] , but different ( 532/534 bp ) from DNA sequences from B . rochalimae detected from fleas ( Pulex irritans ) from red foxes ( Vulpes vulpes ) in Andalusia , Spain [23] and from dogs and gray foxes ( Urocyon cinereoargenteus ) in northern California ( 531/534 bp ) [9] . A 621 bp sequence of ITS region of B . v . berkhoffii was obtained from six Peruvian dogs . Phylogenetic analysis indicated a high homology with DNA sequences from genotype III ( Figure 5 ) . Similar to B . rochalimae sequences from this study , the DNA sequence of B . v . berkhoffii from Peruvian dogs ( HQ185695 ) was 99 . 6% homologous ( 562/564 bp ) to a previously reported DNA sequence from another flea ( Pulex sp . ) collected in 1998 from another human in Cusco , Peru [22] . B . v . berkhoffii sequence from Peruvian dogs was 99 . 7% homologous ( 619/621 bp ) to B . v . berkhoffii genotype III isolate obtained from a human with endocarditis from the United Kingdom [24] , and a dog with endocarditis from the USA [25] . A 614 bp sequence of groEL gene was obtained from eight out of 15 dogs infected with B . rochalimae ( JX846497 ) being 100% homologous ( 565/565 bp ) to the original B . rochalimae isolate obtained from the sick woman [5] ( Figure 6 ) . The groEL sequence from Peruvian dogs was also 96 . 8% homologous ( 547/565 bp ) to a Bartonella sp . sequence detected from rat fleas from Egypt [26] and it was 96 . 5% homologous ( 545/565 bp ) to a Bartonella sp . isolated from an American red squirrel [27] . Other groEL sequences of B . rochalimae were not available in GenBank database at the time of writing . A 614 bp sequence of groEL gene was obtained from two out of the six dogs infected with B . v . berkhoffii ( JX846496 ) being 97 . 2% homologous ( 549/565 bp ) to B . vinsonii subsp . vinsonii isolate obtained from a vole from Canada ( strain Baker , ATCC VR-152 ) , and 96 . 3% homologous ( 544/565 bp ) to B . arupensis isolate obtained from an American cattle rancher with bacteremia ( ATCC 700727 [28] ) . However , the canine Peruvian sequence of B . v . berkhoffii was only 94 . 3% homologous to B . v . berkhoffii genotype I isolated from a dog with endocarditis in the USA ( ATCC 51672 [21] ) . Further comparison with genotypes II , III , and IV was not possible due to lack of those groEL DNA sequences in GenBank database at the time of writing . This is the first report of molecular detection of Bartonella species in dogs in Peru , documenting the largest number of domestic dogs infected with or exposed to zoonotic Bartonella species to date . Our study documented that over half of the canine population tested was exposed to B . rochalimae with 1 in every 14 asymptomatic dogs being infected with this zoonotic organism . It is suggested that the natural reservoirs of B . rochalimae are gray foxes , coyotes and raccoons in the United States [6] , [29] , red foxes in Europe [6] , [29] , [30] , and possibly rodents in Asia [31] . Experimentally , dogs are more permissive than cats or guinea pigs to infection with B . rochalimae [10]; however , prior to this publication , only a very limited number of naturally-infected dogs were reported [6]–[9] . A significant number of seropositive dogs was detected in this study , with one in five dogs presenting reciprocal titers ≥1∶256 , suggesting that these companion animals are frequently exposed to this zoonotic organism , and indirectly suggests that the human population may be at risk of infection as well . DNA sequencing of two distinct regions demonstrated that B . rochalimae from Peruvian dogs was 100% similar to the original isolate of B . rochalimae from a sick woman , supporting the zoonotic potential of this organism in Peruvian dogs . The majority of dogs enrolled in this study were located in underserved rural communities or in low-income areas of cities ( Figure 2 ) , where ectoparasite control and access to veterinary care are very limited . Collective , the current data suggest the life cycle of this zoonotic pathogen is intrinsically related to dogs , vectors , and humans and may be naturally taking place in the study areas . Of relevance is the fact that all seropositive and/or bacteremic dogs for B . rochalimae in our study were asymptomatic , which was previously demonstrated in an experimental infection study in dogs [10] . The lack of clinical signs poses an epidemiological challenge since these dogs may serve as reservoirs for extended periods of time without the need to seek veterinary care , which would provide the opportunity for diagnostic investigation of this and other vector-borne zoonotic diseases . Additionally , approximately 40% of the dogs in this study were exposed to B . v . berkhoffii , with 6 dogs infected with this pathogen . In contrast , antibodies to B . v . berkhoffii are infrequently detected ( <4% ) in sick referral or healthy ( <1% ) dogs in the USA [1] . However , over three quarter of the Peruvian dogs seropositive to this organism had antibody titers between 1∶64 and 1∶128 . Possible explanations would include past exposure to B . v . berkhoffii , modulation of the host immunity by the pathogen , or cross-reactivity with other bacteria . B . v . berkhoffii has co-evolved with domestic dogs and wild canids , and we have previously reported that only 50% of dogs infected with B . v . berkhoffii were seroreactive by IFA testing [32] . Therefore , limited antibody response is expected in dogs infected or exposed to this pathogen . Clinical manifestations in immunocompetent dogs and humans infected with this pathogen have been reported , including fatigue , headache , arthritis , muscle pain , neurologic or neurocognitive abnormalities , endocarditis , or epithelioid hemangioendothelioma in humans [24] , [33]–[36] , and cavitary effusions ( pleural effusion , ascitis ) , endocarditis , cardiac arrhythmias , polyarthritis , anterior uveitis , meningoencephalitis and hemangiopericytoma in dogs [1] , [34] , [37] , [38] . The fact that only asymptomatic dogs infected with B . v . berkhoffii were detected in this study is a consequence of the inclusion criteria used , and does not rule out the possibility that sick dogs or humans in Peru may be infected with this pathogen . The genotype of B . v . berkhoffii documented in the six Peruvian dogs in this study was closely related to genotype III originally detected from an endocarditis case in a human in the United Kingdom and in a dog in the USA [24] , [25] , but differed from the genotype II , which is the most frequent of the four genotypes to be found in dogs and humans in North America [1] . Unfortunately , IFA testing for other genotypes was not available . Lack of concordance between serology and PCR results for Bartonella species , as detected in this study , has been previously described in humans and animals , and it is suggested to be associated with an anergic immune response of the host based on the “stealth” properties of Bartonella species , or substantial antigenic variation among various Bartonella strains , resulting in false-negative IFA assay results [2] , [39] . In our study , 39 . 8% of dogs tested by IFA were seroreactive to both B . v . berkhoffii and B . rochalimae . Similar results were recently detected from gray foxes in Texas , where 54 . 5% of 132 foxes were seroreactive to antigens of B . v . berkhoffii and B . clarridgeiae , which is a suitable antigen marker for B . rochalimae detection [40] . Cross reactivity between B . v . berkhoffii and B . rochalimae cannot be excluded; however , in an experimental infection with these Bartonella species in two dogs , no cross reactivity occurred between host anti-B . v . berkhoffii or anti-B . rochalimae antibodies and antigens from other Bartonella species by IFA ( titers ≤1∶16 ) [10] . But when the dog previously exposed to B . v . berkhoffii was later experimentally inoculated with B . rochalimae , cross reactivity to other Bartonella species was documented [10] . These and other limited results suggest that infection or re-infection with multiple species of Bartonella could substantially increase the serological cross reactivity between species [10] , , which may have occurred within the Peruvian dogs enrolled in this study . However , natural exposure to both pathogens cannot be excluded , as co-infection with more than one species of Bartonella species has been documented in several animal species , including dogs and humans [1] , [15] . Infections with B . rochalimae or B . v . berkhoffii have never been described in humans or animals from Peru , even though other known and unknown species of Bartonella have been described from small mammals , bats , body lice , fleas and ticks from Peru [22] , [42]–[45] . In our study , we documented 10 . 3% of dogs infected with B . rochalimae and 7 . 7% of dogs infected with B . v . berkhoffii from Caraz and Huaraz . In that same region , 127 human cases of bartonellosis were documented in 2008 based on serology methods , with the isolation of B . bacilliformis from 11 human specimens [46] . Since humans in Peru are not tested for antibodies against B . rochalimae or B . v . berkhoffii , their exposure status to these pathogens is unknown . In addition , culture methods used for isolation of Bartonella in Peru may bias the detection of B . bacilliformis since culture media and temperature used for isolation of B . bacilliformis ( 28°C ) are different from B . rochalimae and B . v . berkhoffii isolation ( 35°C ) . Growth differences between isolates of the Bartonella genus have been shown in vitro , where culture medium and temperature differences as low as only 2°C can select the growth of a given species [47] . Even though B . bacilliformis has been a well-established human pathogen in Peru for over a century , our data suggest that other Bartonella species may also cause illness in humans in Peru . The fact that the DNA sequences of B . rochalimae and B . v . berkhoffii obtained from Peruvian dogs matched 100% and 99 . 6% , respectively , with Bartonella sequences obtained from human fleas ( Pulex irritans ) previously collected from schoolchildren and adults in Peru [22] suggests that human fleas could be the vector for both Bartonella species for dogs and humans in that country . The human flea is an aggressive and promiscuous ectoparasite found on a wide range of hosts , including domestic dogs [48] , [49] . In addition , B . rochalimae was also recently detected in a brown dog tick ( Rhipicephalus sanguineus ) from a dog in the province of Callao [45] , located only 25 km ( 15 . 5 miles ) northwest of San Juan de Miraflores , the site where the largest number of dogs infected with B . rochalimae was detected in our study . Both Callao and San Juan de Miraflores are suburbs of the country's capital ( Lima ) , where human and canine population densities and social and economic conditions may favor the multiplication and dissemination of vectors , increasing the risk of human infection . The role of ticks as vector for B . rochalimae or B . v . berkhoffii in dogs or humans is still unclear . However , clinical studies have associated canine infection with B . v . berkhoffii and the presence of R . sanguineus ticks [50] . R . sanguineus is the most frequent urban tick in tropical regions , and its ability to feed on humans have been documented , including its role in a recent outbreak of Rocky Mountain Spotted Fever in humans in the United States [51] . Cat bites and scratches can transmit B . henselae or B . clarridgeiae to humans [3] . However , it is still unclear if dogs are capable to transmit Bartonella species to humans using similar ways . Bartonella DNA was detected in dog saliva [52] , and limited case reports have suggested possible direct transmission to humans [53] . In addition , professionals with frequent animal and arthropod exposure , such as veterinarians , veterinary assistants , cattle ranchers and biologists appear to have an occupational risk for Bartonella infection [1] . Therefore , the populations in contact with bacteremic dogs should be instructed to prevent arthropod bites , arthropod feces , dog bites , and scratches and direct contact with bodily fluids from infected animals . In conclusion , our results expand the current knowledge about B . rochalimae and B . v . berkhoffii , suggesting that asymptomatic domestic dogs in Peru are exposed to these zoonotic pathogens and may be their natural reservoir . Consequently , our results indirectly suggest that the local human population may be exposed to infections with B . rochalimae or B . v . berkhoffii and support public health actions for vector control in dogs and humans .
Bartonella are bacteria transmitted by fleas , ticks , sandflies and other insects capable of infecting humans , domestic animals , livestock and wildlife , including marine mammals . In humans , they cause diseases such as trench fever , cat scratch disease , endocarditis , fever of unknown origin and have been recently associated with neurologic and neurocognitive abnormalities . Bartonella bacilliformis was first described in Peru in 1913 , and it has never been detected in animals . Despite the fact that 14 other Bartonella species have been detected infecting humans around the world , no other Bartonella species has yet been described from Peruvian humans or domestic animals . We documented a significant number of healthy domestic dogs in Peru infected or exposed to two Bartonella species ( B . rochalimae and B . vinsonii subsp . berkhoffii ) , which are known to cause disease in humans . These same species were previously detected in human fleas and dog ticks in Peru , suggesting that vector transmission between dogs and humans may be possible . While the role of dogs as a source of Bartonella species for direct transmission to humans is not well understood , preventive measures including vector control in dogs should be implemented to prevent human infection .
You are an expert at summarizing long articles. Proceed to summarize the following text: We present the AGEMAP ( Atlas of Gene Expression in Mouse Aging Project ) gene expression database , which is a resource that catalogs changes in gene expression as a function of age in mice . The AGEMAP database includes expression changes for 8 , 932 genes in 16 tissues as a function of age . We found great heterogeneity in the amount of transcriptional changes with age in different tissues . Some tissues displayed large transcriptional differences in old mice , suggesting that these tissues may contribute strongly to organismal decline . Other tissues showed few or no changes in expression with age , indicating strong levels of homeostasis throughout life . Based on the pattern of age-related transcriptional changes , we found that tissues could be classified into one of three aging processes: ( 1 ) a pattern common to neural tissues , ( 2 ) a pattern for vascular tissues , and ( 3 ) a pattern for steroid-responsive tissues . We observed that different tissues age in a coordinated fashion in individual mice , such that certain mice exhibit rapid aging , whereas others exhibit slow aging for multiple tissues . Finally , we compared the transcriptional profiles for aging in mice to those from humans , flies , and worms . We found that genes involved in the electron transport chain show common age regulation in all four species , indicating that these genes may be exceptionally good markers of aging . However , we saw no overall correlation of age regulation between mice and humans , suggesting that aging processes in mice and humans may be fundamentally different . Aging is characterized by the progressive functional decline of multiple organs and tissues , eventually culminating in death . A long sought after goal has been to identify biomarkers of aging to characterize aging . Biomarkers of aging may indeed be tightly linked to aging processes , and hence may provide insight regarding mechanisms underlying aging . Changes in cellular morphology or cell type number are quantifiable traits that can be correlated with physiological age . For example , age-related changes in specific tissues include hair whitening , muscle atrophy , wrinkling of skin , thymic involution , and glomerulosclerosis of the kidney . However , these age-related tissue changes do not pinpoint specific genes or molecular pathways involved in distinct aging processes . Further , it is difficult to determine whether age-related changes in different tissues are due to common or distinct molecular causes . At the molecular level , an attractive approach is to use changes in gene expression as biomarkers of aging . DNA arrays can be used to scan a large fraction of the genome for genes that change expression with age . Identification of age-regulated genes provides key insights regarding aging . In mice , at least 19 studies have profiled changes in gene expression with age in nine separate mouse tissues . Many of these studies find that similar classes of genes are regulated with age in different tissues . For instance , genes involved in the inflammatory response and heat shock factors have been found to increase expression levels with age in mouse brain , muscle , and heart [1–5] . Other pathways commonly found to be associated with aging in mice include metabolic energy pathways [1 , 6] , extracellular matrix genes [5 , 7] , and degradation pathways [1 , 4 , 5 , 8] . These studies also show that feeding mice under conditions of caloric restriction attenuates many age-related changes in gene expression . These DNA microarray studies were performed using different experimental protocols , with different types of gene arrays ( e . g . , Affymetrix GeneChips or DNA microarrays ) , and by different labs . Because of these differences in methodology , most studies in mouse aging have not compared their results to each other . In this study , we present AGEMAP ( Atlas of Gene Expression in Mouse Aging Project ) , which is a highly standardized study of gene expression changes as a function of age in mice . AGEMAP includes expression data for 8 , 932 genes from 16 tissues taken from the same set of C57BL/6 mice using an identical gene array platform and experimental protocol . Because the gene array experiments were analyzed at the same time and in the same way , we can compare gene expression profiles for aging between different tissues in order to identify common aging biomarkers . Previous studies have shown that the overall pattern of expression from age-regulated genes ( a gene expression profile ) can be used as a biomarker of aging . For instance , a transcriptional profile of aging in human kidneys correlated strongly with a histological measure of kidney function , and thus could be used to predict physiological age [9] . Similarly , in human muscle , a gene expression profile of aging was found to correlate with Type II muscle fiber atrophy , a measure of muscular age [10] . In mice , changes in gene expression levels associated with aging were seen to be slowed by caloric restriction , a known aging intervention [1 , 2] . Expression profiles can be used to compare aging in different tissues . In humans , there is extensive overlap in gene expression profiles for aging between different parts of the same tissue . Specifically , similar aging transcriptional profiles were found between the cortex and medulla of the kidney and between different sections of the prefrontal cortex of the brain [9 , 11] . In contrast , there is little in common between aging expression profiles across different human tissues . However , a small amount of overlap was found involving six genetic pathways that showed similar age regulation among the kidney , skeletal muscle , and brain [10] . These six common age-regulated genetic pathways may be closely tied to basic processes of human aging . Lessons learned about the role of these pathways in aging may reveal not only how tissues grow old , but also how aging results in pathology , disease , and loss of function in elderly individuals . Expression profiles of aging for one species can be compared to that of another species , thereby showing which age-related changes in expression are unique to one species and which are shared by different animals [12] . Genes and gene pathways that appear to be age regulated in diverse species may be unavoidably linked with major aging processes , making them exceptionally strong candidates for biomarkers of aging . Comparisons of transcript profiles of aging among human , mouse , fly , and worm have shown an overall decrease in the expression level of the electron transport chain genes in these distantly related species [10 , 13] . The mitochondrial electron transport chain is the primary source for free radical production , which can result in cellular damage . Alteration in the expression of the electron transport chain with age likely affects levels of oxidative damage . In this work , we present an analysis of data from AGEMAP , a database of changes in gene expression with respect to age in mice , which can be used as a resource for gerontological research . We generated mRNA transcript profiles of aging involving 8 , 932 genes and 16 mouse tissues at four times during aging . The raw data can be downloaded and reanalyzed by researchers interested in specific tissues or genes . We show that the entire gene expression profile of aging can be analyzed as a molecular phenotype of aging . First , we used the number of age-regulated genes in different tissues to estimate the magnitude of age-related decline among tissues and found that there were great differences in the levels of age-related change in different tissues . Some tissues showed little or no changes in expression ( e . g . , liver and striatum ) , whereas other tissues showed a great deal of transcriptional change with aging ( e . g . , thymus ) . Second , the aging expression profile for different tissues showed three main patterns for murine aging ( neural , vascular , and steroid-responsive ) . Finally , we compared the expression profiles for mouse aging to those from human aging . Although there are a small number of genetic pathways that age similarly between the two organisms , we found no overall correlation in age-related transcriptional changes between mouse and human . We profiled the effects of aging on gene expression in different tissues dissected from C57BL/6 mice . Mice were of ages 1 , 6 , 16 , and 24 mo , with ten mice per age cohort ( five mice of each sex ) . We dissected 16 tissues from each mouse: the cerebellum , cerebrum , striatum , hippocampus , spinal cord , adrenal glands , heart , lung , liver , kidney , muscle , spleen , thymus , bone marrow , eye , and gonads . These particular tissues were surgically accessible and relatively easy to harvest in the limited amount of time available before the onset of tissue ischemia . We isolated mRNA from each tissue sample and generated radiolabeled cDNA , which was then hybridized to two filter membranes containing a total of 16 , 896 cDNA clones corresponding to 8 , 932 genes . Data from 23 arrays were eliminated for quality-control reasons . In total , we generated expression data from 617 gene arrays including 16 tissues and four ages . The full set of expression data from these experiments can be found in Table S1 , and investigators can display age-related expression data for specific genes of interest at http://cmgm . stanford . edu/~kimlab/aging_mouse . First , we wanted to identify genes that showed age-related changes in each tissue . We employed a multiple regression model taking age and sex into account in order to calculate the slope of expression with age for each of the 8 , 932 genes on the gene array ( Materials and Methods ) . The 16 tissues showed from 0 to 346 age-regulated genes ( p < 0 . 001 ) ( Table S2 ) . We used two criteria to determine whether the amount of age-regulation observed for each tissue was statistically significant . First , we restricted attention to tissues with more than nine significantly age-regulated genes , because we would expect to see nine genes by chance alone at p < 0 . 001 . Second , we made sure that the actual number of identified genes was unusually large compared to what we would see if there were no age-regulated genes , while accounting for the possibility of correlations existing between the expression levels of different genes . To do this , we permuted the ages of the male mice and those of the female mice and repeated the tests . In 1 , 000 independent random permutations of this kind , we counted how often we obtained more significant genes in the permuted dataset than in the original one . We discarded data from tissues if more than 10% of permutations gave rise to a greater number of apparently age related genes than we found for a given tissue . Of the 16 tissues , six were eliminated by the first test , one more by the second , and nine showed statistically significant age regulation ( Tables 1 and S2 ) . One of the tissues ( gonads ) consists of 20 ovary samples and 20 testes samples . When the ovary and testes samples were analyzed separately , we did not observe a significant number of age-regulated genes , possibly because the sample size is smaller than the other tissues . However , surprisingly , when we grouped the ovaries and testes together , we found a relatively high number of age-regulated genes ( 45 genes , p < 0 . 001 ) , indicating that these germline tissues share a common underlying aging profile . Age-related expression changes are relatively small in magnitude; most genes increase or decrease expression less than 2-fold over the course of life . The remaining seven tissues showed little or no age-dependent expression changes . We also explored the impact of aging on the overall expression levels of gene sets and molecular pathways within mouse tissues . We used a modified gene set enrichment analysis [14 , 15] , which measures the cumulative effect of small but consistent changes in expression levels of genes within a gene set . This method is very sensitive , as it can show whether overall age regulation of a gene set is statistically significant even when expression changes of individual genes that comprise the gene set are not . We assayed a collection of 401 gene sets consisting of between ten and 200 genes defined by the Gene Ontology Consortium for age regulation ( Table S3 ) [16] . We used gene set enrichment analysis to assay the age regulation of these 401 gene sets in each of the 16 mouse tissues . Our version of gene set enrichment analysis employs a Van der Waerden statistic to determine whether a gene set is skewed towards an overall increasing or decreasing expression level with age , compared to a random , identically sized set of genes ( p < 0 . 001 ) . Our analysis makes 401 hypotheses , one for each gene set . For each hypothesis , we get a van der Waerden test statistic and judge its significance in a bootstrap analysis . This analysis models the mice as randomly sampled . The gene sets themselves are treated as fixed , and the bootstrap analysis assigns p-values based on resampling of mice without assuming that the gene set was a random sample . Our van der Waerden statistic is a special case of the test statistic advocated by Newton et al . [17] . The thymus showed the greatest number of age-regulated gene sets ( 51 age-regulated gene sets ) , whereas the liver and striatum showed none . Of 16 mouse tissues , ten exhibit more age-regulated gene sets than would be expected by chance alone; nine of these tissues also have a statistically significant number of age-regulated genes ( Tables 1 and S4 ) . Only nine of the 16 tissues showed significant levels of age-related gene expression changes . To test whether different levels of age regulation reflect different quality of expression data from the gene filters , we calculated the average Pearson correlation for all genes between biological repeats . We found no association between experimental variability and the number of age-regulated genes found in different tissues ( Table 1 ) , suggesting that the difference in number of age-regulated genes in each tissue is due to differences in the tissues themselves rather than differences in technical reproducibility . Thus , tissues may not all age at the same rate . Some tissues may age rapidly , while others remain relatively constant throughout life . For instance , the histological appearance of the liver remains relatively constant throughout life and likewise we found no discernable changes in gene expression when assayed by cDNA arrays . Conversely , sections of the thymus show obvious histological changes ( thymic involution ) over the course of life [18] , and there are a large number of age-regulated genes in this tissue . Thus , the number of age-regulated genes in a tissue may be a measure of the amount of change in that tissue during aging . We were interested in finding which genes and gene sets are similarly age-regulated in multiple mouse tissues , which could potentially identify core cell biological pathways involved in common aging mechanisms . We used empirical meta-analysis to find genes , as well as gene sets that are age regulated across multiple tissues . Empirical meta-analysis combines single p-values for age regulation of a gene or gene set from each tissue into an overall p-value representing age regulation in all of the tissues ( Materials and Methods ) . We identified 314 genes that were significantly age regulated in multiple tissues ( p < 0 . 001 ) and in which no single tissue contributed more than 50% of the test statistic's value ( Table S5 ) . Given 8 , 932 genes , we would expect nine genes or less to appear age regulated in multiple tissues at this threshold . We also investigated the false discovery distribution by permuting ages for all samples 1 , 000 times and recalculating empirical meta-analysis p-values , maintaining permuted ages for the same mouse within every tissue . We found that the average number of genes returned by permutation in this simulation was approximately nine genes , as expected . The distribution of the number of falsely discovered genes has a long tail , but even so , only three of the 1 , 000 simulations generated more than 314 genes . This result indicates that it is highly unlikely that we would obtain 314 common age-regulated genes by sampling error . We examined 401 gene sets defined by the Gene Ontology Consortium [16] to find those that exhibit age regulation in multiple tissues . We identified 84 gene sets ( p < 0 . 001 ) that contained genes in which the overall age-related slopes trended either consistently up or down with age in multiple tissues ( Table S6 ) . At this threshold for significance , we would expect less than one gene set to appear age related by chance . To determine which tissues were aging similarly , we used the commonly age-regulated genes and gene sets to group tissues by unsupervised hierarchical clustering ( Figure 1 ) [19] . Clustering tissues based on age regulation of genes as well as clustering based on gene sets showed three predominant patterns . The cerebellum and spinal cord clustered distinctly together as a neural group . The heart , lung , and spleen formed a second group of highly vascular tissues . The thymus and gonads were strongly grouped together in a steroid-responsive group . The adrenal glands clustered with this third group when clustering by genes , but not when clustering by gene sets . Finally , the eye remained an outlier; although it is considered neural tissue , it did not group with either the cerebellum or the spinal cord . In summary , these results identify three distinct modes of aging for different mouse tissues . Not all tissues age the same way , and different tissues may show different aging transcriptional profiles because they are subject to different forms of age-related stress . To characterize the differences between the three different modes of tissue aging , we searched for gene sets that were strongly age regulated within one tissue group but not others . For each of the tissue groups , we first selected gene sets that showed strong age regulation in that tissue group ( statistical significance at p < 0 . 001 , and practical significance via a Van der Waerden Z-value with |Z| > 1 ) , and then screened for those that showed much weaker age regulation in the other two tissues groups ( p > 0 . 5 ) ( Figure 2 ) . Genes in the cytosolic ribosome set stand out as the strongest age-related difference in expression between tissues . This gene set shows an increasing trend in expression in the neural group , but decreasing trends in the vascular and steroid-responsive groups . Increasing expression of ribosomal genes in neural tissues is curious , as rates of protein synthesis are known to decrease with age [20] . In addition to ribosomal gene expression , the neural aging group is characterized by age-regulated expression of genes encoding nuclear hormone receptors , NADH reductase enzymes , and adherens complex proteins . The nuclear hormone receptor gene set shows a decreasing trend in expression with age in neural tissues , which may be significant because age-related changes in expression of these transcription factors may also change expression in a battery of downstream genes . NADH reductase genes play an important role in the electron transport chain , and increased expression of these genes in neural tissues in old age may affect levels of oxidative damage . Neural tissues show increased expression trends of genes in the adherens junction gene set , which may affect communication between neurons in old age . The steroid-responsive aging group shows decreased expression of genes in the protein targeting gene set . Genes in this gene set play an important role in protein secretion , suggesting a role for changes in hormone and growth factor expression in aging of steroid-responsive tissues . Besides clustering tissues based on the expression profile of commonly age-regulated pathways , we also compared the degree of overlap in age regulation between each tissue with every other tissue . For each of the nine age-regulated mouse tissues , we selected genes that decrease expression with age , and genes that increase expression with age ( p < 0 . 01 ) . We calculated the extent of the overlap of age-regulated genes between all tissue pairings using Fisher's exact test for independence ( Materials and Methods ) . We identified seven pairs of tissues that show a high degree of overlap ( p < 0 . 001 ) ( Table 2 ) . These seven pairs form three groups of tissues that exactly match our tissue clustering results in Figure 1 . Specifically , the spinal cord and cerebellum exhibit high levels of aging similarity . The heart , lung , and spleen show a great deal of overlap in age regulation with one another , as do the adrenal glands , thymus , and gonads . Interestingly , the eye shows a strong overlap of age-regulated genes with the heart , although the two tissues did not cluster together in Figure 1 . Individuals age at different rates , even when they have nearly identical genetic backgrounds and are grown under similar conditions . Different rates of aging can be seen by measuring the lifespan of individuals , the morphology and physiology of different tissues , or expression profiles of age-related genes . To what extent is the aging of one tissue coordinated with other tissues in the same individual ? For instance , one might expect different organs in the cardiovascular system ( heart and lung ) to age in step with one another , but aging of the brain may not be tightly linked to aging of the heart . To test for coordination of aging among different tissues , we first developed a score for apparent age of each tissue in mice from the same age cohort based on the expression profile for that tissue . We then determined whether the apparent age of one tissue was correlated with the apparent age of another tissue in the same mouse . To calculate an aging score for a specific tissue , we first use Z-normalized expression data from mice of every age to select the 50 genes that increase expression the most and the 50 genes that decrease expression the most with age in that tissue ( Materials and Methods ) . The expression levels of these 100 genes are used to calculate the apparent age of that tissue in each individual mouse . For each tissue , we summed the relative expression levels for these 100 genes , generating a single value describing its apparent age for that mouse ( Materials and Methods ) . We perform this operation for all nine tissues that show age-related changes in every mouse . Then , for every combination of age cohort and tissue , we assign each of the mouse samples a ranked fractional score on the basis of their age-regulated gene expression , with 1 . 00 representing the mouse with the oldest apparent age and 0 . 00 representing the youngest ( Materials and Methods ) . For each age cohort , we were interested in seeing whether there was coordinate aging among tissues . If aging is coordinated among tissues , then a mouse may contain many tissues with high apparent ages ( represented by high fractional scores ) and thus generate a very high overall aging score . Conversely , another mouse may contain many young tissues and generate a very small overall aging score . If aging is not coordinated among tissues , all mice should show a mix of old and young tissues , and thus have an overall aging score close to the average for all mice . We summed the fractional scores of all tissue for a given mouse within an age cohort , resulting in ten overall scores . Coordinate aging among tissues within an age cohort should generate a set of ten overall scores that shows a high variance , whereas independent aging should show a variance similar to those generated by random permutation . For each age cohort , we compared the variance of the ten overall scores to variances generated by permutation to calculate a p-value for coordinated aging ( Materials and Methods ) . At 6 , 16 , and 24 mo of age , we found that there is coordinate aging of different tissues ( p < 0 . 05 , Figure 3 ) . Specifically , the variance of the sums of fractional scores was found to be significantly larger than the values generated from permuted data . The group of 24-mo-old mice showed the most coordination of aging among tissues . We wanted to find genes and gene sets that show similar age regulation in different species: M . musculus , H . sapiens , D . melanogaster , and C . elegans . To compare age regulation in similar microarray studies among these four species , we first identified orthologous genes ( Table S7 ) , and then we identified equivalent gene sets ( GO gene groups comprised of between ten and 200 orthologous genes; Table S8 ) . For each orthologous gene and gene set , we first used empirical meta-analysis to screen for age regulation in the nine mouse tissues shown in Figure 1 ( p < 0 . 01 ) . For each of these genes or gene sets , we next determined whether it also showed a similar pattern of age regulation in multiple human tissues ( empirical meta-analysis; p < 0 . 01 ) , using DNA microarray data on aging from human muscle [10] , kidney [9] , and brain [21] . Finally , we determined whether there were common patterns of aging in transcriptional profiles of aging in D . melanogaster [22] and C . elegans ( Jiang et al . , unpublished data ) for orthologous genes ( linear regression; p < 0 . 001 ) or gene sets ( Gene Set Enrichment Analysis; p < 0 . 001 ) . This search yielded 22 genes and 17 gene sets that are commonly age regulated in mice and humans ( Figure 4; Tables S9 and S10 ) . Of these , only one gene set ( the mitochondrial electron transport chain gene set ) was commonly age regulated in all four species , showing an overall decreasing trend in expression in old age ( p < 1 × 10−13 overall for four species ) . The mitochondrial electron transport chain is the primary source of free radicals in cells , which are highly reactive molecules that damage cellular components such as proteins and DNA . Overall reduction in expression of components of the electron transport chain may reduce levels of free radical damage in old age . One other gene set ( the lysosomal gene set ) showed a common increasing trend in expression with age in humans , mice , and flies , but not worms . The lysosome is involved in degradation of extracellular proteins , and increased expression of lysosomal genes may reflect increased turnover of damaged cell surface proteins in old age . Fifteen gene pathways show common age regulation in humans and mice but not flies or worms . The peroxisome gene set shows a decreasing trend in expression with age . The peroxisome is important for detoxification of foreign compounds and also produces hydrogen peroxide , a known precursor of free radicals . Decreasing expression of genes in the peroxisome pathway might lead to decreased tolerance toward toxins and decreased levels of oxidative damage in old age . The inflammatory response and cytokine activity gene sets show an increasing trend in expression with age , suggesting that inflammation becomes more active with age . Finally , genes involved in cell cycle arrest tend to increase expression with age , implying a tendency for cells to lose the ability to proliferate in the elderly . Next , we determined whether there is an overall correlation between age-related expression in mice and in humans . That is , among the entire set of genes that increase expression with age in mice , is there also a tendency for these genes to increase expression with age in humans , and vice versa ? As a measure of age regulation across nine mouse tissues and three human tissues , we used the Z-score from empirical meta-analysis . We then generated a scatter plot showing the Fisher's value for every orthologous gene and gene set in mice and humans ( Figure 5A and 5B ) . We found that there was no correlation between age regulation in mice and humans and that the scatter plots were randomly distributed . Specifically , each gene that changed significantly in both mice and humans contributed to a 2 × 2 table recording whether it had a positive or negative Z-value in mice and similarly in humans . We computed Fisher's exact test for independence on that table and found no association at the level of genes ( p < 0 . 15 ) and gene sets ( p < 0 . 71 ) . Genes or gene sets that increase with expression with age in mice may increase , decrease , or not change expression at all with age in humans . The same is true for genes or gene sets that decrease with expression with age . Finally , we repeated these analyses to compare aging in mice and humans for two specific tissues: kidney and muscle ( Figure S1 ) . As before , we found no overall correlation between age regulation in mice and humans . Thus , we have found no evidence for overall similarities in age-related expression between mice and humans . We have provided an expression database on mouse aging to be used as a resource for the research community . This dataset is unprecedented in its magnitude and experimental uniformity; it contains a total of 5 , 519 , 976 expression values for 8 , 932 genes in 16 tissues at four times during aging with ten biological repeats at each time . The tissue samples were isolated from the same set of 40 mice , and were hybridized to similar gene arrays using identical protocols in the same lab to maximize uniformity . Out of practical consideration , the experiments were limited to the C57BL/6 strain , and future experiments will be needed to find age-related differences between C57BL/6 and other mouse strains . The full dataset can be downloaded ( Table S1 ) and data for specific genes can be queried by researchers at http://cmgm . stanford . edu/~kimlab/aging_mouse and at http://www . grc . nia . nih . gov/branches/rrb/dna/agemap_data . htm . The DNA array experiments revealed a total of 906 age-regulated genes in nine different mouse tissues . These genes may be downstream markers for aging , such as stress response genes that are induced in old age due to accumulated oxidative damage . The expression levels of these genes inform us about the relative age of a tissue sample ( i . e . , high expression levels indicate high stress or old age ) . The identities of the age-related genes ( i . e . , stress response ) provide important clues about mechanisms that drive transcriptional changes in old age ( oxidative stress ) . This work identifies a large number of age-regulated genes that each provides an entry point for experimental study to determine its physiological role in aging . Further work may show that some genes are not only downstream markers , but that changes in their expression may either prolong life or hasten senescence . We note that in this work , we have analyzed these data using specific statistical techniques . Future analysis using increasingly advanced techniques may identify age-related behavior in gene expression that was not identified using current methods . We also focused on biological phenomena pertaining to changes in the mouse with age as a whole , as well as relationships and similarities between mouse tissues and between whole mouse and other species with age . In-depth work examining the effects of aging on specific , individual tissues or tissue groups has been accomplished for adrenal glands ( S . Chigurupati , data not shown ) , central nervous system regions [23] , thymus [24] , and gonads ( A . Sharov , data not shown ) . In addition to revealing expression of one gene at a time during aging , the entire set of age-regulated genes can be used as a molecular phenotype of aging and may be used to assess the influence of antiaging interventions on the aging process . The expression levels from all of the age-regulated genes can be compiled into one score revealing the relative age of a tissue sample . One can view the age of a tissue at the molecular level using such a transcriptional profile of aging , similar to viewing the cellular age of tissues with a microscope or the age of individuals with a camera . We used the aging expression phenotype to discern whether aging has a great or small effect on specific tissues , to categorize different tissues into one of three distinct aging groups , and to compare molecular phenotypes of aging among different tissues and among different species . Biomarkers of aging have been sought for some time for their potential utility in aging research [25] . In mice , numbers of T cells have been proposed to be measures of lifespan [26 , 27] . p16Ink4A and other inhibitors of the cell cycle have been identified as a biomarker of aging in mice [28–30] . p16Ink4A inhibits the cell cycle by repressing activity of cyclin-dependent kinases [31] . p16Ink4A and other cell cycle inhibitors increase expression during aging , and may be markers of physiological age . The DNA array data presented here confirmed an increasing trend in expression with age of cell cycle inhibitors in mice , and further showed a similar increasing expression trend in humans suggesting that these may be biomarkers of human aging as well . In humans , gene expression profiles of aging in kidney and muscle can be used as aging biomarkers , as the expression profiles predict physiological age of tissues in different patients [9 , 10] . In this study , we identify gene expression profiles for nine different tissues in mice . Our results show a substantial heterogeneity in the amount of age regulation in diverse tissues . Nine tissues show some level of age-related expression changes , ranging from 17 to 346 age-regulated genes . Since the great majority of physiological changes in a tissue will also result in changes in expression ( e . g . , oxidative damage induces the oxidative damage stress response ) , we expect there to be a high correlation between those tissues that age the most and those that show the greatest changes in gene expression . Tissues with a large number of age-regulated genes ( such as the thymus , eye , lung , and spinal cord ) may be exceptionally prone to age-related changes , and thus may contribute a disproportionate amount to aging in the mouse overall . Age-related expression changes may be due either to changes in gene expression or to changes in cell heterogeneity within a tissue . For example , the thymus is known to involute in old age , as thymocytes are replaced by fat cells and the overall size of the thymus is dramatically reduced . The expression profile for aging in the thymus contains genes with high expression in fat cells that show increased abundance in the thymus in old age , and genes with high expression in thymocytes that show decreased abundance . Both changes in cell-intrinsic gene expression as well as changes in heterogeneous cell populations could functionally impact aging tissues , and we do not distinguish between the two in this work . Seven tissues showed little or no significant changes in expression with respect to age , despite having similar levels of technical variability as the rest of the tissues . The lack of transcriptional change with age indicates that these tissues may be exceptionally resilient to the aging process . However , aging may affect these tissues in ways that would not be detected by the gene arrays , such as changes in protein levels or amounts of oxidative damage . Using the aging transcriptional profiles , we were able to cluster the tissues into three groups corresponding to three distinct modes of aging: neural , vascular , and steroid responsive . The formation of distinct aging clusters suggests that different tissues age via different pathways . The three distinct aging clusters might be affected to different extents in different mice , suggesting that individual mice may be composed of a mosaic of tissues with different physiological ages . However , these three age-related modes are defined by changes in gene expression and not by physiological measurements , and it will be important to determine whether these age-related transcriptional patterns accurately depict functional decline . Age regulation of 14 gene sets distinguishes the different aging patterns of the three tissue groups . The strongest signature differentiating the three tissue groups involves genes that encode ribosomal subunits . Genes in this pathway decrease overall expression with age in the steroid-responsive and vascular tissues , but increase expression with age in neural tissues . This finding suggests a strong role for protein synthesis and ribosomal regulation in determining how different tissues age . Although the steroid-responsive and vascular tissues showed fairly similar patterns of age regulation , a chief difference between the two tissue groups was in the age-dependent regulation of oxidoreductase genes . We have shown that many genes and several genetic pathways are age regulated in a similar fashion between human and mouse , despite the large disparity in lifespan between the two species . We have previously shown that the mitochondrial electron transport chain gene set shows an overall decreasing trend in expression with age in humans , mice , and flies [10] . Here , we have greatly expanded the analysis by including data from 15 more mouse tissues , and have shown that 22 genes and 17 gene sets change expression with age in both humans and mice . However , among all of these commonly age-regulated genes and gene sets , the mitochondrial electron transport chain gene set stands out because it is the only one that shows similar age regulation in flies and worms . One theory of aging is that accumulation of oxidative damage over a lifetime results in cellular senescence , and the electron transport chain is the primary source of oxidative damage via free radicals [32] . A decrease in expression level of genes in the electron transport chain pathway may reduce levels of oxidative damage in old age . However , we found no global , overall correlation between age-related expression changes in mice with those from humans; we found similarity only in a few isolated , specific gene sets . Thus , an aging pathway that showed increased expression with age in mice is as likely to decrease with age in humans as it is to increase . Similarly , for pathways that decrease expression in mice . We performed this analysis by comparing the overall change in expression averaged from all nine mouse tissues and all three human tissues . We repeated the analysis by comparing age-related expression changes between mice and humans in single tissues ( kidney and muscle ) . We found that none of these two tissues showed a significant correlation in age-related changes in expression between mice and humans ( Figure S1 ) . The results from this paper indicate that age-related changes in a gene or pathway in mice do not reliably predict age-related changes in humans . Future experiments using a larger sample size , improved technology , or different analytical techniques may show an overall correlation between aging in mice and humans . Unless an overall correlation can be found , in order to understand human aging , it will be necessary to perform human experiments to find out which aspects of aging are shared between mice and humans . Once identified , focused experimental attention on these public age-related pathways in mice has the greatest potential to reveal mechanisms relevant to human aging . Male and female C57BL/6 mice were obtained from the National Institute on Aging ( NIA ) colony at 3 wk and 5 , 15 , and 23 mo of age . Mice were euthanized by cervical dislocation at 1 , 6 , 16 , and 24 mo of age . Each mouse was dissected in a span of time ranging from 10–12 min . After cervical dislocation , mice were decapitated and an incision was made in the ventral side . Following the incision , the skin was degloved and harvested , exposing the abdominal tissues , which were harvested in order of pancreas , gonads , and liver . Following abdominal dissection , thoracic tissues were harvested in order of heart , lung , thymus , and kidney . Simultaneously with abdominal and thoracic dissection , the head was dissected and brain tissues ( cerebellum , cortex , hippocampus , striatum , and spinal cord ) collected . Finally , skeletal muscle and bone marrow were harvested last . All tissues were collected in RNAlater ( Ambion ) on ice and stored at −20 °C . The animal study protocol was reviewed and approved by the gerontology research center's animal care and use committee . All tissue samples were homogenized using a minibeadbeater-8 with 1 . 0mm zirconia/silica beads ( Biospec Products ) . Total RNA was isolated from each sample homogenate using a RNeasy Mini Kit ( Qiagen ) in accordance with the manufacturer's protocol . RNA quality was assessed using an Agilent 2100 Bioanalyzer . All hybridizations were to NIA mouse 17K cDNA arrays . These arrays are composed of cDNA spots taken from both the NIA mouse 15K cDNA and NIA mouse 7 . 5K cDNA clone sets [33 , 34] . The 15K and 7 . 5K cDNA clone sets are constructed from E7 . 5 extraembryonic mouse tissue , preimplantation embryos , and stem cells using a PCR-based cDNA library construction method , and may therefore exclude some genes not expressed in placental and embryonic tissues . Each gene array membrane was prehybridized in 4 ml of hybridization buffer containing 3 . 2 ml of Microhyb ( Invitrogen ) , 0 . 8 ml of 50% dextran sulfate , 100 μl of 10 mg/ml denatured human Cot DNA ( Invitrogen ) , and 100 μl of 8 mg/ml denatured poly ( dA ) ( Sigma-Aldrich ) for 2 h at 55 °C . Radiolabeled cDNA probes were heat denatured and hybridized with gene array membranes using 4 ml of fresh hybridization buffer for 18 h at 55 °C . After three high-stringency washes in 2× SC and 0 . 1% SDS for 15 min at 55 °C , the membranes were exposed to a phosphor screen for 3 d and scanned using a Storm 860 PhosphoImager ( Molecular Dynamics ) with 50 μm resolution . We calculated log2 ( expression level ) , and then used the Z-score method to normalize the gene array expression data as previously described [35] . All data will be available on the Gene Expression Omnibus upon publication , at http://cmgm . stanford . edu/~kimlab/aging_mouse , and at http://www . grc . nia . nih . gov/branches/rrb/dna/agemap_data . html . When different probes on the gene array corresponded to the same gene , we averaged the Z-scores from each of the probes together . We used a multiple regression model to measure changes in expression with age for 8 , 932 genes in each of 16 individual tissues . This model assumed that differences in levels of gene expression may exist because of age or sex: where Yij is the expression level of the jth probe set for the ith sample , Agei is the age of the ith sample , Sexi corresponds to the sex of the ith sample ( 0 for male , or 1 for female ) , ɛij represents an error term , β1j is the change of expression with age , β2j is the change of expression with sex , and β0j is the regression intercept . For each gene j , we used a least-squares approach to determine each coefficient; we were primarily interested in genes that show either a positive or negative value for β1j , indicating either increasing or decreasing expression levels with age , respectively . We wanted to determine whether the observed trends in expression of these genes were driven by developmental changes between 1-mo-old mice and older mice . We excluded the 1-mo-old cohort from the data , and then recalculated the slopes with respect to age for all of the genes that were originally found to be age related in Table 1 . We found that all of the original age-related genes continued to show age-related trends in gene expression for 6–24 mo ( unpublished data ) . This result indicates that the changes in expression occur throughout aging , and not only during development . At p < 0 . 001 , we would expect approximately nine genes on average to appear age regulated by chance alone , given a gene array containing 8 , 932 genes . If the gene tests were independent , then there would be less than a 10% chance of seeing 14 or more false rejections . However , expression levels of different genes are correlated for biological as well as technical reasons . This intrinsic biological correlation induces unperceived correlations in the test statistics for genes . To obtain an exceedance control that accounts for correlated genes we used permutations . We randomly permuted the ages of the 20 female mice and 20 male mice , maintaining sex . We then calculated the number of significant genes at p < 0 . 001 on each instance of permuted data . We repeated the permutation process 1 , 000 times and counted the fraction of those 1 , 000 repeats that generated more genes than we found in the original data . If that fraction was larger than 10% we did not consider the tissue to be age regulated . We used a modified version of gene set enrichment analysis [14 , 15] to test whether gene sets either increase or decrease their overall gene expression levels with age [10] . Our modification replaces the weighted Kolmogorov-Smirnov test statistic by a Van der Waerden Z-score . The Z-score is intended to measure whether the bulk of the genes in the gene set are age regulated and is less affected by small numbers of extreme genes . For gene j and tissue k we computed a one-sided p-value for age based on the t-test from a regression of gene expression on age and sex . The corresponding two-sided p-value is = 2min ( , 1 − ) . A standard method for combining m independent p-values is Fisher's method based on . When the m p-values are independent and all m null hypotheses hold , then has the distribution . Fisher's method is very powerful , but we needed to modify it . The statistic is equally sensitive to all patterns in which gene j increases with age in one subset of tissues and decreases in another . To focus on genes that show consistent directionality , we introduce two one-sided meta analysis statistics , and , which take extreme values for genes that are consistently decreasing or consistently increasing , respectively , over the m tissues . From these we obtained nominal one-sided p-values and and the combined directionally sensitive p-value for gene j is . The statistic does not have the U ( 0 , 1 ) distribution even when gene j is unrelated to age in all m tissues . First , the factor 2 is a Bonferroni style correction for making two tests on that gene , and is somewhat conservative . Second , the m tissue-specific datasets on which and are based are statistically dependent because they have come from the same animals . There were 346 genes with in the dataset . We discarded 32 of them for which the p-value was driven by essentially one tissue only , leaving 314 genes for further analysis . The gene list is potentially too small because the factor of 2 may be too conservative and potentially too large because of dependencies among tissues . We validated the list empirically using the 8 , 932 observed genes , through a permutation analysis . In each resampling , the ages of the 20 male mice were randomly permuted , as were those of the 20 female mice . Male and female mouse ages were permuted independently . The newly generated ages were used for all tissues from the same animal . Among 1 , 000 such permutations , only three of them had more than the 314 significant genes found in the data . Fisher's meta-analysis is perhaps the best-known method for combining multiple test statistics on a single hypothesis , but other approaches are available . Birnbaum [36] has shown that the most powerful combination method depends on the alternatives of interest . We constructed our test to be sensitive to alternatives where several alternatives hold with the same sign . Whitlock [37] mentions the combination method we have used in his introduction but discounts it in favor of some one-sided weighted Z alternatives to Fisher's test . Whitlock advocates a one-sided weighted Z test . Such a test would “punish” a gene heavily for having a slope in one tissue of a sign opposite to that which it has in several other tissues . We did not use Z-based tests because we are interested in finding such genes . We thank a referee for bringing Whitlock's paper to our attention . For each age-regulated tissue , we first identified the 50 genes most increasing expression with age ( as determined by slope with age ) as well as the 50 genes with the greatest decrease in expression with age . For each tissue and mouse pair , we then calculated the expression: where Si denotes the score for the ith mouse , βage , j shows the slope of expression with age for the jth gene , Xij is the Z-normalized expression level of the ith mouse in the jth gene , and is the average Z-normalized expression level of the jth gene in the specific tissue . We then converted the raw scores for each tissue into fractional scores within each of the four age cohorts , denoted by: Ri is the fractional score for the ith mouse . This method of generating fractional ranks minimizes the effects of missing mice for a particular tissue and age cohort by centering the fractional scores on 0 . 5 regardless of the number of mice-tissue pairs within an age cohort . We finally calculate the overall , or sum fractional , score by adding fractional scores across all tissues for each individual mouse as described by: Oi is the overall score for the ith mouse , and T is the total number of tissues; in this case , nine . In order to determine whether the overall scores for each of the four age cohorts were more widely distributed than we would expect by chance , we calculated the variance of the overall scores for all ten mice in each of the four cohorts . For each cohort , we then permuted the fractional scores for each mouse within each tissue 1 , 000 times , and recalculated overall scores using Equation 4 and determined the variance for each set of permuted fractional scores . The p-value for significance for each age cohort was equivalent to the number of times that permuted variances were greater than the actual variances . We considered the possibility that the results could reflect a technical artifact in which the RNA from all of the tissues of an individual mouse was degraded , due to mishandling , disease , or some other factor . If so , then all of the transcripts from that mouse would show weak correlations with the other mice , not just the age-regulated genes . In order to address this possibility , we calculated the Pearson coefficients between individual mice within each tissue for all 8 , 932 genes . We did not observe any mice exhibiting an unusually low Pearson correlation with other mice ( unpublished data ) , showing that the tissue coordination effect is seen for age-regulated genes .
This work studies the aging process in mice using DNA microarrays to identify genes that change expression during aging . The entire set of age-regulated genes constitutes a transcriptional profile that can be used to measure ages of different individuals . Furthermore , the aging expression profile highlights genetic and metabolic pathways that change with age , providing key insights about possible molecular changes that may contribute to cell senescence and physiological decline . This aging study is massive in scope , involving gene expression measurements from 16 different tissues at four different ages . Expression data for genes of interest can be queried over the web by researchers interested in aging . Different tissues were found to have strikingly different levels of age-related change , and could be divided into three groups based on their patterns of aging: a neural group , a vascular group , and a steroid-responsive group . The electron transport chain pathway stands out because it is the only genetic pathway that shows a similar pattern of age-related change in mice , humans , worms , and flies . However , there is little overall similarity between changes in gene expression during aging of humans and mice , consistent with evolutionary theories suggesting that aging lies outside the force of natural selection .
You are an expert at summarizing long articles. Proceed to summarize the following text: House dust mites are common pests with an unusual evolutionary history , being descendants of a parasitic ancestor . Transition to parasitism is frequently accompanied by genome rearrangements , possibly to accommodate the genetic change needed to access new ecology . Transposable element ( TE ) activity is a source of genomic instability that can trigger large-scale genomic alterations . Eukaryotes have multiple transposon control mechanisms , one of which is RNA interference ( RNAi ) . Investigation of the dust mite genome failed to identify a major RNAi pathway: the Piwi-associated RNA ( piRNA ) pathway , which has been replaced by a novel small-interfering RNA ( siRNA ) -like pathway . Co-opting of piRNA function by dust mite siRNAs is extensive , including establishment of TE control master loci that produce siRNAs . Interestingly , other members of the Acari have piRNAs indicating loss of this mechanism in dust mites is a recent event . Flux of RNAi-mediated control of TEs highlights the unusual arc of dust mite evolution . House dust mites are ubiquitous inhabitants of human dwellings , and are the primary cause of indoor allergy [1] . Dust mites have an unusual evolutionary history , descending from a parasitic ancestor [2] . Parasite genomes are typically highly modified; possibly to accommodate genetic novelty needed to productively interact with a host [3 , 4] . The sequence of events leading to adoption of a parasitic lifestyle may require a period of genomic crisis to yield the rewired parasite genome . Dust mites represent an extreme case potentially experiencing a second round of genomic change to reacquire a free-living ecology . Transposable element ( TE ) activity is a major source of genome instability [5 , 6] . Silencing of TE activity in multicellular organisms is commonly achieved by RNA interference ( RNAi ) -based mechanisms , which employ small RNAs associated with Argonaute/Piwi ( Ago/Piwi ) proteins to target TE transcripts [7] . In many animals , the Piwi-associated RNA ( piRNA ) pathway is the primary RNAi-based defense [8 , 9] . In arthropods and vertebrates piRNAs are recognized as being roughly 23–32 nucleotides ( nt ) long , and unlike other small RNAs , such as microRNAs ( miRNAs ) and small-interfering RNAs ( siRNAs ) , they are not excised from double-stranded RNA ( dsRNA ) precursors by the RNase III enzyme Dicer [10] . piRNAs in Drosophila are generated in two collaborative pathways: Phased cleavage of transcripts by the RNase Zucchini ( Zuc ) and a “ping-pong” mechanism involving direct cleavage by Piwi proteins [11] . Ago/Piwi proteins may possess “slicer” activity which cuts transcripts base-paired with a bound small RNA 10 nt from the 5’ end of the small RNA [12] . Zuc-dependent piRNA biogenesis is initiated by Piwi-mediated slicing of targeted transcripts , which propagates in a 5’-3’ direction from the site of scission [13 , 14] . These piRNAs feed into the ping-pong where Piwi proteins collaborate to capture fragments of TEs and convert them to new piRNAs [15 , 16] . This leads to further production of Zuc-dependent piRNAs in an amplifying system [17] . As TE transcripts processed through the ping-pong pathway are products of slicing they exhibit 10 nt 5’ overlaps with cognate , antisense piRNAs [15] . In contrast , Zuc-dependent piRNAs are derived from single stranded RNA precursors , and while this process has been found to be dependent on initial slicing there are Drosophila cell types in which the ping pong system is absent that initiate zuc processing through the factor Yb [18] . Another feature of piRNA-mediated genome surveillance is the involvement of piRNA cluster master loci as sites of Zuc-dependent piRNA production [19] . These loci are composed of TE fragments , serving as catalogs of restricted sequences . Loss of master loci integrity compromises TE repression and causes sterility . Nematode piRNAs , while possessing a related role in controlling TEs , differ in that they are short ( 21nt ) cleavage products of small discrete transcripts [20] . Despite these differences in biogenesis , piRNAs in both species typically exhibit an “U” residue at the 5’ terminus . The exception is some ping-pong piRNAs , which instead have an “A” at the tenth position . While piRNA regulation of TEs is common in animals , it has been lost in several nematodes and platyhelminths [21 , 22] . In the nematode species , alternative mechanisms restrict TE mobilization involving Rdrp ( RNA dependent RNA polymerase ) and Dicer . Conversion of TE transcripts by Rdrp into dsRNA substrates of Dicer results in siRNA generation . Ago proteins then associate with nascent TE transcripts , recruiting chromatin modulators including DNA methyltransferase . This process , RNA-induced transcriptional silencing ( RITS ) is common in plants and fungi [23–25] . RITS-like mechanisms are found in animals as nuclear localized Ago and Piwi proteins can influence chromatin biology [26 , 27] . However , outside nematode clades , amplifying RITS mechanisms involving siRNAs have not been observed in vertebrates or other ecdysozoans–potentially due to absence of Rdrp [28] . One possible exception is chelicerae arthropods , a lineage where dust mites belong , which possess Rdrp proteins . RNAi pathways in chelicerates appear complex as they have both Rdrp and Piwi class Argonaute proteins , both of which appear to have roles in controlling TE’s [29–31] . Here we investigate the status of small RNA pathways in the dust mite to understand how RNAi biology might be structured in this highly-derived organism . We obtained a genome sequence for the American house dust mite Dermatophagoides farinae using Illumina and PacBio platforms . The HGAP pipeline was used through PacBio SMRT analysis portal to filter and assemble PacBio reads , which resulted in 1 , 828 contigs producing a total length of 93 , 777 , 723 bp [32] . Then , Illumina reads were used to connect and extend the PacBio contigs using SSPACE scaffolding , which produced a 93 , 804 , 520 bp assembly in 1728 scaffolds [33] . After removal of bacterial contamination , the final contig number was reduced to 1706 with a N50 read length of 19 , 371 . The assembled and filtered final genome was ~92 Mb compared to a 53 Mb genome that was previously reported [34] . Using mRNA-seq datasets we annotated ~18 , 500 transcripts through the Cufflinks program [35] . 47% of the genic transcripts exhibited similarity to S . scabiei and/or D . melanogaster protein coding genes or to the NCBI conserved domain collection ( Materials and Methods ) ( S1 Table ) [34 , 36] . Ago/Piwi proteins were identified in the D . farinae genome using RNA-seq annotations and amino acid sequences of seven Ago and six Piwi proteins from Tetranychus urticae–the closest relative of D . farinae with a high-quality genome and experimentally supported annotations [29] . Eight confident Ago homologs were found ( Ago1-GenBank ID: KY794591 , Ago2-GenBank ID: KY794592 , Ago3-GenBank ID: KY794593 , Ago4-GenBank ID: KY794594 , Ago5-GenBank ID: KY794595 , Ago6-GenBank ID: KY794596 , Ago7-GenBank ID: KY794597 , Ago8-GenBank ID: KY794598 ) . Ago proteins from T . urticae , D . melanogaster , C . elegans , and Ascaris suum were compared to D . farinae Agos using amino acid sequences of Paz , Mid , and Piwi domains ( Fig 1A ) . Our phylogenetic analysis recovered two Ago family members likely involved in miRNA ( DfaAgo1 ) and siRNA ( DfaAgo2 ) pathways [37] . The remainder belong to a divergent clade specific to dust mites ( DfaAgo3-8 ) . Surprisingly , none of the Agos from D . farinae belong to the Piwi clade . We examined D . farinae Agos for the presence of slicer motifs . The DEDH slicer motif , which is common in metazoan Ago and Piwi proteins , was found in DfaAgo1 ( miRNA ) and DfaAgo2 ( siRNA ) . The divergent Agos have an uncommon DEDD catalytic motif ( S2 Fig ) . Orthologs containing a DEDD motif can be found in scabies ( S . scabiei ) , social spiders ( Stegodyphus mimosarum ) , and in C . elegans Ago family members of unknown function; which emphasizes the unusual nature of this Ago clade [36 , 38 , 39] . A dust mite small RNA library of nearly 400 million reads was generated to investigate whether piRNA-class small RNAs could be identified ( S1 Text ) ( S1 Fig ) . To accommodate the repetitive nature of piRNA targets all mapping events were captured for reads that mapped fewer than 100 times . An overall rate of ~80% mapping was observed with ~0 . 69% discarded due to mapping >100 times ( S1 Fig ) . Next an algorithm that determines small RNA read overlap probabilities in mapping data was used to characterize biogenesis of dust mite small RNAs [40] . When applied to either all mapping or mapping in discreet size ranges no clear bias for 10nt overlapping reads was uncovered , showing an absence of ping pong processing ( Fig 1B ) . Instead a strong signal seen in a register 2nt shorter than the length of read sizes . This is congruent with 2nt overhangs left by Dicer cleavage . Overlaps seen in dust mites starkly contrast with those seen in spider mites and Drosophila . In spider mites , ping pong signatures could be seen in longer reads ( 23-28nt ) and the dicer-associated 2nt register in shorter ( 20-22nt ) reads ( Fig 1C ) . Likewise , in RNAs sequenced from Drosophila female bodies a prominent ping pong signature is evident ( Fig 1D ) . siRNA processing was not evident when considering whole genome mapping , but could be seen in a group of Drosophila IDEFIX retroelements that had biased mapping of 21nt RNAs ( S3 Fig ) ( S3 Table ) . Drosophila endo-siRNAs are a relatively small proportion of total small RNAs , and are frequently produced from inverted repeat loci which are not captured by the overlap probability calculation used here [41] . Moreover , this highlights a correlation between the presence of Rdrp in spider mites and an expanded population of Dicer products . Together this shows a dramatic departure in the composition of dust mites small RNA populations relative to those in the Piwi protein possessing spider mite . The difference is even more stark when comparing dust mites to the more distantly related fruit fly . The configuration of RNAi in spider mites is likely ancestral due to the similarities to Drosophila , which is supported by clear orthology of spider mite Piwis to distinct Drosophila ping-pong partners: dmeAgo3 and Piwi/Aub ( Fig 1A ) . Thus , RNAi pathways have diverged in dust mites and appear to be dominated by siRNA-like Dicer products , and lack the signature of amplifying ping pong piRNAs [42] . To functionally characterize dust mite small RNAs , we sought to identify genomic loci that generate and/or are targeted by these transcripts ( S1 Fig ) . To achieve this , annotation of the dust mite genome was extended to find ncRNAs and repetitive elements using Repeatmasker . Additionally , non-miRNA , small RNA producing loci were annotated that exhibited >1000 read density and were longer than 200nt . The identities of regions were determined using blast2go [43] . Nearly a third of the loci were rRNA or mRNA . The remainder showed homology to either TEs or lacked similarity to known sequences . Together this permitted segmentation of the dust mite genome into mRNA , TE , rRNA , tRNA , snRNA , and unknown small RNA-mapping loci ( S2 Table ) . Small RNA reads were then mapped to these regions using multiple mapping conditions described above , as well as unique mapping . To ensure multi-mapping events were specific to loci groups , datasets were cleaned before mapping by removing reads that mapped to non-target genomic features ( S1 Fig ) . Multi-mapping alignments showed considerable enrichment at TEs relative to other classes , consistent with their repetitive nature ( Fig 2A ) . Both multi- and uniquely mapping TE reads also exhibited lower strand bias with only a single locus showing 100% bias after unique mapping ( S4 Fig ) . This is consistent with processing from dsRNA . Higher bias was seen at other loci , suggesting that some mapping events may be due to capture of RNA degradation fragments and not functional small RNAs . This was supported when overlap probabilities were calculated; which , with exception of TEs and mRNAs , did not show consistent processing signatures ( S5 Fig ) . This includes the unknown loci , suggesting these transcripts may be degradation products of uncharacterized ncRNAs and are not generally siRNA or piRNA class small RNAs . Closer inspection of per locus overlaps did show Dicer processing at a minority of loci ( S6 Fig ) . There was no clear ping pong processing at unknown loci . Small RNA mapping coverage was calculated per locus to understand siRNA production from TEs and mRNAs ( Fig 2B ) . On average , small RNA coverage was even across TE loci , while mRNAs had greater coverage at transcript 3’ ends . This pattern at mRNA loci is suggestive of cis-NAT siRNAs [44] . Depth of coverage at TE loci varies , showing that active targeting is occurring at a subset of loci . The absence of purely single-stranded small RNA producing loci that have homology to TE sequences suggests that dust mites also lack a Zuc-dependent piRNA-like pathway that is involved in genome surveillance . This does not rule out the existence of dual strand piRNA clusters; however , piRNAs produced from these loci are found to participate in the ping pong cycle , which we did not observe [45] . These data suggest that the piRNA pathway has been lost in dust mites and control of TE’s is likely under the purview of a siRNA-like pathway . To investigate the role of dust mite small RNAs in genome surveillance we compared the biogenesis of TE-associated small RNAs to those found in spider mites . The size distribution of genome-aligned dust mite small RNAs is unimodal with a peak at 24nt , versus a bimodal distribution in spider mites ( Fig 3A ) . When TE-mapping reads are examined , the 24nt sized RNAs in dust mite were enriched by 10% , while in spider mites only larger size range RNAs were found ( Fig 3B ) . In other locus groups , less read size bias was observed , consistent with the heterogeneity seen in strand bias and read overlap probabilities , further reinforcing that generally non-TE loci do not produce small regulatory RNAs ( S7 Fig ) . Next we looked at the 5’ nucleotide bias and found that dust mites TE siRNA reads have an equal prevalence of T and A residues versus spider mites where there was striking over representation of T ( Fig 3C ) . Then we examined per locus read size distribution and overlap probabilities to assess whether Dicer processed ~24 nt small RNAs are common across dust mite TE loci ( Fig 3D ) . All loci exhibited mapping of predominantly 24 nt reads , and in the most prevalent size ranges ( 23-26nt ) a clear pattern of overlaps could be seen that is consistent with Dicer processing ( Fig 3D ) . This contrasts with a similar analysis in spider mite where a ping pong signature was seen across all TEs . Together this suggests siRNAs are the main RNAi-based mode of controlling TEs in dust mites , accommodating the apparent loss of piRNAs . This is a clear departure from spider mites where stereotypical piRNAs target TEs . In the D . farinae genome we found three Dicers ( DfaDcr1-GenBank ID: KY794588 , DfaDcr2-GenBank ID: KY794589 , DfaDcr3-GenBank ID: KY794590 ) . DfaDcr1 is a close ortholog of Arthropod miRNA-producing dicer ( S8 Fig ) . The other two Dicer proteins are related to family members in other mites and lophotrochozoans , and are unrelated to Arthropod Dicer2 or nematode Dicer ( S8 Fig ) . Unexpectedly , DfaDcr1 possesses an ATP binding helicase domain , which is implicated for processing of long dsRNA ( S9 Fig ) [46] . The more divergent Dicers , DfaDcr2 and DfaDcr3 , lack both DUF283 and dsRNA binding domains , and have divergent PAZ domains ( S9 Fig ) [46–48] . Together this suggests that mites , and possibly other chelicerates , possess ancient Dicer biology present in basal protostomes that was lost both in nematoda and pancrustacea ( insects and crustaceans ) . To verify whether TEs are controlled by Dicer-produced siRNAs we sought to inhibit the activity of dust mite Dicer proteins . To generate loss of Dicer function we elicited RNAi against each Dicer by feeding mites cognate dsRNA ( Fig 4 ) . Dust mites tolerate being soaked for several hours in aqueous solution , which they can be observed to ingest after 30 mins ( Fig 4A ) . Small RNAs ( 20-27nt ) derived from dsRNA can be recovered from soaked mites ( Fig 4B ) . Knockdown of target genes can also be observed ( Fig 4C–4K ) . Depletion by RNAi of each DfaDcr protein resulted in derepression of multiple TEs ( Fig 4L ) ( S10 Fig ) . A strong effect was seen with loss of DfaDcr1 and DfaDcr2 function . The presence of processive helicase activity in DfaDcr1 suggests that long dsRNAs could be substrates . This combined with the lack of dsRNA binding motifs in DfaDcr2/3 suggests DfaDcr1 has a unique capacity to process dsRNA ( S9 Fig ) , and therefore it is unsurprising that it has a significant role in the control of TEs ( Fig 4L ) . Loss of DfaDcr2 showed a greater effect on TE expression compared to DfaDcr3 . How these atypical Dicer proteins function is unclear; however , residues in the DfaDcr3 PAZ differ significantly from those in DfaDcr2 PAZ suggesting non-overlapping roles in the metabolism of dust mite small RNAs ( S9 Fig ) . These results are consistent with reports that psoroptid mites are sensitive to dsRNA soaking , resulting in gene knockdown [49 , 50] . Investigation of RNAi in dust mites revealed loss of the piRNA pathway and replacement by siRNAs . This is similar to observations in nematodes and flatworms [21 , 22] . The loss of piRNA activity in dust mites , nematodes , and possibly in flatworms may be tolerated due to compensation by amplifying siRNAs produced by Rdrp [21 , 51] . The collective function of dust mite Rdrps , however , appears to be distinct from nematodes , as only processive versions are present , suggesting the de novo siRNA pathway may not be present in mites ( S11 Fig ) . Substantial Rdrp activity does appear to be present in dust mites; dsRNA soaking results in elevation of target mRNA when reverse transcription is carried out with random hexamers ( Fig 4E , 4G , 4I and 4K ) but not oligo dT ( Fig 4D , 4F , 4H and 4J ) . Increase of transcript abundance was not due to the presence of ingested dsRNA as the region cloned to generate dsRNA was distinct from the qPCR amplicon ( S10 Fig ) . Random priming will capture Rdrp products , while oligo dT will only hybridize to the initial transcript . For all the genes tested an elevation of cognate transcripts could be observed after random priming that were poorly recovered from Oligo dT primed cDNA . Dust mites differ from nematodes that lost piRNAs in the organization of siRNA producing loci . A key feature of piRNA biology is the cataloging of restricted sequences into master loci . In nematode lineages lacking piRNAs , master loci also appear to be absent [21] . This is not the case in dust mites ( Fig 5A ) . Three loci were discovered that span 62 kb , contain sequences from multiple varieties of TEs , and exhibit homology to 70% of TE mapped small RNAs ( Fig 5B ) . Two of the loci , ML-283 and ML-95 , appear to be generated by duplication; however , some sequence divergence indicates they are distinct loci . Similar regions could not be found in the S . scabiei genome [52] . Though , poor conservation is a characteristic of piRNA master loci [53] . The dust mite loci appear to be generated from a dsRNA precursor as both strands of the loci show similar rates of read mapping ( Fig 5A ) . We found a tendency for 2nt overhangs along with little evidence for nucleotide bias ( S12 Fig ) . The loci were inspected for common motifs using the meme suite [54] . Motifs recovered were primarily simple repeats with none being shared between loci suggesting dust mite master loci don’t possess elements like the Ruby motif which is central to directing piRNA transcription in C . elegans [55] . Following knockdown of each of the individual dust mite Dicers significant ( >80% ) reduction in siRNAs exhibiting homology to these regions was observed , indicating a dependence on the activity of all dust mite Dicers for biogenesis ( Fig 5C ) . Detection of the siRNAs was accomplished with a combination of oligonucleotide probes complementary to sites of highest small RNA density in the three master loci ( S1 Text ) . They also have homology to other regions of the genome , specifically TEs . Thus , the Dicer sensitive siRNAs include master loci derived primary siRNAs and potentially secondary siRNAs generated from processed TE transcripts . This is consistent with loss of TE control after knockdown of each Dicer ( Fig 4L ) . However , there is a clear difference in the magnitude of TE expression , which may point to roles for dust mite Dicer proteins outside the production of siRNAs and to involvement in targeting of TE transcripts . This could be similar to limiting of latent viral infection by Drosophila Dcr2 [56] . Next , we sought to characterize terminal moieties of master loci associated siRNAs through biochemical tests to gain greater insight into their biogenesis ( Fig 5D and 5E ) . The primary goal was to determine if the siRNAs had characteristics of Dicer cleavage: 5’-monophosphates and 3’-OH groups . β-elimination showed a shift to a lower molecular weight indicating an unmodified 2’OH; therefore , unlike Drosophila Ago2 endo-siRNAs or C . elegans Prg-1 associated small RNAs , dust mite siRNAs are not 2’-OH methylated ( 2’OMe ) ( Fig 5D ) [57 , 58] . Next , we identified groups on 5’ ends of small RNAs using the 5’ monophosphate specific terminator ribonuclease . After treatment , a 50% reduction in siRNAs could be observed ( Fig 5E ) . Degradation by terminator could be abrogated by prior treatment with calf intestinal phosphatase ( CIP ) . There is a noticeable lag in siRNA gel migration following CIP treatment , which is consistent with removal of 5’ phosphate groups and loss of charge . These results also reinforce the absence of a de novo siRNA pathway . Small RNAs produced by non-processive Rdrps in C . elegans have 5’ triphosphate groups . While treatment with terminator did not completely eliminate siRNAs there was no observable change in migration . If the remaining small RNAs were spared due to the presence of trisphosphate groups there would be shift towards a smaller molecular weight , relative to untreated . Together , dust mite master loci associated siRNAs appear to be Dicer products arising from a dsRNA precursor , possess the expected 5’-monophosphate , but differ from insect endo-siRNAs due to the absence of 2’-OMe groups . We were able to identify a dust mite gene with similarity to Hen1 methyltransferase proteins; however , inspection of potential open-reading frames revealed the absence of a common motif involved in recognition of 2 nt 3’ overhangs characteristic of Dicer products ( S12 Fig ) . This likely explains the lack of 2’-OMe groups on dust mite siRNAs . Extent of DNA methylation in CG widely varies across insect clades and can be as high as 40% in roaches , while other groups , like flies , show little evidence for this modification [59] . Here we investigated whether this epigenetic control mechanism is a component of TE control in dust mites , as the genomes of nematodes and platyhelminths that lack the piRNA pathway are frequently modified by cytosine methylation [21 , 60] . Dust mites differ from these organisms , as evidence for this modification seems minimal and it is not enriched at TE loci ( Fig 6A ) . Indeed , bisulfite sequencing showed potential CG and CHG methylation is underrepresented in TE sequences , despite these sites occurring at the same rate as other genomic loci . Furthermore , the overall rate of DNA methylation ( 0 . 5% ) was very low , suggesting this base modification is not a major feature of dust mite chromatin regulation . Moreover , we found a single DNA methyltransferase in the D . farinae genome , a Dnmt1 homolog ( Fig 6B and 6C ) . It is likely a pseudogene as it appears to be truncated and shows little evidence of expression . This further highlights the distinct , derived nature of small RNA-mediated genome surveillance in dust mites . This work provides insight into the elaborate nature of RNAi in chelicerates , many of which appear to have both Piwi proteins and Rdrps [29 , 30 , 39] . Loss of the piRNA pathway in dust mites probably occurred in the parasitic ancestor . Inspection of the scabies mite genome similarly failed to uncover Piwi proteins ( S13 Fig ) [36] . Members of the divergent dust mite Ago family; however , were found . Indeed , a deeper inspection of scabies mite RNAi factors uncovered further similarities to dust mites ( Table 1 ) . Thus , absence of the piRNA pathway in dust mites is likely a consequence of descending from an ancestor that underwent dramatic genome changes , potentially during the acquisition of a parasitic life style . This highlights plasticity of RNAi pathways and how clade-specific biology might impact evolution of RNAi technologies . Dust mites exhibit a highly distinct RNAi biology , possessing both novel and ancient effectors that haven’t been studied in popular ecdysozoan model organisms . Indeed , there seems to be wholesale changes to the small RNAome of these organisms . Dicer produced siRNAs are an unusually common feature of the dust mite small RNA populations , comprising approximately three-fourths of all small RNA species . This contrasts with many other organisms where microRNA-class small RNAs are the archetype . Dust mite siRNAs are , at least in part , involved in genome surveillance . They target TE’s and depletion of Dicer proteins causes derepression of these elements . Control of TE’s is typically carried out by piRNAs in flies , from which dust mite siRNAs are distinct . A common feature of nearly all piRNAs is a “U” residue at the first position . We do not observe this in any subset of dust mite siRNAs . Furthermore , well-described modes of piRNA biogenesis found in Drosophila and C . elegans are absent in dust mites . Loss of piRNAs seems specific to psoroptidian mites , as they are clearly present in other Acari , like spider mites . The divergent nature of dust mite siRNAs is particularly apparent in the absence of 2’-OMethylation of siRNAs–a common feature of siRNAs and piRNAs in other organisms . Interestingly , scabies mites also lack the requisite Hen-1 protein [36] . Inspection of syntenic regions of the dust mite and scabies mite genomes showed rearrangements at this locus , potentially linking the loss of this activity to the evolution of Psoroptidia-specific Ago proteins ( S13 Fig ) ( Table 1 ) . The highly divergent RNAi pathways of dust mites provide an evolutionary perspective not only on the utility of small RNAs to acquire roles in genome surveillance , but also that the precise mechanism may not be that important . This is supported by relatively similar composition of classes of TE’s in spider mites , dust mites , and scabies mites ( S15 Fig ) . While similar classes were observed their locations and specific identities are distinct . Furthermore , this indicates that the collection of dust mite TEs analyzed in this study accurately represent the overall TE population . Flux of small RNA pathways correlates with evolutionary innovation; for example , higher arthropods lost Rdrp in favor of piRNA control of TE [61] . This also occurred when vertebrates diverged from basal chordates [62] . In both cases , loss of Rdrp accompanied innovation in body plan and sensory organs . In vertebrates , whole genome duplication occurred twice following descent from a Rdrp expressing chordate ancestor , affirming a period of genome instability [62] . TE mobilization may be fortuitous for adaptation , and dramatic evolutionary changes may require extreme events such as perturbation of surveillance mechanisms . The dust mite genome was assembled using reads produced by PacBio and Illumina platforms . The initial assembly was generated by PacBio HGAP . Illumina reads were preprocessed in three steps before using them for extending PacBio contigs: a ) Using Trimmomatic [63] , from both ends of reads , nucleotides with base quality lower than 15 were removed . b ) Using FastUniq [64] , duplicate pairs were removed from the PE library , and c ) SOAPec [65] was used to correct read error [64 , 65] . Any initial genome sequence has bacterial contamination due , at least , to the presence of gut microbiota in DNA isolates . To remove bacterial DNA sequences from D . farinae genome sequence , 4 , 864 , 367 Bacterial genome sequences [66] were downloaded from RefSeq database at: ftp://ftp . ncbi . nih . gov/refseq/release/bacteria and a blast database was created using the sequences [66 , 67] . All the contigs were blasted against the created bacterial genome database to check bacterial contaminations in the sequenced contigs . Then the matched percentages were calculated for each of the contigs . If the matched percentages were higher than 10% of an individual contig length , the contig was considered as contaminated by bacterial DNA and was discarded . After this process , our final contig number was reduced to 1706 , N50 Read Length of 19 , 371 with the total length of 91 , 947 , 272 bp . Finally , a published dust mite genome [34] was compared to our assembled contigs using QUAST [34 , 68] . 79 . 3% bases of the reference genome could be aligned in the new assembly . Using available mRNA-seq datasets [34] , transcripts were identified by the Tuxedo suite . Initial mapping with Tophat was followed by transcript annotation with cufflinks [69] . Transcript similarity was estimated using Blast2Go . Total RNA isolated via the trizol method from bulk collected dust mites in order to capture life stages of D . farinae . Small RNAs were cloned from total RNA with an Illumina small RNA truseq kit , and sequenced on the Illumina NextSeq platform . The dataset was comprised of nearly 400 million reads . Quality of the sequenced library was assessed by FastQC tool and the small RNA reads were analyzed using a custom pipeline ( S1 Fig ) [70] . Mites collected with the salt bath method were suspended in a solution of dsRNA dissolved in nuclease free water ( S1 Text ) . After 6 hours , animals were washed in water and dried on filter paper . After that the animals were kept in 23°C with relative humidity of 80% . After two days , total RNA was extracted using trizol method and resolved in a 12 . 5% urea-polyacrylamide gel . When animals were fed unlabeled dsRNAs , RNAs were transferred to nylon membranes and subject to northern blotting as previously described ( S1 Text ) [56] . If radiolabeled RNAs were fed , gels were directly exposed to phosphoimager screens . 20 μg of total RNA was oxidized at room temperature in borax/boric-acid buffer ( 60 mM borax and 60 mM boric acid-pH 8 . 6 ) containing 80 mM NaIO4 for 30 min . β-elimination reaction was carried out for 90 min using 200 mM NaOH at 45°C . Following precipitation , RNA was resolved on a 12 . 5% urea-polyacrylamide gel , and subject to northern blotting as previously described [56] . 20 μg of total RNA was used for each of reaction . Terminator exonuclease ( epicenter ) was added to one tube and the tube was incubated at 30°C for 60 minutes . After that the reaction RNA was purified by organic extraction protocol [71] . In the second condition , 1 μl CIP ( Calf intestinal phosphatase , NEB ) was added and incubated at 37°C for 30 min . Terminator exonuclease was added followed by a second incubation at 30°C for 60 minutes . Precipitated RNAs were resuspended in loading buffer and resolved on a 12 . 5% urea-polyacrylamide gel , and subjected to northern blotting as previously described [56] . A Methyl DNA seq library was created with Illumina Methyl-seq TruSeq Kit from dust mite DNA recovered by organic extraction followed by precipitation . Using the Bismark algorithm [72] base converted dust mite genome indexes were used to determine the rate of cytosine methylation . Using coordinates from cufflinks ( mRNA ) , and RepeatMasker ( TE ) annotations , rates of methylation were determined for different genomic features . Reads were mapped uniquely and duplicated reads were discarded that resulting in an average 6X coverage depth [72] . Using bedtools , genomic regions that had >4 reads mapping were determined and the base conversion rate measured . Assembled genome was submitted under GenBank ID: NBAF01000000 . Small RNA bioSample accession number is: SAMN05441789 . Datasets of Bi-sulfite sequencing are deposited under the BioSample accession number: SAMN06891248 . Spider mite small RNA datasets used in the study can be accessed at GEO GSE32005 . Drosophila small RNA dataset using in the study can be accessed at GEO GSE83698 .
Investigation of small RNA populations in dust mites revealed absence of the piwi-associated RNA ( piRNA ) pathway . Apart from several nematode and platyhelminths lineages , piRNAs are an essential component of animal genome surveillance , actively targeting and silencing transposable elements . In dust mites , expansion of Dicer produced small-interfering RNA ( siRNA ) biology compensates for loss of piRNAs . The dramatic difference we find in dust mites is likely a consequence of their evolutionary history , which is marked by descent from a parasite to the current free-living form . Our study highlights a correlation between perturbation of transposon surveillance and shifts in ecology .
You are an expert at summarizing long articles. Proceed to summarize the following text: Substantial experimental evidence suggests the cerebellum is involved in calibrating sensorimotor maps . Consistent with this involvement is the well-known , but little understood , massive cerebellar projection to maps in the superior colliculus . Map calibration would be a significant new role for the cerebellum given the ubiquity of map representations in the brain , but how it could perform such a task is unclear . Here we investigated a dynamic method for map calibration , based on electrophysiological recordings from the superior colliculus , that used a standard adaptive-filter cerebellar model . The method proved effective for complex distortions of both unimodal and bimodal maps , and also for predictive map-based tracking of moving targets . These results provide the first computational evidence for a novel role for the cerebellum in dynamic sensorimotor map calibration , of potential importance for coordinate alignment during ongoing motor control , and for map calibration in future biomimetic systems . This computational evidence also provides testable experimental predictions concerning the role of the connections between cerebellum and superior colliculus in previously observed dynamic coordinate transformations . Evidence for cerebellar involvement in map calibration comes from studies of prism adaption in primates [1 , 2] and cerebellar patients [3–6] , and from measurements of human brain activity during adaptation [7 , 8] . This evidence suggests that "the cerebellum is particularly involved in the realignment process that is necessary to re-establish a correct spatial mapping among visuo-motor and sensorimotor coordinate systems" ( [7] , p . 176 ) . Given the ubiquity of map representations in the brain , such involvement represents a very significant new role for the cerebellum . However , although computational studies have indicated how the cerebellum could form internal models of a wide variety of dynamic processes [9–11] , it is unclear how these ideas could be applied to the problem of calibrating maps . One possible mechanism for map calibration is suggested by electrophysiological studies of collicular maps that are used to guide orienting movements . These maps receive information about target location from multiple modalities [12] , and issue motor commands to eyes , head and body depending on the species [13] . In primates and humans the superior colliculus primarily controls saccades that bring the target onto the fovea , and these saccades can be artificially miscalibrated by allowing the target to move during the saccade itself [14] . Accuracy can be relearnt , a process termed saccadic adaptation , provided the relevant region of the cerebellum is intact [15] . Current evidence suggests that the cerebellum can act both downstream of collicular maps , and on the maps themselves [16] , consistent with the massive reciprocal connections between the cerebellum and the superior colliculus [17] . In the case of maps combining visual and auditory information , a problem arises when the eyes do not look straight ahead , since the head-based auditory coordinate frame becomes misaligned with the visual coordinate frame . Recordings from primate superior colliculus indicate that auditory receptive fields are appropriately altered by information about the position of the eyes in the orbit [18] . Similar results were obtained for a combined visual and somatosensory map , when the task was to saccade to a tactile signal delivered to the hand [19] . These results suggest that the superior colliculus receives map-calibration signals that can vary dynamically on a trial-by trial basis . We therefore investigated whether such signals could be in principle be generated by current computational models of the cerebellum . We used as a basic framework the standard ‘chip’ metaphor of cerebellar function , which has been employed to represent the combination of a homogeneous cerebellar cortical microcircuit with individual microzones having unique external connections [11 , 20] . In this framework we constrained the model by requiring the cerebellar microcircuit to be represented in a familiar form , so that the novel feature was the architecture connecting cerebellum and superior colliculus . The familiar form we chose was the basic adaptive filter model of the cerebellar microcircuit [21] , a development of the original Marr-Albus theoretical framework that uses the covariance rule to implement the least mean square learning rule for time-varying input signals . This model has been used successfully in a wide variety of sensorimotor contexts [22] , and here we investigated whether it could be applied without change to the very different computational problem of calibrating a topographic map driving an orienting response . We determined whether the model was capable of acquiring two competencies , first correcting a unimodal map that has become distorted and secondly resolving mismatches between modalities in a multimodal map . In addition , since the algorithm we chose naturally results in maps which are predictive , we examined how the cerebellum could be used to calibrate prediction of the future position on the map of a moving target . This competence has been demonstrated for the auditory tectal map in the owl [23] and a colliculus-related map in cat [24] , and is consistent with the demonstrated role of the superior colliculus or optic tectum in prey catching in a number of species [25–29] . Fig 1 shows in schematic form the architecture for calibration of a unimodal sensory map , in which the adaptive filter learns to produce dynamic modulating inputs to the map that increase its accuracy . The cerebellar cortical microcircuit is modelled as an adaptive filter [21 , 22] . This uses a systems level interpretation , in which each cerebellar microzone has two inputs ( climbing fibre , mossy fibre ) and a single Purkinje cell output . Such a model has previously been applied in a range of sensorimotor contexts [22] . We keep the same model hardwiring ( as described below and in Fig 1A and 1B ) to determine if it can still be applied in the very different context of map calibration . A simplified version of the microcircuit is shown in Fig 1A , in which the mossy-fibre inputs u are recoded in the granular layer to produce parallel-fibre signals pj . These signals influence the simple spike firing z of Purkinje cells via the synapses w . Purkinje cells also a receive a climbing-fibre input e . In the adaptive-filter interpretation of this circuit ( Fig 1B ) processing in the granular layer is represented by a set of fixed filters G1 … GN whose outputs p1 … pN are weighted by w1 … wN where the weights correspond to the efficacies of the synapses between parallel fibres and Purkinje cells . Purkinje cells linearly sum the weighted parallel-fibre signals to produce their simple-spike output z = Σwipi . The climbing-fibre input e acts as a teaching or error signal that alters the weights w1 … wN using the covariance learning rule 𝛿wi = -β<epi> , which corresponds to the Least Mean Square learning rule [30] . In this form of supervised learning the weights are altered until correlations between presynaptic inputs and output error are removed [30 , 31] , hence the term decorrelation learning [32] . A compact schematic of the adaptive filter ( Fig 1C ) is used in subsequent diagrams . In the simplest version of the architecture the superior colliculus was represented by a single topographic map ( Fig 1D ) . Target locations xd = ( xd , yd ) are selected from within a two dimensional grid , then transformed into sensor data which is written into the collicular map , modelled as a square grid with each grid point corresponding to a collicular neuron . The sensor data are generated by a linear sensor model sd = Kxd , where K is a 2 x 2 matrix that defines the sensor model and is determined from the sensor scaling , noise level and rotation of target ( e . g . [33] ) . The sensor data are then written into the topographic collicular map to provide a distributed representation of the target location ( Fig 1D ) . Neurons in the map had receptive field centres ( xi , yj ) , so that if only an individual neuron fired , it would produce an orienting response to the real-world location ( xi , yj ) . It assumed here that the map’s connections to the motor system are fixed , and that neuron centres are assumed to be dense enough to code the target location accurately . A 2D elliptical Gaussian function was used to provide the distributed target position which when sent to the motor system generates an orienting response to the estimated position of the target . For an accurate map this corresponds to the actual position of the target xd , thus bringing the target onto the fovea ( in primates ) or the area of the mouth ( in rodents ) . The two components are calculated from the distributed firing rates of the collicular neurons ( details in Materials and Methods ) . The distributed collicular response is also sent to the cerebellum as mossy-fibre input , where it is processed in the granular layer to produce a coarse coded map carried by the parallel fibres ( Fig 1D ) . Coarse coding was used to provide a sparser representation to ensure both an acceptable speed of learning and an acceptable degree of precision . An evenly spaced k by k grid of Gaussian receptive fields , GPn ( where n denotes the nth Gaussian in the k by k grid ) was used to coarse code the topographic map . The activity of each grid point was found by multiplying each Gaussian receptive field by the topographic map activity and summing and normalising . When the collicular map is correctly calibrated , the target positions estimated by the map are accurate , and so are the orienting movements it generates . In the absence of orienting errors the climbing fibres to the cerebellum will not carry any error signals , and the weights between parallel fibres and Purkinje cells will stay fixed . When the collicular map is inaccurate it generates an erroneous estimate xg = ( xg , yg ) of the actual target location xd = ( xd , yd ) so that the resulting orienting movement will be in error ( e = xd−xg ) . This would be foveation error in the case of saccade generation , or a tactile signal provided by micro-vibrissae in the case of rodent prey acquisition . The cerebellum receives a corresponding error signal via climbing fibres , a signal assumed here to be signed and two dimensional , with axes approximately aligned with horizontal and vertical ( x and y directions ) . This error signal is used to adjust the weights of the synapses between parallel fibres and Purkinje cells so that the output to the superior colliculus sent from the cerebellar cortex via the deep cerebellar nuclei biases the collicular map in order to shift the position of peak map activity ( Fig 1D ) . The simplest way for the cerebellum to act on a topographic map is to assume a 2D output z which is fed to all neurons in the map and biases their centre position . That is , for a given sensory map , a cerebellar bias input z to a target neuron with centre x will make it act as though it has centre x+z . In effect cerebellar input ‘slides’ map activity across the map by an amount z = ( δx , δy ) . We therefore assume there are 2 biasing microzones for each sensory map , so that map activity can be shifted independently in 2 dimensions . Using a global map shift is a simplification that can be applied when considering single targets . For multiple targets , different regions of the map are likely to require shifting by different amounts . To achieve this , the map could be split into different regions , calibrated by a separate cerebellar zones . We consider single targets to avoid overcomplicating the problem . We use the notation xa = ( xa , ya ) to denote the adjusted target location xa = xg+z . Subsequent orienting errors are calculated from the shifted estimated location e = xd−xa = ( ex , ey ) ( further details in Fig 1 ) . The bias signal is generated as follows . A weight is associated with each parallel fibre signal . The cerebellar weights to bias the map in the x—and y–directions are learnt from initial values of zero . As indicated above , the learning rule is given by Δwx = −β ex P , Δwy = −β ey P , where ex and ey are the errors , P the coarse coded parallel fibre signals , and β is a learning rate . In the first problem we asked the cerebellar-collicular architecture described above to calibrate a unimodal map ( green grid in Fig 2A , left-hand panel ) that had been distorted as a result of sensor changes ( red grid ) . The nature of the distortion varied with stimulus location , as indicated by the arrows which show the changes to the map that are needed to restore its accuracy . The sensory map after 3000 trials of cerebellar recalibration ( blue dashed grid ) is shown in the centre panel of Fig 2A , and is very substantially restored to its undistorted from . The right hand panel shows the combined learnt weights in the x- and y-directions corresponding to each coarse coded set of parallel fibre signal ( weights initially zero ) . The time course of the recalibration is shown in Fig 2B , which plots the RMS error of the orienting response against number of stimulus presentations . The impact of learning maps with a low quality error signal was also investigated by testing a version of the learning rule that simply used the sign of the error signal . Learning with the full signal ( Fig 2B ) gave RMS errors with mean 0 . 008 over the last 2500–3000 iterations . When the sign of the error was used this was increased to 0 . 015 . However , both signals substantially restored the map to the undistorted form . The model is robust to reductions in the quality of the error signal , even if it is sign only , learning is little affected . The details of dynamic recalibration for a particular target location are illustrated in Fig 2C . The shift needed to restore response accuracy to this location is shown as a red arrow on the collicular map image in the left panel . The coarse-coded , normalised parallel fibre signals generated by the inaccurate target location are shown in the centre panel ( cf . Fig 1D ) . At the start of recalibration , each of the weights of these signals ( corresponding to the efficacy of the corresponding synapses on Purkinje cells ) were zero . After learning the weights had changed to produce a cerebellar output that shifted the map appropriately ( Fig 2C , right-hand panel ) . It is important to emphasise that recalibration by the architecture described above is a dynamic process since the cerebellar bias signal depends on the current target position . This means that , although the whole map receives the same bias signal , the bias signal changes according to the position of the target . The parallel-fibre representation used here contains enough terms to allow affine recalibrations . In general the complexity of possible re-calibrations depends only on the completeness of the parallel-fibre representation , e . g . radial basis function inputs could generate very general calibrations . The superior colliculus has both unimodal and multimodal maps ( e . g . [34] ) . In the example illustrated in Fig 3 , information from a visual and a somatosensory map are combined into a multimodal map that drives the orienting response . If one or both unimodal maps are distorted , the output of the multimodal map produces an inaccurate orienting response . The problem is to use this error information to calibrate all three maps . The architecture used to address this problem ( Fig 4A ) was an extension of that used for calibrating a single map ( Fig 1 ) . For the case of two sensors we assume two sets of PCs , where each set consists of an x- bias and y-bias PC . Writing undistorted sensory data into each map used linear sensor models as before , where the sensory signals were generated from target locations xd by s1d = K_1xd , s2d = K_2xd . Both K_1 and K_2 were set to the same value to simplify the simulation . The sensor data were then written into the topographic collicular map to provide a distributed representation of the target location as previously , using identical 2D elliptical Gaussian functions . The outputs of the unimodal maps were combined to generate the multimodal map using element by element multiplication of the individual multimodal maps , a method that implements Bayes’ rule ( Materials and Methods ) . Copies of the distributed neuronal responses in the unimodal maps were also sent to the cerebellum as parallel-fibre inputs ( Fig 4A ) . Coarse-coded parallel-fibre signals for each map were generated as before , with the same values for the parameters for each set . The total parallel fibre signal P is thus a vector consisting of the values of P1 at each grid point and P2 at each grid point . When one or both maps were distorted , the output of the multimodal map produced an inaccurate orienting response . In the first method tried for calibrating the unimodal maps , this erroneous response was used to bias the unimodal maps , just as in Task 1 where there was only a single map . Application of this simple method revealed a fundamental calibration ambiguity . Since estimated target position is a weighted combination of individual map estimates , multiple sensors can be miscalibrated in such a way that their combined errors cancel on average ( Fig 5A ) . In principle this ambiguity can be resolved if the sensors have varying accuracies , because the relative weightings of different sensors will vary so that cancellation cannot be exact . However , the learning architecture above cannot utilise this information about sensor accuracy , because all sensor calibration modules are trained by the same error signal ( from the combined , single map ) and so any behavioural error is necessarily attributed to all sensors . This generates a credit attribution problem: since any error is attributed to all sensors , a sensor is forced to learn even when it is accurate ( Fig 5B ) . The required teaching signal , calculated theoretically by the method of gradient descent , is target error inversely weighted by sensor accuracy . But even in simple cases this requires detailed information about sensor accuracy to modulate the target error signal , and is therefore biologically implausible . A more plausible solution would use available sensory signals as teaching signals . Map calibration is often regarded as a static , target independent process . The architecture used here , however , implements a dynamic process since the cerebellar bias signal depends on the current target position . This means that , although the whole map receives the same bias signal , the bias signal changes according to the position of the target . This allows position dependent curvilinear recalibration using a single biasing output as illustrated in Figs 1 and 2 . The dynamic formulation turns also leads to a natural implementation of predictive calibration . This is because in the adaptive filter the granular layer is assumed to act as an information processing reservoir , so that the parallel fibres carry information not only about current mossy fibre inputs , but also about the history of those inputs [22] . If we idealise this process by adding further parallel fibre inputs to the biasing microzones which contain the coarse coded map information filtered by leaky integrators at a range of time scales , then , in the presence of delay in either the sensory or motor systems , the adaptive filter learns to predict target position so as to acquire the target accurately . Fig 7A illustrates this predictive architecture and Fig 7B , 7C and 7D show the results when applied to a target which moves along a smooth curve ( Methods ) whose position is both distorted by miscalibration and delayed by sensory processing with respect to the raw sensory input . The algorithm can be seen to successfully reduce mean square acquisition error ( Fig 7C ) , and both remove the distortion and shift the target peak at its predicted position ( Fig 7D ) . There are two time scales involved in the calibration process . Learning the weights ( or corrections ) is relatively slow and takes place over many iterations . Once the weights are learnt then the application of the corrective signals during dynamic behaviours is fast . This is only possible because the target motion is predictable; in effect the cerebellum learns an internal dynamic model of target behaviour and uses it to predict future positions . Fig 7 shows that this internal model is optimally adapted to the statistics of the target behaviour , which in this case were bandpassed white noise trajectories chosen as an example of a stochastic motion with an adjustable level of predictability . If the target trajectory only contains low frequencies then prediction is more accurate and uses a simpler internal model based on fewer filter inputs . When higher frequency components are present the trajectory is less accurately predictable and requires a more complex internal model utilising a larger range of filter time-scales . Similar predictive shifts in target position have been observed experimentally , for example in the map of auditory space found in the optic tectum of the barn owl [23] . The optic tectum is homologous to the mammalian superior colliculus , and is used by the barn owl to generate orienting movements required for prey capture ( Fig 8A ) . If the prey is moving , then the orienting response must be directed to its predicted not current location , requiring a shift in tectal receptive fields . The nature of such shifts in response to horizontal stimulus movement was examined by manipulating the cue used for localising horizontal position , namely interaural time difference ( ITD ) using dichotic presentation of sounds through earphones . Sound presentation corresponding to a stimulus location moving at constant velocity elicited receptive field changes corresponding to predicted location ( Fig 8B , 8C and 8D ) . Consistent with this interpretation , the size of the receptive-field change increases with ( virtual ) stimulus velocity ( Fig 8D ) . The changes are consistent with a predictive time-lead of ~100 msec , which corresponds to the time taken to complete saccadic gaze shifts produced by electrical stimulation of the tectum [37] . The predictive recalibration architecture ( Fig 7A , with simulation parameters provided in the Methods ) was able to reproduce this pattern of changes ( See Fig 1 in [23] for experimental results ) . Here no sensor distortion was applied , so the algorithm just learns to account for the delay between the estimated and actual target location . The time scales of the experimental and simulated results differ , however the simulation is not intended to replicate the experiment , but to demonstrate that the adaptive cerebellar filter is able to explain the behaviour seen . Note that an even better correspondence could be obtained if evidence accumulation was added to the salience map write-mechanism , so that sensor inputs were optimally combined over time in the map . This would result in a tighter bound on target location over time , mimicking the behaviour seen in the experimental data . Both the cerebellum and superior colliculus have been implicated in saccadic adaptation [14] . The precise nature of that involvement has proved difficult to identify , because saccadic adaptation has turned out to be more complex than it originally appeared , with evidence for different mechanisms being involved depending on whether the adaptation is gain-up or gain-down , short term or long term , or of reactive or voluntary saccades ( e . g . [39 , 40] ) . It appears that sensory remapping is likely to be involved in the gain-up adaptation of reactive saccades , and more generally in the adaptation of voluntary saccades ( e . g . [41] ) . In the former case it seems likely that the altered map is within the superior colliculus [16 , 33] , whereas for the latter spatiotopic cortical maps appear to be implicated [42 , 43] . A possible anatomical basis for dynamical cerebellar remapping of maps in the superior colliculus is the extensive projection from the deep cerebellar nuclei to the superior colliculus [17] . However , little is known about the signals sent by these projections , though it has been suggested they may be “involved in correlating the modality maps within the SC” ( [17] , p . 352 ) . There is evidence for tonic excitatory inputs in anaesthetised rats [44–46] that directly influence collicular sensory cells , and affect movements resembling pursuit , but how that influence works during normal behaviour is not understood . There is good evidence that the cerebellum is involved in the sensory remapping that occurs in prism adaptation [1–5] . The location of the recalibrated maps is however unclear , though event-related FMRI implicates the superior temporal cortex [7] . Adaptation of voluntary saccades has been argued to be similar to prism adaptation [41 , 43] and also appears to involve alterations of maps in higher level frameworks than the retinotopic maps in the superior colliculus . The basic framework for map recalibration proposed here should in principle work for such higher-level maps . A necessary requirement for this is the existence of a recurrent architecture involving cerebral cortex rather than the superior colliculus . Evidence for such an architecture connecting multiple cerebellar and cortical areas has been summarised by Ramnani [47] . Overall , the biological evidence appears to be consistent in broad terms with the map calibration scheme proposed here . The next step is to consider more detailed evidence , that could be provided by testing specific predictions generated from the present results . As mentioned in the Introduction , accurate saccades to auditory targets can be made when the eyes are in an eccentric starting position , causing auditory and visual maps to become misaligned [18] . The scheme investigated here predicts that saccadic accuracy to auditory stimuli in this situation will be severely impaired after selective inactivation of cerebellar inputs to the superior colliculus , or of collicular outputs to the cerebellum . It also predicts that this impairment will be accompanied by a loss of the shift in auditory receptive fields that normally results from change in eye position , again as demonstrated by Jay and Sparks [18] Accurate saccades can also be made to somatosensory targets ( stimulation delivered to the hands which are not visible ) from different starting positions of the eye [19] . We again predict that saccadic accuracy to these somatosensory stimuli under these conditions will be severely impaired after selective inactivation of connections between cerebellum and superior colliculus , and that this impairment will be accompanied by a loss of the shift in somatosensory receptive fields that normally results from change in eye position [19] . Finally , owls are able to capture moving prey , an ability connected with predictive shifts in the receptive field of auditory neurons in the optic tectum [23] . We predict that selective inactivation of connections between the cerebellum and optic tectum will seriously affect the ability to capture moving prey , and abolish the predictive shifts in auditory receptive fields . The recalibration mechanisms investigated here may have application to the generic problems of realigning collicular maps when the body moves that were outlined in the Introduction . In the absence of a recalibrating input auditory and visual maps would become misaligned when the head moves ( e . g . [18] ) , as would tactile and visual maps when the hands move ( e . g . [19] ) . Dynamic recalibration appears to be particularly useful for such problems , and a role for the cerebellum is suggested by consideration of the computational complexities of determining target position in eye-centred coordinates of a tactile target delivered to a hand . “If the stimulus is delivered to the finger , the angles of the finger joints , wrist , elbow , shoulder , neck and eyes must be known … a neural implementation of a multi-dimensional lookup table with indexes for all the intervening joint angles could convert stimulus position from body-centred space to eye-centred space” ( [19] , p . 450 , p . 450 ) . Dynamic coordinate alignment is crucial for motor coordination in multi-jointed animals , and its implementation by the cerebellum could greatly simplify higher-level motor control . One suggestion for future work would be to investigate to what extent the tactile/visual map exemplar could be considered as ( or rephrased as ) an eye-position/retinotopic . Finally , dynamic recalibration might also prove useful for biomimetic control schemes in robotics . The adaptive-filter model of the cerebellum has been applied to a number of robot control problems , including plant compensation [48 , 49] and the reafference problem [50] . Preliminary results suggest that adaptive-filter based dynamic remapping can be utilised with a robotic platform to improve the accuracy of orienting responses [51] . More generally , the dynamic coordinate transformations referred to above are also required for control of multijoint robots , and it is possible the scheme investigated here could be useful in that context . When the collicular map is not correctly calibrated , the estimated target position xg will differ from the actual location xd , and the orienting movement will be in error ( e = xd−xg = ( ex , ey ) ) . The cerebellum receives a corresponding error signal via climbing fibres , a signal assumed here to be signed and two dimensional with axes approximately aligned with horizontal and vertical ( x and y directions ) , which is used to adjust the weights associated with each parallel fibre signal Δwx=−βexP Eq ( 12 ) Δwy=−βeyP where ex and ey are the error components , P the coarse coded parallel fibre signals , and β is a learning rate here set to 1 . The initial value of the weights was zero . The learnt weights were used to bias the map in the x—and y–directions by generating a cerebellar signal ( δx , δy ) corresponding to the sum of the weighted parallel fibre signals δx=∑wxP Eq ( 13 ) δy=∑wyP The cerebellar bias signal in effect slides map activity across the map by an amount ( δx , δy ) . Sensory maps can be used for the pursuit of moving targets . We therefore examined whether the proposed role of the cerebellum in calibrating a unimodal sensory map using stationary targets ( Fig 1 ) could be extended to pursuit . For moving targets delays in sensory processing ( for example in the retina ) become important , because the map no longer has access to the current target location x ( T+Δ ) ( where Δ is the delay and T the trial number , Fig 7A ) but only to its delayed location x ( T ) . In addition the error signal is no longer the difference between current estimated and actual target locations , but between current estimated location and actual location Δ times steps earlier ( Fig 7A ) . To solve this calibration problem the system must learn to predict future target location , hence the term predictive recalibration . The parallel-fibre signals from map to cerebellum now conveyed temporal information , required for the prediction of target trajectories . The new temporal signals were generated by a bank of fixed temporal filters ( Fig 7A ) . Incorporating fixed filters increases the number of parallel fibre signals and corresponding weights to adjust , but does not change the rest of the algorithm .
The human brain contains a structure known as the cerebellum , which contains a vast number of neurons–around 80% of the total ~90 billion . We believe the cerebellum is involved in learning motor skills , and so is vitally important for accurately controlling the movements of our body , amongst other things . However , like most regions of the brain , we still do not fully understand the role of the cerebellum and evidence for new roles is appearing all the time . One such new role is in the calibration of sensorimotor maps in the brain that link our sensory perception to motor function , such as when a visual stimulus causes a redirect of our gaze . We investigated this problem by connecting a mathematical model of the cerebellar cortical microcircuit to simulated sensory maps in the superior colliculus that are used to control orienting movements . We found the error signal generated by inaccurate orienting movements could be used to accurately calibrate sensorimotor maps , and to allow predictive tracking of moving targets . This finding points to a potentially widespread role for the cerebellum in calibrating the sensorimotor maps that are ubiquitous in the brain and could prove useful in controlling the movements of multi-joint robots .
You are an expert at summarizing long articles. Proceed to summarize the following text: Germ granules , termed P granules in nematode C . elegans , are the germline-specific cytoplasmic structures widely observed from worms to humans . P granules are known to have critical functions for postembryonic germline development likely through regulating RNA metabolism . They are localized at the perinuclear region of germ cells during most of the developmental stages . However , the biological significance of this specific localization remains elusive . PGL-1 and PGL-3 , the defining components of P granules , were shown to be lost from the perinuclear region prior to germ cell apoptosis . Furthermore , this loss was shown to be significantly enhanced upon DNA damage . Here , we show that the removal of PGL-1 and PGL-3 from the perinuclear region following UV-induced DNA damage is significantly reduced in autophagy mutants . Autophagy was previously shown to be required for DNA damage-induced germ cell apoptosis . We show that the apoptosis defect of autophagy mutants is bypassed by depletion of pgl-1 or pgl-3 . These findings are consistent with time-lapse observations of LGG-1 foci formation , showing that autophagy is activated following UV irradiation and that maximal accumulation of LGG-1 foci occurs before PGL-1 removal . We also show that some of the autophagy genes are transcriptionally activated following UV irradiation by CEP-1 , the worm p53-like protein . Taken together , our results indicate that autophagy is required to remove the major P granule components , PGL-1 and PGL-3 , and that their removal is required for the full induction of DNA damage-induced germ cell apoptosis . Our study contributes to a better understanding of germ cell apoptosis , a process that leads to the elimination of the vast majority of germ cells in various animals from worms to mammals . During mammalian germline development , the vast majority of germ cells are eliminated by apoptosis [1–5] . Studies in mouse models demonstrated that germ cell apoptosis following DNA damage requires the activities of the DNA damage response pathway and the p53 family members [6 , 7] . The majority of germ cell apoptosis occurs during the pachytene stage in meiotic prophase I , in which failure of meiotic recombination and perturbation in chromosome structure are detected . The Caenorhabditis elegans germ line provides a model system to study the apoptotic elimination of germ cells [8] . In C . elegans , the germ cell apoptosis is restricted to female germ cells residing in the late pachytene stage . A basal level of germ cell apoptosis termed “physiological germ cell apoptosis” can be observed under standard culturing conditions [8] . Upon DNA damage or meiotic recombination failure , the germ cell apoptosis is further induced [9] . This type of germ cell apoptosis is called “DNA damage-induced germ cell apoptosis” . As does somatic programmed cell death , all types of germ cell apoptosis require the activities of both CED-4/Apaf-1-like adaptor protein and CED-3 caspase for the execution [10 , 11] . Germ cell apoptosis is also negatively regulated by CED-9 , an anti-apoptotic protein homologous to Bcl-2 [8 , 9] . DNA damage-induced germ cell apoptosis in C . elegans requires the activation of multiple proteins involved in the DNA damage checkpoint pathway [9] , including CEP-1 , the sole C . elegans homolog of the mammalian p53 tumor suppressor family composed of p53 , p63 , and p73 [12] . DNA damage-induced germ cell apoptosis also requires the two pro-apoptotic BH3-only proteins , EGL-1 and CED-13 , which are thought to promote apoptosis by directly antagonizing CED-9 [9 , 13 , 14] . EGL-1 and CED-13 are not required for physiological germ cell apoptosis . Germ granules are germline-specific non-membrane-bound ribonucleoprotein organelles , which are observed in various animals from worms to humans [15 , 16] . Germ granules are considered to play pivotal roles in the formation or maintenance of germ cells . In C . elegans , germ granules are also called P granules [17] . P granules are considered to regulate RNA metabolism of germ cells; most of the protein components so far identified contain RNA-binding motifs [18 , 19] . In germ cells , P granules are localized at the cytoplasmic side of the nuclear envelope by associating with clusters of nuclear pores during most of the developmental stages [20–23] . It was postulated that P granules localize to the perinuclear region to control the transport of proteins and mRNAs between the nucleus and the cytoplasm [24 , 25] . In previous studies , we identified PGL-1 and PGL-3 as the major constitutive components of P granules [26 , 27] . The presence of PGL-1 and PGL-3 , especially that of PGL-1 , is essential for the functions of , and assembly of other components to , P granules [28–30] . In this study , we use PGL-1 and PGL-3 as markers for intact P granules . In C . elegans wild-type adult hermaphrodite gonads , although the majority of germ cells contain both PGL-1 and PGL-3 at their nuclear periphery as constitutive components of P granules , a few PGL-absent germ cells are also constantly observed in the pachytene region of the gonads under physiological conditions [21] . In a previous study , we found that the number of PGL-absent germ cells is significantly increased following UV irradiation [31] . In addition , under both physiological and DNA-damaged conditions , gonadal sheath cells selectively engulfed germ cells lacking PGL proteins , indicating that PGL-depleted germ cells are apoptotic cells and that the removal of PGL-1 and PGL-3 from germ cells occurs concomitant with germ cell apoptosis [31] . PGL-depleted germ cells are selectively committed to apoptosis because these cells do not efficiently retain SIR-2 . 1 in the nucleus [31] . SIR-2 . 1 is a C . elegans homolog of the Sirtuin family , and the translocation of SIR-2 . 1 from the nucleus to the cytoplasm has been correlated with the induction of germ cell apoptosis upon DNA damage [32] . Macroautophagy ( hereafter referred to as autophagy ) is a ubiquitous intracellular degradation process conserved among eukaryotes including C . elegans [33 , 34] . Autophagy sequesters cytoplasmic materials including organelles into a double-membrane vesicle termed autophagosome , which subsequently fuses with the lysosome to degrade the sequestered materials . The autophagic process can be dissected into several distinct steps , which include ( 1 ) induction , ( 2 ) cargo selection and packaging , ( 3 ) vesicle nucleation , ( 4 ) vesicle expansion and completion , ( 5 ) retrieval of autophagy proteins from vesicle , and ( 6 ) vesicle targeting , docking , and fusion with the lysosome [35] . Autophagy-related genes or Atg genes that act at respective autophagic steps have been identified mainly through genetic screens using yeast Saccharomyces cerevisiae . The C . elegans genome has orthologs for many of the yeast Atg genes [34] . It has been shown that four enzymatic complexes are involved in the formation of autophagosome ( Fig 1A table ) [34] . A serine/threonine protein kinase complex , which includes UNC-51 and ATG-13 , induces autophagic activity . A class III phosphatidylinositol 3-kinase complex , which includes BEC-1 and VPS-34 , acts for vesicle nucleation . Two ubiquitin-like conjugation pathways , including ATG-3 , ATG-4 , ATG-7 , and LGG-1 , bring about vesicle expansion and completion . Furthermore , a protein retrieval system , including ATG-2 , ATG-9 , and ATG-18 , recycles autophagy proteins . In addition , some of the EPG ( Ectopic P Granules ) gene products , including EPG-2 , EPG-11 , and SEPA-1 , are involved in cargo selection and packaging [36–38] . Autophagy is involved in multiple biological processes during C . elegans development [39 , 40] . For example , autophagy selectively degrades sperm-derived paternal mitochondria and membranous organelles in newly fertilized embryos [41–43] . Autophagy is also required for the survival of newly hatched L1 larvae upon starvation [44] , for the development of dauer larvae , and for lifespan extension [45] . Autophagy is further required to maintain the number of mitotically dividing germ cells in the distal region of gonads during larval development [46] . Furthermore , autophagy modulates several miRNA-mediated processes by downregulating components of RNA-induced silencing complex [47] . Notably , in relation to this study , it was reported that autophagy mediates the degradation of PGL-1 and PGL-3 that are missegregated to somatic blastomeres during embryogenesis [48] . Autophagy and apoptosis are often simultaneously activated during development and in response to stress [49–51] . However , molecular mechanisms that link the two degradation processes are still elusive . It was previously reported that loss of BEC-1 , the C . elegans ortholog of Atg6/Vps30/Beclin 1 , a key regulator of autophagy , increased the number of apoptotic cells in both the soma and the germ line [52] . BEC-1 was also shown to physically interact with the anti-apoptotic protein CED-9 [52] . Therefore , BEC-1 may also function as a regulator of apoptosis by interacting with CED-9 . However , this apparent increase in the number of apoptotic germ cells in BEC-1-depleted hermaphrodite gonads could have been caused by a clearance defect of engulfed germ cell corpses in somatic gonadal sheath cells rather than by a bona fide increase in the number of germ cells undergoing apoptosis , because BEC-1 was also shown to be required for endocytosis in various somatic cells including the sheath cells [53] . Furthermore , depletion of another key endocytosis regulator VPS-34 , the class III phosphatidylinositol 3-kinase that associates with BEC-1 , also caused an apparent increase in the number of apoptotic germ cells under physiological conditions [53] . Therefore , further analysis is required to clarify whether BEC-1 indeed functions as a regulator that links autophagy to apoptosis . Another possible link between autophagy and apoptosis was reported to occur during DNA damage-induced germ cell apoptosis . That is , it was shown that autophagy is required for the full induction of germ cell apoptosis upon DNA damage [54] . However , it has not been elucidated how autophagy amplifies germ cell apoptosis following DNA damage . Here , we show that some of the autophagy genes are transcriptionally upregulated following UV-induced DNA damage , and that this upregulation requires CEP-1 . We show that DNA damage-induced autophagy removes PGL-1 and PGL-3 from a substantial number of germ cells , which leads to increase in the level of germ cell apoptosis . Our results suggest the presence of a novel mechanism that links autophagy to apoptosis , which is required for efficient induction of germ cell apoptosis following DNA damage . To examine a possible link between autophagy and germ cell apoptosis , we first examined the levels of germ cell apoptosis in various autophagy mutants under physiological conditions ( Fig 1 ) . Apoptotic germ cells are observed in the pachytene region of adult hermaphrodite gonads ( Fig 1A ) . To detect apoptotic germ cells , we used acridine orange ( AO ) vital staining as previously described [31 , 55] . Because four distinct steps are involved in the formation of autophagosome ( Fig 1A ) [33 , 34] , we included the following homozygous viable autophagy mutants , which function in respective autophagic steps , in the apoptosis analysis: atg-13 ( bp414 ) and atg-9 ( bp564 ) functioning in the induction step ( I ) , epg-8 ( bp251 ) in the nucleation step ( II ) , atg-3 ( bp412 ) , atg-4 . 1 ( bp501 ) , and atg-4 . 2 ( tm3948 ) in the elongation step ( III ) , atg-2 ( bp576 ) and atg-18 ( gk378 ) in the retrieval step ( IV ) , and epg-5 ( tm3425 ) and epg-9 ( bp320 ) , which function in other steps ( Fig 1A; S1B Fig ) . We found that four of the autophagy mutants , atg-13 ( bp414 ) , atg-9 ( bp564 ) , atg-4 . 1 ( bp501 ) , and atg-4 . 2 ( tm3948 ) , showed higher levels of germ cell apoptosis than wild-type N2 under physiological conditions ( Fig 1B , p < 0 . 05 ) . Physiological germ cell apoptosis can be increased by ectopic activation of DNA damage checkpoint pathway [56] , and this signal is mediated by CEP-1 , the sole C . elegans homolog of the mammalian p53 tumor suppressor family [12 , 13] . Therefore , we tested whether the increased physiological germ cell apoptosis in the four autophagy mutants is suppressed by depletion of CEP-1 . We found that although an increase in germ cell apoptosis in wild-type N2 following UV irradiation was significantly suppressed by RNAi depletion of cep-1 , increased physiological germ cell apoptosis in the four autophagy mutants was not reduced to the N2 level after RNAi depletion of cep-1 ( Fig 1C ) . Therefore , we consider that the increase in physiological germ cell apoptosis in these autophagy mutants was not caused by ectopic activation of DNA damage checkpoint pathway . To evaluate whether there is a direct correlation between the levels of physiological germ cell apoptosis and the levels of autophagy activity in these autophagy mutants , we quantified the number of LGG-1 foci in the pachytene region of their gonads after immunostaining with anti-LGG-1 antibody ( S1A Fig ) . LGG-1 , the C . elegans ortholog of ATG8/LC3 , is a commonly used autophagy marker , which forms cytoplasmic foci when autophagy is activated [34] . We found that none of the autophagy mutants we examined formed any LGG-1 foci in the pachytene region of their gonads under physiological conditions ( S1C Fig ) . This result indicates that the level of physiological germ cell apoptosis in autophagy mutants is not directly regulated by their autophagy activity . It was previously reported that apparent increase in the level of physiological germ cell apoptosis after depletion of bec-1 or vps-34 was more likely caused by a clearance defect of engulfed germ cell corpses in somatic gonadal sheath cells rather than by a bona fide increase in germ cell apoptosis [53] . Therefore , although some autophagy mutants show higher levels of germ cell apoptosis than wild type under physiological conditions , this is likely caused by a mechanism that is related to but different from autophagy . It was previously reported that autophagy mutants failed to increase the level of germ cell apoptosis following DNA damage [54] . We confirmed this result ( Fig 2 ) . First , germ cell apoptosis was increased substantially following UV irradiation compared with non-irradiated conditions in wild-type N2 , pgl-1 mutant , and pgl-3 mutant adult hermaphrodites , as previously described ( Fig 2 ) [9 , 10 , 31] . Second , in contrast to N2 , pgl-1 mutant , and pgl-3 mutant , the levels of germ cell apoptosis were not significantly changed following UV irradiation compared with non-irradiated conditions in respective single autophagy mutants , as previously reported ( Fig 2 ) [54] . We further examined whether the levels of germ cell apoptosis in respective autophagy mutants are affected by depletion of pgl-1 or pgl-3 because of the following reasons . First , we previously found that removal of PGL-1 and PGL-3 from germ cells was significantly increased in N2 adult hermaphrodite gonads following UV irradiation ( S2 Fig ) , and that this removal of PGL proteins contributed to increase germ cell apoptosis upon DNA damage [31] . Second , it was previously reported that autophagy mediates the degradation of PGL-1 and PGL-3 when they are missegregated to somatic blastomeres during embryogenesis [48] . We found that the levels of germ cell apoptosis were significantly increased following UV irradiation compared with non-irradiated conditions in all the autophagy mutants when pgl-1 or pgl-3 was simultaneously depleted by either RNAi or mutation ( Fig 2 , p < 0 . 05 compared with respective single autophagy mutants ) . Our results suggest that the failure of autophagy mutants to increase germ cell apoptosis following DNA damage is caused , at least in part , by their failure to remove PGL-1 and/or PGL-3 from germ cells upon DNA damage . To examine whether the removal of PGL-1 and PGL-3 from germ cells upon DNA damage is indeed compromised in autophagy mutants , we irradiated or not irradiated adult hermaphrodites of N2 and several autophagy mutants with UV , and immunostained their dissected gonads with anti-PGL-1 and anti-PGL-3 antibodies ( Fig 3 ) . First , we confirmed that both PGL-1 and PGL-3 were removed from a large number of germ cells in the pachytene region of UV-irradiated N2 adult hermaphrodite gonads , in contrast to non-irradiated N2 hermaphrodite gonads ( Fig 3A , white lines; also see S2 Fig ) . Second , we found that compared with N2 , removal of PGL-1 and PGL-3 from germ cells following UV irradiation was significantly reduced in adult hermaphrodite gonads of all the autophagy mutants examined , including atg-4 . 1 ( bp501 ) , atg-9 ( bp564 ) , atg-13 ( bp414 ) , atg-18 ( gk378 ) , and epg-5 ( tm3425 ) , in which only a small number of germ cells lost PGL-1 and PGL-3 ( Fig 3A , white arrowheads ) . The above observations were confirmed by the quantification of the number of PGL-absent germ cells in the pachytene region ( Fig 3B ) . We further examined whether the protein level of PGL-1 in adult hermaphrodites is also reduced following UV irradiation in an autophagy activity-dependent manner , by western blot analysis of N2 and autophagy mutant adult hermaphrodites using anti-PGL-1 antibody ( Fig 3C ) . We found that the protein level of PGL-1 was indeed significantly reduced in N2 adult hermaphrodites following UV irradiation compared to non-irradiated condition ( Fig 3C , p < 0 . 001 ) . In contrast , PGL-1 protein level was less significantly reduced in atg-4 . 1 ( bp501 ) adult hermaphrodites ( p < 0 . 05 ) and not significantly reduced in atg-18 ( gk378 ) adult hermaphrodites ( p > 0 . 05 ) following UV irradiation compared to non-irradiated condition ( Fig 3C ) . These results indicate that at least a portion of PGL-1 protein is degraded following UV irradiation in an autophagy activity-dependent manner in adult hermaphrodites . Therefore , autophagy is most likely involved in the removal and/or degradation of PGL proteins in adult hermaphrodite gonads in response to DNA damage . To directly examine whether autophagy is indeed activated following DNA damage in the pachytene region of adult hermaphrodite gonads , in which germ cell apoptosis takes place , we observed the formation of LGG-1 foci in the pachytene region in a time-course following UV irradiation ( Fig 4 ) . LGG-1 forms cytoplasmic foci when autophagy is activated [34] . We treated N2 or GFP::LGG-1 transgenic animals , which express GFP::LGG-1 in the germ line [41] , with UV irradiation . Subsequently , a subset of these animals was collected every few hours , and their gonads were immunostained with either anti-LGG-1 [41] or anti-GFP antibody along with anti-PGL-1 antibody co-immunostaining and DNA counterstaining ( Fig 4A , 4B and 4D ) . In both N2 and the GFP::LGG-1 transgenic adult hermaphrodites , we found that LGG-1 foci started to appear soon after UV irradiation , and the foci formation was maximal at 3 h after irradiation in the cytoplasm , and occasionally around the perinuclear region , of germ cells in the pachytene region of their gonads ( Fig 4A and 4B , white and yellow arrows ) . The above observation was confirmed by the quantification of the number of LGG-1 foci formed in the pachytene region of gonads with or without UV irradiation ( Fig 4C ) . The formation of LGG-1 foci in the cytoplasm of pachytene-stage germ cells following UV irradiation was also successfully observed by time-lapse live imaging of a hermaphrodite carrying an integrated Ppie-1::GFP::lgg-1 transgene , after simultaneous depletion of asp-10 , vha-5 , and vha-13 to suppress quick turnover of LGG-1 foci via reducing the activities of lysosomal enzymes ( S3 Fig ) [57] . We also observed that removal of PGL-1 from germ cells became prominent following the peak of LGG-1 foci formation , around 3–6 h after UV irradiation ( Fig 4A and 4B , arrowheads ) . In contrast , in adult male gonads , neither UV-induced LGG-1 foci nor PGL-1 depletion was observed in the pachytene region , consistent with the absence of apoptosis in the male germ line ( Fig 4D ) . Our results indicate that DNA damage induces autophagy in hermaphrodite pachytene-stage germ cells before the massive degradation of P granules becomes evident . We examined the levels of LGG-1 foci in various autophagy mutants following UV irradiation . We found that , in contrast to N2 , all of the examined autophagy mutants failed to form LGG-1 foci efficiently in the pachytene region of their gonads following UV irradiation ( S1A and S1C Fig ) . These results indicate that all these autophagy gene activities are required to form LGG-1 foci in the pachytene region of adult hermaphrodite gonads following DNA damage . We next asked whether the formation of LGG-1 foci following DNA damage requires PGL-1 and/or PGL-3 . We therefore treated N2 , pgl-1 single , pgl-3 single , and pgl-1; pgl-3 double mutant hermaphrodites with UV irradiation , collected these animals at 3 h after UV irradiation , and immunostained their gonads with anti-LGG-1 antibody along with DNA counterstaining ( Fig 5A ) . We found that the number of LGG-1 foci generated following UV irradiation significantly decreased in gonads of pgl-1 single , pgl-3 single , and pgl-1; pgl-3 double mutant hermaphrodites compared with N2 hermaphrodite gonads ( Fig 5A and 5B ) . Especially , in pgl-3 single and pgl-1; pgl-3 double mutant hermaphrodite gonads , only a very small number of LGG-1 foci were observed in the pachytene region following UV irradiation ( Fig 5A and 5B ) . These results indicate that PGL-1 and PGL-3 , especially PGL-3 , are required to induce autophagy following DNA damage . We also examined the formation of LGG-1 foci in glh-1 mutant hermaphrodite gonads . GLH-1 is a member of another family of constitutive P granule components [58] . It was previously shown that in glh-1 mutant germ cells , PGL-1 and PGL-3 are dissociated from the perinuclear region and dispersed to the cytoplasm [26 , 27 , 59] . It was also shown that germ cell apoptosis was more increased in glh-1 mutant hermaphrodites than in N2 hermaphrodites under both physiological and DNA-damaged conditions as in pgl-1 and pgl-3 mutants [31] . We found that , in contrast to N2 hermaphrodite gonads , a substantial number of LGG-1 foci were persistently present in the pachytene region of glh-1 mutant hermaphrodite gonads with or without UV irradiation ( Fig 5A and 5B ) . These results suggest that subcellular localization of PGL-1 and PGL-3 , that is , whether they localize at the nuclear periphery or they are dispersed to the cytoplasm , influences the formation of LGG-1 foci ( see Discussion ) . It was previously shown that SEPA-1 ( Suppressor of Ectopic P granules in Autophagy mutants ) functions as a bridging molecule that directly binds to both PGL-3 and the autophagy protein LGG-1 to mediate degradation of PGL-1 and PGL-3 , which are missegregated to somatic blastomeres , during embryogenesis [48] . It was also reported that autophagic removal of PGL proteins from somatic blastomeres is impaired when epg-11 , encoding an arginine methyltransferase that methylates PGL-1 and PGL-3 , or epg-2 , encoding a scaffold protein that associates with SEPA-1 aggregates , is mutated [60] . To examine whether these activities are also required for autophagic removal of PGL proteins from germ cells in adult hermaphrodite gonads , we measured the extent of PGL-1 removal in UV-irradiated hermaphrodite gonads after RNAi depletion of the genes described above ( Fig 6A and 6C ) . Because SEPA-1 is a member of a protein family consisting of 11 members [48] , we also included homozygous viable deletion mutants of three genes , vet-2 ( C35E7 . 1 ) , vet-6 ( F44F1 . 7 ) , and ZK1053 . 3 , which encode other members of SEPA-1 family , in this analysis ( Fig 6B and 6D ) . As expected , we found that the RNAi depletion of atg-4 . 1 , atg-9 , atg-13 , atg-18 , bec-1 , and lgg-1 , which all served as positive controls , led to dramatically reduced PGL-1 removal compared with a mock RNAi control ( Fig 6A and 6C , p < 0 . 001 ) . The same result was observed upon epg-2 and epg-11 RNAi depletion ( Fig 6A and 6C , p < 0 . 001 ) , indicating that the scaffold protein EPG-2 and the arginine methyltransferase EPG-11 play critical roles during the autophagic removal of PGL-1 in adult hermaphrodite gonads as in somatic blastomeres . In contrast , RNAi depletion of sepa-1 did not cause a significant reduction in PGL-1 removal ( Fig 6A and 6C , p > 0 . 05 ) . We confirmed that our treatment for RNAi depletion of sepa-1 was effective because the same sepa-1 RNAi treatment effectively phenocopied the sepa-1 mutant phenotype [48] . That is , ectopic formation of PGL granules in somatic blastomeres of autophagy mutant embryos was efficiently suppressed by our sepa-1 RNAi treatment ( S4 Fig ) . We also examined the expression pattern of SEPA-1 using an integrated transgenic strain HZ455 , in which sepa-1::GFP is expressed under the control of its own promoter ( S5 Fig ) . We observed that SEPA-1::GFP was not expressed at a detectable level in gonadal germ cells in HZ455 adult hermaphrodites ( S5 Fig ) . These results support the view that SEPA-1 does not play a critical role in the autophagic removal of PGL-1 in adult hermaphrodite gonads as opposed to its critical role in somatic blastomeres . On the other hand , among the three deletion mutants for other SEPA-1 family members , we found that vet-2 and vet-6 mutant hermaphrodites reduced the level of PGL-1 removal following UV irradiation compared with N2 hermaphrodites ( Fig 6B and 6D , p < 0 . 05 for vet-2 and p < 0 . 001 for vet-6 ) . Furthermore , we found that the formation of LGG-1 foci following UV irradiation in the pachytene region was significantly reduced in vet-6 mutant hermaphrodite gonads compared with N2 gonads ( S6A and S6B Fig , p < 0 . 001 ) . Therefore , in place of SEPA-1 , other members of SEPA-1 family , such as VET-2 and/or VET-6 , may function as a bridging molecule , or another class of protein may function as a bridging molecule , to link PGL proteins with the core autophagic machinery . In summary , our results suggest that the autophagic machinery that removes PGL proteins from gonadal germ cells largely overlaps with the machinery removing PGL proteins from somatic blastomeres except for the requirement of SEPA-1 . In a previous study , we found that the removal of PGL-1 from germ cells following DNA damage was significantly reduced in cep-1 , but not egl-1 , mutant gonads [31] . Furthermore , the failure to increase germ cell apoptosis following DNA damage was significantly rescued by a pgl-1 mutation in cep-1 , but not egl-1 , mutant hermaphrodites [31] . Given that the apoptosis defect of both cep-1 and autophagy mutants was rescued by depletion of PGL-1 , we tested the possibility that CEP-1 and autophagy act in the same pathway . We first tested whether autophagy induction requires the activity of CEP-1 and/or EGL-1 . We found that the formation of LGG-1 foci following UV irradiation was significantly reduced in the pachytene region of cep-1 , but not egl-1 , mutant hermaphrodite gonads compared with N2 hermaphrodite gonads ( Fig 7A and 7B; also see S7 Fig for their LGG-1 foci formation without UV irradiation ) . These results indicate that the activity of CEP-1 , but not EGL-1 , is required to activate autophagy in the pachytene region following DNA damage to facilitate the removal of PGL proteins in adult hermaphrodite gonads . To examine whether CEP-1 transcriptionally activates autophagy genes following DNA damage , we measured mRNA levels of several autophagy genes including atg-4 . 1 , atg-9 , atg-18 , and lgg-1 in N2 , cep-1 mutant , and egl-1 mutant hermaphrodites under both non-irradiated and UV-irradiated conditions using qRT-PCR ( Fig 7C ) . We found that the mRNA levels of these four genes significantly increased following UV irradiation in N2 , but not cep-1 mutant , hermaphrodites , indicating that CEP-1 is required for transcriptional activation of all the four autophagy genes following UV irradiation ( Fig 7C ) . In egl-1 mutant hermaphrodites , the mRNA levels of atg-4 . 1 , atg-9 , and lgg-1 genes increased but the mRNA level of atg-18 gene failed to increase following UV irradiation ( Fig 7C ) . These results indicate that EGL-1 is not required for transcriptional activation of several autophagy genes including atg-4 . 1 , atg-9 , and lgg-1 following UV irradiation . On the other hand , atg-18 gene seems to require the activities of both CEP-1 and EGL-1 for the transcriptional activation following UV irradiation . To examine whether CEP-1 functions in the germ line for the transcriptional activation of autophagy genes , we measured the mRNA level of atg-18 gene in rrf-1 mutant hermaphrodites under both non-irradiated and UV-irradiated conditions following cep-1 RNAi treatment ( Fig 7D ) . In rrf-1 mutants , although the germ line is susceptible to RNAi as in N2 , many , but not all , somatic tissues are resistant to RNAi [61] . We found that RNAi depletion of cep-1 dramatically repressed the transcriptional activation of atg-18 gene following UV irradiation in rrf-1 mutant hermaphrodites ( Fig 7D ) . This result indicates that CEP-1 functions either in the germ line or in some specific somatic tissues , which are susceptible to RNAi in rrf-1 mutants , for the transcriptional activation of autophagy genes . In summary , these results indicate that CEP-1 is required for induction of autophagy upon DNA damage and that CEP-1 activates some of the autophagy genes at the transcriptional level . PGL-1 and PGL-3 are the major constitutive components of C . elegans germ granules termed P granules . In this study , we revealed that autophagy removes PGL-1 and PGL-3 from germ cells in adult hermaphrodite gonads upon UV-induced DNA damage . Why does autophagy remove PGL-1 and PGL-3 from germ cells upon DNA damage ? Although it was previously shown that autophagy also eliminates PGL-1 and PGL-3 when they are missegregated to somatic blastomeres during embryogenesis [48] , it has not been clearly answered why autophagy needs to specifically target PGL-1 and PGL-3 . It was demonstrated through the study of mes-1 mutants that P granules do not necessarily alter or deteriorate preprogrammed somatic development when they are missegregated to somatic tissues [62] . In this sense , although autophagic elimination of PGL-1 and PGL-3 in somatic cells may serve as a fail-safe mechanism , in fact , autophagy does not have to remove PGL-1 and PGL-3 from somatic blastomeres to assure or protect normal somatic development . In previous studies , we found that PGL-1 and PGL-3 serve as critical repressors of apoptosis not only in the germ line but also in the soma by repressing both the protein level of CED-4 and the cytoplasmic translocation of SIR-2 . 1 [31 , 63] . From our previous and current studies , we propose that autophagy specifically targets PGL-1 and PGL-3 primarily to synergistically link autophagy to apoptosis during postembryonic germline development , that is , to induce a higher level of germ cell apoptosis following DNA damage . Whereas autophagy functions to degrade organelles within cells , apoptosis functions to eliminate cells within organisms . Depending on the circumstances , autophagy and apoptosis act either cooperatively or competitively [64] . In many cases , autophagy blocks the induction of apoptosis , while activation of apoptosis-associated caspase shuts off the process of autophagy [65] . However , consistent with our results , autophagy can also help to induce apoptosis . Several studies have demonstrated that autophagy is required to activate apoptosis under stress conditions [66–68] . In this study , we identified PGL-1 and PGL-3 as the key molecules that link autophagy to apoptosis during DNA damage-induced germ cell apoptosis . A synergistic link between autophagy and apoptosis has also been observed during Drosophila oogenesis , in which autophagic degradation of dBruce , an inhibitor of apoptosis , facilitates apoptosis of nurse cells [69] . It will be interesting to examine whether a similar synergistic link between autophagy and apoptosis also exists during mammalian oogenesis and/or spermatogenesis to maintain the integrity and quality of the gametes . It appears that loss of PGL-1 occurs very early during C . elegans germ cell apoptosis under both physiological and DNA-damaged conditions likely as a decisive and irreversible event [31 , 70] . We propose that monitoring removal of PGL-1 and/or PGL-3 from germ cells can serve as an alternative method for sensitive detection , time-lapse observation , and even quantification of apoptotic germ cells , with the caveat that scoring PGL-absent germ cells provides significantly larger numbers than previous apoptosis counting methods , because , compared to the previous methods , loss of PGL-1 and PGL-3 occurs very early during the apoptosis and stably remains until the apoptotic germ cells are finally engulfed and consumed [31] . We have shown that the removal of PGL-1 and PGL-3 from germ cells in adult hermaphrodite gonads following DNA damage requires largely the same set of autophagy genes as the ones required for the removal of PGL-1 and PGL-3 that are missegregated to somatic blastomeres during embryogenesis . However , our data also suggest that the removal of PGL proteins from gonadal germ cells requires a different mechanism for substrate recognition as compared to the one used in somatic blastomeres . Further studies are required to determine the exact mechanism of P granule targeting in gonadal germ cells for the autophagic degradation . Our data are consistent with the hypothesis that the removal of PGL proteins by autophagy is required for the full induction of germ cell apoptosis upon DNA damage . Multiple lines of evidence support this model . A previous report had indicated that DNA damage-induced germ cell apoptosis depends on autophagy [54] . Our previous data had demonstrated that P granules are massively degraded in apoptotic germ cells upon DNA damage [31] . We now show that autophagy induction precedes the P granule degradation and that autophagy , as evidenced by LGG-1 foci accumulation , closely associates with and depletes the major P granule component , PGL-1 . Our model is supported by the genetic interactions we have uncovered . The apoptosis defect of autophagy mutants is bypassed by depletion of pgl-1 or pgl-3 . We have also revealed that a number of autophagy genes are transcriptionally activated following DNA damage and that this activation requires CEP-1 , the worm p53-like protein needed for induction of germ cell apoptosis upon DNA damage . Autophagy , as measured by LGG-1 foci formation , was almost absent without DNA damage , but LGG-1 foci accumulated soon after UV irradiation in the pachytene-stage germ cells in wild-type adult hermaphrodite gonads . The finding that LGG-1 foci formation upon DNA damage was significantly reduced in pgl-1 and pgl-3 mutant hermaphrodite gonads suggests that PGL-1 and PGL-3 are possibly the major substrates of autophagy , because PGL-3 , which itself is the substrate of autophagy , is required for the autophagic degradation of PGL-1 in somatic blastomeres [48] . The view that PGL-1 and PGL-3 serve as the substrates of autophagy in adult hermaphrodites was also supported by the results of our western blot analysis showing that the protein level of PGL-1 was reduced following UV irradiation in adult hermaphrodites in an autophagy activity-dependent manner . In contrast , LGG-1 foci persisted with or without UV irradiation in glh-1 mutant hermaphrodite gonads , in which PGL-1 and PGL-3 are dissociated from the perinuclear region and dispersed throughout the cytoplasm [26 , 27 , 59] . These results suggest that not only the presence of PGL-1 and PGL-3 but also their subcellular localization , that is , their dispersal to the cytoplasm is important for the activation of autophagy . We hypothesize that , although autophagy is not activated when PGL-1 and PGL-3 are normally localized to the perinuclear region , autophagy is activated when PGL-1 and PGL-3 are dispersed to the cytoplasm , as exemplified in somatic blastomeres during embryogenesis . In these cells , missegregated PGL-1 and PGL-3 are dispersed to the entire cytoplasm and autophagy is activated without DNA damage [48] . The coincidence between dispersal of PGL-1 and PGL-3 to the entire cytoplasm and persistent formation of LGG-1 foci is also observed in developing oocytes and newly fertilized embryos [26 , 41] . It remains to be determined whether subcellular localization or physiological status of PGL-1 and/or PGL-3 is indeed affected upon DNA damage in the pachytene-stage germ cells for the activation of autophagy . The p53 family of tumor suppressors trigger autophagy in mammalian cancer cells in response to genotoxic and/or environmental stimuli , mediated by the nutrient energy sensor AMP-activated protein kinase ( AMPK ) , by inhibition of the mammalian target of rapamycin ( mTOR ) , and by induction of the autophagy modulator DRAM1 [71 , 72] . Furthermore , it was recently described that p53 transcriptionally activates the expression of Sestrins , the highly conserved stress-responsive proteins that promote AMPK signaling for the formation of autophagic vesicles [73] . These results indicate that p53 activates autophagy through several mediators . In C . elegans , CEP-1 has been shown to play critical roles during DNA damage responses in the germ line . When DNA damage induces a higher level of germ cell apoptosis in C . elegans adult hermaphrodites , the DNA damage signal is transduced through multiple gene products in the DNA damage checkpoint pathway to activate CEP-1 [13 , 74] . Remarkably , cep-1 mutants show no increase in germ cell apoptosis upon DNA damage [13] . CEP-1 has been shown to induce transcription of both egl-1 and ced-13 that encode two pro-apoptotic BH3-only proteins in response to DNA damage to increase germ cell apoptosis [14] . However , although the increase in germ cell apoptosis following DNA damage was reduced , the increase was not completely abrogated in egl-1 single , ced-13 single , and even egl-1; ced-13 double mutants [9 , 14] . These results suggest the presence of additional transcriptional targets of CEP-1 for full induction of germ cell apoptosis following DNA damage . In this study , we found that not only the formation of LGG-1 foci , but also the transcription of several autophagy genes , was induced by CEP-1 following DNA damage in adult hermaphrodites . These results suggest that autophagy genes are previously unidentified transcriptional targets of CEP-1 to increase germ cell apoptosis following DNA damage . Germ granules , the germline-specific cytoplasmic structures , are also observed in various mammals . Germ granules have been implicated in the formation or maintenance of the germ line . As in C . elegans , the vast majority of mammalian germ cells undergo apoptosis . Germ cell apoptosis is critical to maintain the quality of germ line because germ cell apoptosis eliminates damaged or compromised germ cells from being used for the next generation . Using the C . elegans germ line , which serves as a prime model for mammalian germ cell apoptosis , we implicated the autophagic removal of P granules , the C . elegans germ granules , in the induction of germ cell apoptosis upon DNA damage . It will be interesting to further follow up on the mechanistic details and to analyze if removal of germ granules also has an apoptotic role in mammalian germ lines . All strains were maintained at 20°C on nematode growth medium ( NGM ) agar plates seeded with Escherichia coli OP50 as previously described [75] . The strains used in this study are listed in S1 Table . As a source of DNA damage , we solely used UV irradiation in this study . L4-stage hermaphrodites were pre-cultured for 24 h at 20°C , irradiated with 400 J/m2 of UV-C light ( Sankyo Denki germicidal lamp G40T10 , 40W , 254 nm ) on OP50-seeded NGM agar plates , post-cultured on the plates for either 3 h ( for observation of LGG-1 foci formation ) or 24 h ( for apoptotic germ cell counting ) at 20°C , and subjected to respective experiments . Immunofluorescence analysis was performed as previously described [31] . In brief , worms were dissected to extrude gonads in 10 μl of M9 buffer containing 100 μg/ml tetramisole on a poly-L-lysine-coated microscope slide , covered with a coverslip , freeze-cracked with liquid nitrogen , fixed with cold methanol and cold acetone , and immunostained with primary and secondary antibodies . The specimens were further counterstained with 1 μM TO-PRO-3 ( Molecular Probes ) to stain DNA , and observed under a confocal microscope ( Olympus , FV1000 Spectral ) . The following primary antibodies were used: mouse monoclonal OIC1D4 , which specifically recognizes PGL-1 ( undiluted; a kind gift from Susan Strome ) , rabbit anti-PGL-3 ( 1:400; a kind gift from Asako Sugimoto ) , rabbit anti-LGG-1 ( 1:400; a kind gift from Ken Sato ) , rabbit anti-GFP ( 1:400; Novus ) . The number of LGG-1 foci in immunofluorescent gonad images was scored using NIS-Elements 3 . 1 software ( Nikon Instruments ) . To do this , we used the “Object Count” function of the software to identify immunostained LGG-1 foci in the pachytene region of the gonads by setting a threshold to distinguish LGG-1 foci from backgrounds . Thresholds were set arbitrarily for respective immunofluorescence images , but when a series of immunofluorescence images were analyzed for quantitative comparison , the threshold was set constant . More than 10 immunofluorescent gonad images were examined to determine the number and distribution of LGG-1 foci in respective genetic backgrounds under respective conditions . Western blot analysis was performed using whole worm protein extract obtained from ca . 100 gravid adult hermaphrodites of each genotype under each condition per gel well . Antibodies bound to a nitrocellulose membrane ( PROTRAN BA83 , Whatman ) were visualized with ECL western blotting detection kit ( Amersham ) , and respective band intensities were measured with LAS-3000 image analyzer using Multi Gauge ( v . 3 . 0 ) software ( Fuji Film ) . To quantify band intensity , we used the “ROI” tool to define the band area , and employed the “Analyze” tool to measure the intensity of respective protein bands on a western blot image . Protein band intensity was presented as a sum of optical density in the ROI using an arbitrary unit . Then , the intensity of each PGL-1 protein band was normalized with that of α-tubulin band on the same lane . Finally , the normalized PGL-1 band intensities were converted to “relative values” , so as to make the PGL-1 band intensities under non-irradiated ( 0 J/m2 UV ) condition on respective western blot images as value 1 . Then , obtained “relative values” ( 3 values for each genetic background under each condition ) were plotted , averaged , and statistically evaluated using t-test . The following primary and secondary antibodies were used: rabbit anti-PGL-1 ( 1:4000 ) [26] , mouse anti-α-tubulin ( 1:2000; Sigma ) , HRP-conjugated goat anti-rabbit IgG ( 1:10000; Santa Cruz Biotech . ) , and HRP-conjugated donkey anti-mouse IgG ( 1:1000; Jackson ImmunoResearch ) . RNAi experiments were performed using “soaking” method as described previously [76] . Briefly , L1-stage worms soaked in respective dsRNA solutions for 24 h were recovered to OP50-seeded NGM agar plates , grown for a few days until they reached the young adult stage ( 24 h after the L4 stage ) , irradiated or not irradiated with UV , further incubated for 24 h , and they ( P0 ) or their progeny ( F1 ) were examined for the resulting RNAi phenotype . Apoptotic germ cells were visualized by Acridine Orange ( AO ) vital staining as previously described [55] , with minor modifications . Briefly , UV-irradiated or non-irradiated worms were stained with 25 μg/ml of Acridine Orange ( AO ) in M9 buffer for 1 h in the dark , allowed to recover on fresh OP50-seeded NGM plates for 20 min , and observed under fluorescence microscopy to count the number of Acridine Orange ( AO ) -positive germ cells per gonad arm . Only one gonad arm was scored for each observed animal . 30–40 animals were examined for each experiment . Adult hermaphrodites of respective genotypes , which were treated or not treated with UV irradiation , were collected in TRIzol ( Invitrogen ) , and total RNA was extracted using a phase lock gel ( MaXtract High Density , Qiagen ) . cDNA was synthesized using oligo-dT primer and M-MLV reverse transcriptase ( Invitrogen ) . qPCR reactions were performed using Power SYBR Green PCR Master Mix ( Applied Biosystems ) . The final PCR volume was 25 μl . act-1 mRNA was used as an endogenous control for data normalization . The primers used in this study are listed in S2 Table . All experiments were repeated more than three times , and p-values were calculated using either Student’s t-test or one-way ANOVA test for statistical evaluation of data .
C . elegans provides a prime model for studying evolutionarily conserved biological mechanisms that control development and physiology . One of the conserved features of germ cells is the presence of germ granules , the germline-specific cytoplasmic structures observed in various organisms from worms to humans . P granules , the C . elegans germ granules , have critical functions for its postembryonic germline development . We previously reported that PGL-1 and PGL-3 , the defining components of P granules , are lost from germ cells prior to germ cell apoptosis , and that this loss is significantly enhanced upon DNA damage . Here , we show that removal of PGL-1 and PGL-3 from germ cells following DNA damage is significantly reduced in autophagy mutants . Furthermore , the failure of autophagy mutants to increase germ cell apoptosis upon DNA damage is significantly recovered by depletion of pgl-1 or pgl-3 . We also show that autophagy , as measured by LGG-1 foci formation , is induced following DNA damage in adult hermaphrodite gonads in PGL-1 , PGL-3 , and CEP-1 , the worm p53-like protein , dependent manner . Taken together , our results indicate that DNA damage activates autophagy through CEP-1 to remove PGL-1 and PGL-3 from germ cells , which contributes to fully induce germ cell apoptosis upon DNA damage in C . elegans adult hermaphrodites .
You are an expert at summarizing long articles. Proceed to summarize the following text: Expression of genes of the locus of enterocyte effacement ( LEE ) is essential for adherence of enterohemorrhagic Escherichia coli ( EHEC ) to intestinal epithelial cells . Gut factors that may modulate LEE gene expression may therefore influence the outcome of the infection . Because nitric oxide ( NO ) is a critical effector of the intestinal immune response that may induce transcriptional regulation in enterobacteria , we investigated its influence on LEE expression in EHEC O157:H7 . We demonstrate that NO inhibits the expression of genes belonging to LEE1 , LEE4 , and LEE5 operons , and that the NO sensor nitrite-sensitive repressor ( NsrR ) is a positive regulator of these operons by interacting directly with the RNA polymerase complex . In the presence of NO , NsrR detaches from the LEE1/4/5 promoter regions and does not activate transcription . In parallel , two regulators of the acid resistance pathway , GadE and GadX , are induced by NO through an indirect NsrR-dependent mechanism . In this context , we show that the NO-dependent LEE1 down-regulation is due to absence of NsrR-mediated activation and to the repressor effect of GadX . Moreover , the inhibition of expression of LEE4 and LEE5 by NO is due to loss of NsrR-mediated activation , to LEE1 down-regulation and to GadE up-regulation . Lastly , we establish that chemical or cellular sources of NO inhibit the adherence of EHEC to human intestinal epithelial cells . These results highlight the critical effect of NsrR in the regulation of the LEE pathogenicity island and the potential role of NO in the limitation of colonization by EHEC . Enterohemorrhagic Escherichia coli ( EHEC ) , especially those belonging to the O157:H7 serotype , are foodborne pathogens and healthy rearing animals are the main reservoir . Human infection occurs through the ingestion of contaminated food . This primary infection yields to the development of intestinal disorders , including aqueous or bloody diarrhea . Moreover , EHEC express a cardinal and well-defined virulence factor , the Shiga-toxin ( Stx ) encoded by genes located in lysogenic lambdoid bacteriophages . Stx is produced in the gut lumen and crosses the epithelial barrier to reach the blood and the target organs including the kidneys . In this context , infected patients may develop life-threatening complications such as the hemolytic and uremic syndrome ( HUS ) , the main cause of renal failure in children in developed countries [1] . EHEC genes carried by the locus of enterocyte effacement ( LEE ) , a chromosomal pathogenicity island organized in 5 operons , encode bacterial factors implicated in the intimate adherence of these bacteria to intestinal epithelial cells [2] . These genes encode a type 3 secretion system ( T3SS; LEE1 , LEE2 , LEE3 ) , a translocon and a syringe ( LEE4 ) that allows bacteria to inject effectors in epithelial cells , such as the LEE5-encoded intimin receptor Tir; moreover , other proteins not carried by the LEE can be translocated by the T3SS into enterocytes [3] , [4] . The injected effectors and/or protein of the translocon itself interact with the host signal transduction , leading to actin polymerization and to microvilli effacement [2] , to regulation of the innate immune response [5] , [6] , and to increased electrolyte transport [7] . Regulation of gene expression within the LEE is known to be complex and governed by a large number of influences , including environmental cues or quorum sensing , and involves several specific or global regulators [8] , [9] . The first gene of the LEE1 operon , ler , encodes a transcriptional regulator that positively regulates the expression of all the other operons [9]–[11] . However a variety of extra-transcriptional mechanisms have also been involved in the regulation of LEE expression , though little detailed mechanistic information is available [12] . GadE ( YhiE ) and GadX ( YhiX ) are two main regulators of the acid fitness island involved in acid-resistance ( AR ) in E . coli K12 [13]–[15] . At acidic pH values , GadE and GadX positively regulate the gadA and gadBC genes , encoding the components of the glutamate-dependent AR . In E . coli O157:H7 , GadE has acquired additional functions and inversely coordinates expression of AR and LEE genes [16]: It has been proposed that , during passage through the human stomach , GadE protects EHEC by inducing the glutamate-dependent AR system and inhibits the unnecessary expression of the LEE genes , while environmental cues in the intestine lead to downregulation of gadE and upregulation of the LEE genes [16] . GadE has been shown to directly bind the ler ( LEE1 ) and sepZ ( LEE2 ) promoters in vitro [8] , but in vivo binding of GadE and the role of GadX have never been investigated . We have previously shown that nitric oxide ( NO ) decreases Stx2 synthesis by EHEC O157:H7 at the transcriptional level [17] . This occurs through the inhibition of the SOS response by the NO sensor nitrite-sensitive repressor ( NsrR ) [17] , the key regulator of the nitrosative stress in enterobacteria [18] . In this context , our aim was to investigate whether NO also modulates LEE gene transcription and therefore EHEC adhesion to epithelial cells . Here we show that NsrR is a direct positive regulator of the transcription of LEE1 , LEE4 and LEE5 genes and an indirect repressor of gadE and gadX genes . In the presence of NO , LEE1/4/5 activation is abrogated , GadE is induced and yields to gadX expression . Finally , we identify GadE and GadX as repressors of LEE4/5 and of LEE1 , respectively . Using a human intestinal epithelial cells/EHEC co-culture model we demonstrate that bacterial adhesion is inhibited in NO producing cells . We first examined adhesion of the E . coli O157:H7 strain EDL933 to cultured Hct-8 intestinal epithelial cells in the presence of the NO donor NOR-4 . Exposure to NOR-4 at 200 µM or 500 µM did not cause any significant difference in the growth rate of EDL933 , as described [17] . However , EHEC adhesion to Hct-8 cells was dramatically inhibited when NOR-4 was added to the co-cultures ( Figs . 1A and 1B ) . The number of EHEC fixed to the cells was significantly decreased by 41±5% and 89±2% in the presence of 200 µM and 500 µM NOR-4 , respectively ( Fig . 1B ) . To further confirm this result , we analyzed the effect of endogenous NO released by enterocytes . Hct-8 cells were first treated for 24 h with a cytokine cocktail known to stimulate the inducible NO synthase ( iNOS ) expression [19] , washed , and then infected with the strain EDL933 in the presence or absence of the iNOS inhibitor N6- ( 1-iminoethyl ) -l-lysine ( l-NIL ) . There was less EHEC fixed to NO-producing epithelial cells than to control cells ( Figs . 1A and 1C ) . The inhibition of EHEC adherence to Hct-8 cells treated with cytokines was abolished by the use of l-NIL ( Figs . 1A and 1C ) . The expression of genes that represent the five operons of the LEE ( Fig . 2A ) was analyzed after treatment with NOR-4 for 6 h . NO was consistently generated in the bacteria culture medium and reached a plateau after 6 h ( Fig . S1 ) . The expression of ler ( LEE1 ) , espA ( LEE4 ) , tir and eae ( LEE5 ) was down-regulated by NO , while the transcription of sepZ ( LEE2 ) was induced by 2 . 4-fold ( Fig . 2B ) . The expression of the gene escV ( LEE3 ) was not modulated by NOR-4 ( Fig . 2B ) . Because GadE and GadX modulates LEE expression in EHEC and EPEC , respectively , [16] , [20] , we investigated the effect of NOR-4 on gadE and gadX transcription . As shown in Figure 2C , the expression of gadE and gadX was significantly induced by 2 . 4- and 2 . 7-fold in bacteria exposed to NOR-4 , respectively . Thereby , these data prompted us to wonder whether NO-dependent down-regulation of LEE1 , LEE4 and LEE5 requires GadE and/or GadX . Since the role of GadX and GadE on LEE expression is not well defined and is strongly dependent on the growth conditions [16] , [20] , [21] , we first analyzed the expression of ler , espA , and tir in EDL933 ΔgadE and ΔgadX mutants . When compared to the EDL933 strain , the mRNA levels of ler , espA and tir were increased by ∼1 . 4- , 2 . 3- , and 2-fold in the ΔgadE strain , respectively ( Fig . 3A ) ; these effects were reversed when the gadE mutant was trans-complemented with the gadE gene in a low copy number plasmid vector ( Fig . 3A ) . The gadX mutation was associated with a spontaneous increase of ler transcription and with a significant reduction of espA and tir gene expression ( Fig . 3A ) . The transcription of ler was repressed while the expression of espA was activated and that of tir was restored to the same level as the WT in the trans-complemented strain ( EDL933 ΔgadX-c; Fig . 3A ) . These data suggest that GadE represses the expression of LEE4 and LEE5 genes independently of Ler , and that GadX represses LEE1 but activates LEE4 and LEE5 gene expression . Interestingly , the NOR-4-dependent down-regulation of ler , espA , and tir was still observed in the ΔgadE , ΔgadX and ΔgadE/gadX mutants ( Fig . 3A ) , suggesting that another factor is implicated in the inhibition of LEE1/4/5 by NO . We next wonder whether GadE and GadX repressed the LEE independently from each other or whether GadX is epistatic to GadE as in E . coli K12 [22] . The expression of ler was similar in a ΔgadE/gadX double mutant and in the EDL933 ΔgadX strain ( Fig . 3A ) , indicating that GadX is epistatic to GadE in controlling LEE1 . Conversely , espA and tir mRNA levels were increased in EDL933 ΔgadE/gadX when compared to the WT strain , as in the ΔgadE strain , demonstrating that GadE is epistatic to GadX for the regulation of LEE4 and LEE5 . Therefore we investigated whether GadE controls gadX expression . Figure 3B shows a 33% decrease in gadX mRNA levels in the gadE mutant , indicating that GadE activates gadX expression . In addition , we observed 3 . 1-fold more gadE mRNA copies in the ΔgadX strain than in the WT strain ( Fig . 3B ) and gadE mRNA levels were dramatically reduced in the complemented strain ( Fig . 3E ) , suggesting that GadX is a repressor of gadE expression . Therefore , the moderate increase in ler expression observed in the ΔgadE strain ( Fig . 3A ) is likely due to the lower level of GadX in this strain and not to a direct effect of GadE on ler transcription . Lastly , the activation of gadX transcription by NOR-4 was suppressed in the gadE mutant , but not in the EDL933 ΔgadE-c strain ( Fig . 3B ) , while the NO-dependent induction of gadE mRNA expression was still observed in the ΔgadX strain ( Fig . 3B ) . These data indicate that NO activates gadX expression through GadE . NsrR is a transcriptional regulator that regulates gene expression in response to NO [18] . Therefore we investigated whether NsrR regulates gadE , gadX , and the LEE genes . In the absence of NO , the mRNA levels of ler , espA , and tir were 6 . 8 , 7 . 1 , and 14 . 3-fold lower in the ΔnsrR mutant than in the WT strain , respectively ( Fig . 4A ) . The expression of these genes was similar in the strains EDL933 and EDL933 ΔnsrR-c ( Fig . 4A ) . Moreover , the NO-dependent regulation of these LEE genes was abrogated in EDL933 ΔnsrR and was restored in the complemented strain ( Fig . 4A ) . Inversely , the transcription of gadE and gadX was significantly increased in the ΔnsrR mutant , but not in the complemented strain . The expression of these two genes was not affected by NOR-4 in the nsrR-deficient strain ( Fig . 4B ) . These data suggest that NsrR is a transcriptional activator of LEE1 , LEE4 , and LEE5 and a repressor of gadE , which in turn modulates gadX expression . NsrR loses its ability to regulate the expression of LEE and gad genes in the presence of NO . The investigation of GadE , GadX and/or NsrR direct binding to the gadE , gadX , and LEE promoter regions was performed by chromatin immunoprecipitation ( ChIP ) experiments using the EDL933 ΔgadE , ΔgadX , and ΔnsrR mutants expressing the 6-His-GadE , the 6-His-GadX , and the 6-His-NsrR fusion proteins , respectively . We first analyzed the gadX promoter described by Hommais et al . [15] , the three promoters described for gadE in E . coli K12 [22] ( Fig . S2 ) , and the gadA promoter as a positive control for GadE and GadX binding [23] . Surprisingly , we found that GadE and GadX did not bind to the gadX and gadE promoters , respectively ( Figs . 5A and 5B ) , indicating that activation of gadX by GadE and repression of gadE by GadX occur through indirect regulations . As expected , the binding of GadX and GadE to the gadA promoter region was observed ( Figs . 5A and 5B ) . Two ler promoters have been described in EHEC , the distal P1 promoter and a putative proximal P2 promoter ( Fig . S2 ) . The P1 promoter is common to EHEC and EPEC , while the P2 promoter is present only in EHEC [24]–[26] . Neither GadE ( Fig . 5A ) nor GadX ( Fig . 5B ) bound to either of these promoters ( Figs . 5A and 5B ) . These data indicate that GadE and GadX do not repress ler expression directly . The LEE4 promoter has been identified in EHEC upstream of sepL [27] , espA being the second gene of the operon . In EPEC , it has been shown that Ler-mediated activation of the LEE5 operon requires sequences between positions -198 and -75 relative to the transcriptional start site [28] . Two primer pairs overlapping this region have been designed for ChIP experiments , amplifying a LEE5 distal ( P1LEE5 ) and a LEE5 proximal ( P2LEE5 ) region ( Fig . S2 ) . ChIP experiments showed that neither GadE nor GadX bound to the LEE4 and LEE5 promoters ( Figs . 5A and 5B ) . Lastly , the binding of GadE and GadX to the LEE1/4/5 promoter regions was not modulated by NOR-4 . These data indicate that control of LEE4 and LEE5 expression by GadE and GadX is due to indirect effects . In contrast , NsrR bound to the distal LEE1 promoter ( P1LEE1 ) , to the LEE4 and LEE5 promoters , and to the promoter of hmpA , a well-know NsrR target gene ( Fig . 5C ) . Furthermore , NsrR binding to these promoter regions was inhibited when the bacteria were grown in the presence of NOR-4 ( Fig . 5C ) . We did not observed NsrR binding to the gadE and gadX promoters ( Fig . 5C ) . We thus performed bio-informatics analysis to identify putative NsrR-binding sites in the LEE1 , LEE4 LEE5 , gadE , and gadX promoters in the strain EDL933 . We used the homologous sequences of seven NsrR-binding sites described in E . coli K12 [29] to generate the sequence logo of the NsrR box in the strain EDL933 ( Fig . 5D ) . We then performed bioinformatics analysis on the LEE1 , LEE4 and LEE5 promoter sequences by the Gibbs Sampler Motif Software , using the matrix of the seven putative NsrR-binding sites of EDL933 . In agreement with the ChIP data , bioinformatics analysis identified sequences presenting high identity with the NsrR consensus binding site in the LEE1 ( P1 ) , LEE4 and LEE5 promoter regions ( Fig . 5D and Fig . S2 ) , but not in the promoters of gadE and gadX . The analysis indicated a 23 bp putative NsrR-binding site in the promoters of LEE1 ( 86 . 9% identity ) and LEE4 ( 78 . 2% identity ) , but only a second half-site NsrR-binding site in the LEE5 promoter ( 90 . 9% identity for the half site; Fig . 5D ) . In silico analyses performed using the BLAST program indicated that these putative binding sites are conserved in a number of EHEC and EPEC strains , but not in Citrobacter rodentium ( Fig . S3 ) , an attaching/effacing pathogen that infects rodents . Since NsrR has been exclusively described as a transcriptional repressor , we investigated the molecular mechanism underlying the direct activation of LEE gene expression by NsrR . For many transcriptional activators , increase of the transcription level results from the recruitment of RNA polymerase through direct interaction between the regulatory protein and one or several subunits of the polymerase [30] . We therefore examined if NsrR can interact with α and σ RNA polymerase subunits . To this end , His-tagged NsrR and hemagglutinin ( HA ) -tagged polymerase subunits α ( RpoA ) or σ38 ( RpoS ) were co-expressed in bacteria . His-NsrR was purified under native conditions using a nickel affinity resin and the different fractions were analyzed by western-blot . As positive controls , RpoA and RpoS were also co-expressed with His-Crp or His-Crl , respectively , two well-known interacting partners [31] , [32] . All tagged proteins were properly expressed as revealed by their immunodetection in the whole extract samples ( Fig . 6 ) . As expected , HA-RpoA and HA-RpoS co-eluted with His-Crp or His-Crl , respectively . No HA-tagged protein was detected in the His eluates of the negative controls , i . e . , bacteria expressing only HA-tagged proteins ( Fig . 6 ) . Importantly , HA-RpoA and HA-RpoS were also specifically recovered in the eluted fractions from the His-NsrR purifications . This finding demonstrates that NsrR can interact with the RNA polymerase complex and suggests that NsrR activates LEE gene expression through the recruitment of RNA polymerase . In order to confirm the role of NO , NsrR , GadE , and GadX in regulating LEE expression , we investigated the attachment of the regulatory mutants to HeLa cells after 6 h of infection in the presence or absence of NOR-4 . As expected , EDL933 adhered to HeLa cells and when NOR-4 was added to the co-culture the level of adhesion was dramatically reduced to that of the ΔescN mutant that lacks a functional T3SS ( Figs . 7A and 7B ) . The adhesion of the ΔgadE and ΔgadX strains was higher than that of the parent strain , correlating with the repressive effect of AR regulatory proteins on LEE gene expression ( Figs . 7A and 7B ) . Conversely , the nsrR mutant was less adherent than the WT strain ( Figs . 7A and 7B ) . The complementation of these three mutants restored the adhesion phenotype of the parental strain ( Figs . 7A and 7B ) . Under NO exposure , adherence properties were affected for the ΔgadE and ΔgadX mutants but not for the ΔnsrR mutant ( Figs . 7A and 7B ) , demonstrating that NsrR is the key regulator controlling the T3SS-dependent adhesion of EHEC in response to NO . In the present report , we show that NO , a critical mediator of the host innate immune response , is a potent inhibitor of LEE gene expression in EHEC O157:H7 and consequently inhibits the adhesion of these pathogens to intestinal epithelial cells . We identified NsrR as an unrecognized regulator that controls the expression of LEE genes in response to NO , and we propose a regulatory model presenting the role of NsrR , GadE and GadX in LEE expression ( Fig . 8 ) . In the absence of NO ( Fig . 8A ) , NsrR directly activates LEE1 , LEE4 , and LEE5 gene expression , and indirectly represses gadE and therefore gadX expression . We also show that GadE indirectly activates gadX expression and represses LEE4 and LEE5 expression independently of Ler , while GadX inhibits gadE and LEE1 expression . When NsrR binds NO ( Fig . 8B ) , it is released from its target DNA , leading to loss of induction of LEE1/4/5 genes and to the up-regulation of gadE and , consequently , gadX . In this context , the NO-dependent LEE1 down-regulation is due to absence of NsrR-mediated activation and to the inhibitory effect of GadX . In parallel , the inhibition of LEE4 and LEE5 gene expression is due to absence of NsrR- and Ler-dependent activation and to increase of GadE level . This model assumes that repression of gadX expression by NsrR is mediated by GadE , which is consistent with the observation that the NO-dependent activation of gadX is abrogated in the ΔgadE and ΔnsrR mutants . NsrR is a key negative regulator of the nitrosative stress in enterobacteria [18] , [33] . NsrR has always been described as a transcriptional repressor . In addition , its DNA-binding activity is suppressed in the presence of NO , yielding to the expression of various genes mainly involved in NO detoxification [18] , [33] . In non-pathogenic E . coli , NsrR also regulates expression of genes involved in metabolism , motility , protein degradation , surface attachment , stress response and transmembrane transport [29] , [34] . Our data indicate that NsrR is also a repressor of the genes gadE and gadX . Nonetheless , the NsrR-dependent repression of gadX is probably mediated by GadE since the NO-dependent up-regulation of gadX is abrogated in the ΔgadE mutant . We did not find a sequence matching the NsrR consensus binding site in the gadE promoter , and ChIP experiments failed to demonstrate physical interaction between NsrR and the gadE promoter . Therefore , the effect of NsrR on gadE transcription is probably indirect and mediated by an unknown regulatory cascade controlled by NsrR . Here we provide compelling evidence that NsrR is a direct positive regulator of LEE1 , LEE4 , and LEE5 operons in EHEC by binding to their own promoters . Moreover , our data also suggest that NsrR acts as a transcriptional activator by recruiting RNA polymerase to promoter regions since NsrR is able to pull-down the α and σ38 subunits of the RNA polymerase . Supporting the concept that it may also be a transcriptional activator , it has been reported that NsrR activates virulence gene expression in Salmonella Typhimurium , in particular expression of genes important for eukaryotic cell adherence , invasion and intestinal translocation , and that an nsrR mutant is impaired in invasion of HeLa cells [35] . However , in silico analysis failed to identify an NsrR consensus binding site in the promoter regions of these genes , indicating that the positive regulatory effect of NsrR is probably indirect in this pathogen [35] . Moreover , using an E . coli K12 strain harboring a multicopy plasmid that titrates out NsrR , Filenko et al . have identified by a microarray analysis 22 transcripts that could be directly or indirectly activated by NsrR [34] . The NsrR binding site is a 23 bp palindrome sequence composed of two 11 bp half sites separated by a single nucleotide , and NsrR binds to DNA as a dimer [36] . However , a number of NsrR target promoters contain only a single half site [29] . Potential NsrR consensus sequence were identified in the LEE1 , LEE4 and LEE5 promoters , with a 23 pb putative NsrR-binding site in the LEE1 and LEE4 promoters , and a putative second half-site in the LEE5 promoter . It has been suggested that , when the NsrR binding site contains only a single half site , one NsrR monomer makes specific contact to the consensus half site and the other monomer forms nonspecific contact [37] . Alternatively , it has been suggested that NsrR binds as a tetramer to the complete binding motif and as a dimer when only one half site is conserved [29] . It is noteworthy that the putative NsrR binding sites identified in the LEE1 , LEE4 and LEE5 promoters are conserved in a number of other EHEC and EPEC strains , but not in C . rodentium , suggesting that NO also influences cell adhesion via NsrR in other E . coli attaching/effacing pathogenic human strains . Influence of GadE on LEE gene expression remains controversial . While Tatsuno et al . described an increased expression of LEE2 , LEE4 , and LEE5 in a ΔgadE mutant , which is not correlated with enhancement of ler expression [20] , KailasanVanaja et al . showed that GadE represses LEE expression by down-regulating ler transcription [16] . These discrepancies are proposed to be due to differences in growth medium and/or differences in the sensitivity of the assays used in each study . Interestingly , our data indicate that GadE may repress the expression of LEE4 and LEE5 via two regulatory cascades , mediated or not by Ler ( Figure 8 ) . On the one hand , we show that GadE inhibits LEE1 through GadX , because a decreased expression of gadX and an induction of LEE1 are observed in the gadE-deficient strain; this results in loss of Ler-dependent induction of LEE4/5 . On the other hand , the deletion of gadX is associated with an increased expression of ler and gadE , and with an inhibition of LEE4/5 , suggesting that GadE inhibits these operons independently of Ler . In accordance , the induction of espA and tir in the gadE mutant and in the ΔgadE/gadX strain demonstrates that GadX regulates LEE4/5 via the repression of gadE . However , although it has been shown in vitro that GadE can bind to the ler promoter in EHEC O157:H7 [8] , we did not observe such an interaction in vivo in our experiments; this difference is probably due to the presence of binding competitors in live bacteria . Regarding GadX , we show herein that it negatively regulates ler transcription in EHEC . However , the effect of GadX on LEE1 expression is indirect since no physical interaction between GadX and the LEE1 promoter has been demonstrated . Interestingly , it has been described in EPEC that LEE1 is down-regulated under conditions in which GadX is induced , namely at pH 5 . 5 or in contact to epithelial cells [21]; this occurs through the inhibition of the transcription of the per locus by GadX [21] . Because the perC homologue in EHEC , named pch , is involved in LEE1 induction [38] , it would be interesting to now determine the role of GadX on pch expression . The biological relevance of LEE1 , LEE4 , and LEE5 inhibition by NO is the decreased adhesion of E . coli O157:H7 to epithelial cells . When EHEC are ingested with the contaminated food , they first reach the stomach . It has been proposed that the acidic conditions of this ecological niche favor GadE induction and therefore limit EHEC adhesion to gastric tissues [16] . There is also abundant nonenzymatically formed NO in the gastric juice caused by acidification of nitrate and nitrite . In this context , we now propose that the NO-dependent LEE4/5 inhibition is a supplementary mechanism developed by EHEC to avoid their persistence in the stomach and to favor bacterial colonization in the colon . Moreover , we have shown in the present study that , not only a chemical source of NO , but also the reactive nitrogen species released by iNOS-expressing colonic epithelial cells inhibit the adherence of O157:H7 E . coli , and our previous work has identified NO as a potent inhibitor of Stx synthesis [17] . Together , these results suggest that NO might limit the infectious process and HUS development . Nonetheless , it has been described that EHEC inhibit the inducible transcription of iNOS in human enterocytes [19] , thus , by limiting NO production , EHEC might favor their own virulence by increasing the intimate adherence to the intestinal epithelium and Stx synthesis . We can therefore speculate that the issue of the crosstalk between EHEC and the host-derived NO might determine the outcome of the infection . Strains and plasmids used in this study are listed in Table S1 . The EHEC O157:H7 strain EDL933 [39] was used throughout the study . The EDL933 ΔgadE and ΔgadX mutants and the ΔgadE/gadX double mutant were constructed using the one-step PCR-based method [40] , [41] . Mutants were verified by PCR to assess the loss of the gene and by RT-qPCR to confirm lack of expression of the gene of interest , using the primers listed in Table S2 . The ΔnsrR mutant strain has been previously described [17] . For complementation analysis and ChIP experiments , the gadE , gadX , and nsrR genes were amplified with the high fidelity polymerase Pfx50 ( Invitrogen ) and cloned under the control of the araC promoter into a low-copy plasmid containing a 6-histidine tag ( pBADHisA or pBADMycHisA; Invitrogen ) , or in pBAD33 . The cloned genes were checked by nucleotide sequencing , and their expression was analyzed by RT-qPCR . The 6-His-NsrR- , 6-His-GadE- , and 6-His-GadX-encoding genes were expressed at the same level than the WT genes . To verify the mutation of the gadE and gadX genes , we analyzed the acid resistance of the mutant strains [42]: Acid-resistance of the ΔgadE and ΔgadX mutants dropped to 0 and 1 . 41% of the parent strain , respectively; acid resistance was restored in the complemented mutant strains ( data not shown ) . A single colony of EDL933 or isogenic mutants was grown overnight in DMEM Low glucose containing 10 mM HEPES . These cultures were diluted in fresh medium to an OD600 = 0 . 03 and grown at 37°C . The medium was supplemented with ampicillin ( 50 µg/ml ) , kanamycin ( 50 µg/ml ) , chloramphenicol ( 25 µg/ml ) , L-arabinose ( 0 . 1 mM–0 . 5 mM ) , or the NO donor NOR-4 ( Enzo Life Science ) when required . The NsrR-binding sequence logo of the strain EDL933 was generated using homologous sequence of the seven NsrR-binding sites described previously by Partridge et al . in E . coli K-12 strain MG1655 [29] and the software Weblogo ( http://weblogo . berkeley . edu/logo . cgi ) . The probabilities of occurrence matrix from the seven homologous sequences in EHEC O157:H7 strain EDL933 served as a model for the identification of a consensus sequence in the promoter regions of the target genes using the online software Gibbs Motif Sampler ( http://ccmbweb . ccv . brown . edu/gibbs/gibbs . html ) . The sequence alignment of the LEE1 , LEE4 and LEE5 putative sites in other EHEC strains , in EPEC strains , and in C . rodentium was performed with the MEGA5 software . The pBADMycHisA::gadE , pBADHisA::gadX , and pBADMycHisA::nsrR plasmids , encoding 6His-GadE , 6His-GadX and 6His-NsrR , were electroporated into the respective mutants to avoid native protein interference . Overnight cultures of each strain in LB medium were diluted 1∶100 in 25 ml of fresh DMEM medium buffered with 10 mM HEPES , with or without NOR-4 . GadE and GadX expression was induced with 0 . 5 mM l-arabinose and NsrR with 0 . 1 mM l-arabinose . After 6 h of growth with shaking , ChIP was performed as described by Lannois et al . [43] with slight modifications . First , the protein-DNA complexes were cross-linked by treating bacteria with 1% formaldehyde at room temperature for 30 min . Bacteria were then washed twice with cold PBS and incubated for 30 min at 37°C in 0 . 7 ml of lysis buffer ( 10 mM Tris pH 8 , 50 mM NaCl , 10 mM EDTA , and 20% sucrose ) containing 10 mg/ml lysozyme ( Sigma ) . Then , 0 . 7 ml of 2X IP buffer ( 100 mM Tris pH 8 , 300 mM NaCl , 2% Igepal CA-630 , 0 . 5% Na deoxycholate ) containing 1 mM PMSF was added and samples were incubated 15 min at 37°C , cooled down on ice , sonicated , and incubated on ice for 1 min . Sonication was repeated 11 times to obtain a solution of fragmented chromatin . A 50 µl aliquot of each sample was treated with 100 µl TE containing 36 µg proteinase K for 2 hours at 37°C , incubated 8 hours at 67°C to reverse crosslinking , and the DNA was purified with the kit Qiaquick ( Qiagen ) ; this was termed as Input fraction . The rest of the fragmented chromatin was used to generate the IP fraction . After a 2 h-incubation with an anti-Histidine monoclonal antibody ( Sigma ) , protein G sepharose 50% ( 40 µl ) was added to each sample and incubated 1 hour at room temperature . The beads were washed twice with IP buffer , twice with 1 ml of ChIP wash buffer ( 10 mM Tris HCl pH 8 , 250 mM LiCl , 1 mM EDTA , 0 . 5% Igepal CA-630 , and 0 . 5% Na deoxycholate ) and twice with 1 ml of TE buffer . The beads were resuspended in 100 µl of elution buffer ( 50 mM Tris HCl pH 8 , 10 mM EDTA , 1% SDS ) , incubated 15 min at 65°C , and centrifuged at 9500× g for 1 min . The supernatants containing the immunoprecipitated DNA were collected and incubated with 100 µl TE containing 36 µg proteinase K 2 hours at 37°C and 8 hours at 65°C . DNA was purified with the Qiaquick kit ( Qiagen ) and amplified by qPCR using the primers listed in Table S2 and depicted in Fig . S2 . The enrichment of DNA targets was calculated as follows for each protein: the promoters of interest as well as a non-specific rpoA intragenic region were amplified with specific primers ( Table S2 ) . For each DNA target , we calculated the ratio between the copy number in the IP fraction and the Input fraction; each value was then divided with the ratio obtained for the non-specific rpoA intragenic region . Then the same ratio was calculated from the parent strain EDL933 containing the empty pBADMycHisA vector . Values higher than 20 , corresponding to twice the values obtained for the strain EDL933 containing the empty pBADmycHisA vector , indicate protein binding to the promoter of interest . For bacterial co-expression experiments , genes encoding NsrR , Crp or Crl were cloned into the first multiple cloning site of pCDFDuet-1 vector ( Novagen ) allowing expression of the proteins tagged with a N-terminal hexahistidine motif . Genes encoding RpoA or RpoS were cloned into the second multiple cloning site using PCR primers allowing the insertion of a N-terminal HA motif ( see Table S2 for primers ) . E . coli BL21 ( DE3 ) harboring the different constructs was grown at 37°C to OD600 nm of 0 . 7 , then induced with 1 mM IPTG and grown for an additional 2 h . After resuspension of bacteria with a 1/10e volume of lysis buffer ( 50 mM NaH2PO4 , 300 mM NaCl ) , samples were sonicated and centrifuged . Supernatants ( whole bacterial extracts ) were incubated with Ni-NTA beads at 4°C for 16 h . Beads were washed four times with lysis buffer containing 60 mM imidazole and bound proteins were eluted with lysis buffer containing 250 mM imidazole . Total RNA from bacteria was extracted using the TRI Reagent RNA Isolation Reagent ( Sigma ) . Each RNA sample ( 1 µg ) was reverse transcribed with Superscript II enzyme ( Invitrogen ) and random primers ( Invitrogen ) . The cDNAs and serial dilutions of EDL933 genomic DNA , which were used for the standard curves , were amplified with gene-specific primers ( Table S2 ) in the Eppendorf Mastercycler eprealplex ( Eppendorf ) apparatus . The results are presented as the ratios between the copy number of mRNA of the gene of interest and the copy number of rpoA mRNA . Samples were mixed with a 2X SDS-PAGE sample buffer , heated for 5 min at 100°C , resolved on 14% SDS-PAGE gels and blotted on PVDF membranes . Membranes were blocked in PBS-0 . 05% Tween 20 supplemented with 5% non-fat dry milk , then probed with murine monoclonal anti-HA or HRP-conjugated anti-HIS Abs ( Sigma; 1/4000 for each ) . An HRP-conjugated goat anti-murine IgG Ab ( Sigma ) was also used for the HA blots . Acquisitions were performed with a G:box system ( Syngene ) . The epithelial cell lines Hct-8 and HeLa were maintained in DMEM with 10% FCS , 10 mM Hepes , 100 U/ml penicillin , 100 µg/ml streptomycin at 37°C under 5% CO2 . Hct-8 cells were plated on LabTek ( Nunc ) , cultured for 7 days , and stimulated for 24 h with human IFN-γ ( 50 ng/ml ) , TNF-α ( 20 ng/ml ) , and IL-1β ( 5 ng/ml ) . HeLa cells were seeded into LabTek and grown for 24 h . These Hct-8 and HeLa cells were washed , and infected with bacteria with an MOI of 100 , in the presence or absence of NOR-4 or of the iNOS inhibitor l-NIL . After 4 washes with PBS , cells were fixed using 1 ml methanol for 15 min at −20°C and stained with Giemsa or May-Grünwald Giemsa for 30 min . The number of adherent bacteria per cell was counted using the AxioVision 4 software . The concentration of the stable oxidized products of NO , NO3− and NO2− , was measured using the Nitrite/Nitrate Assay Kit ( Cayman Chemical ) . All the data represent the mean ± SEM . Student's t test or ANOVA with the Newman-Keuls test were used to determine significant differences between two groups or to analyze significant differences among multiple test groups , respectively .
Enterohemorrhagic Escherichia coli ( EHEC ) O157:H7 are food-borne pathogens for humans causing bloody diarrhea and , especially in children under five years old , kidney damages leading to death in 5% of cases . Antibiotics are contra-indicated because they are suspected to increase the severity of the disease . Therefore , it is crucial to develop alternative preventive or therapeutic strategies to fight EHEC infection . To reach this goal , a deeper knowledge of host-pathogen interaction is required . A critical step in EHEC infection is the adhesion of bacterial cells to intestinal epithelial cells . In response to the bacterial infection , the host triggers an immune response directed against the pathogen . The current study shows that a main effector of this immune response , nitric oxide ( NO ) , dramatically reduces the capacity of EHEC to adhere to intestinal epithelial cells . We have investigated the molecular mechanisms involved and identified a NO-sensor regulator that controls the expression of the genes required for EHEC adhesion . This finding underlines that NO could be a potential protective factor limiting the development of EHEC-induced diseases and provides a new avenue of investigation for the development of therapeutic strategies against infections with O157:H7 bacteria .
You are an expert at summarizing long articles. Proceed to summarize the following text: Mucorales are an emerging group of human pathogens that are responsible for the lethal disease mucormycosis . Unfortunately , functional studies on the genetic factors behind the virulence of these organisms are hampered by their limited genetic tractability , since they are reluctant to classical genetic tools like transposable elements or gene mapping . Here , we describe an RNAi-based functional genomic platform that allows the identification of new virulence factors through a forward genetic approach firstly described in Mucorales . This platform contains a whole-genome collection of Mucor circinelloides silenced transformants that presented a broad assortment of phenotypes related to the main physiological processes in fungi , including virulence , hyphae morphology , mycelial and yeast growth , carotenogenesis and asexual sporulation . Selection of transformants with reduced virulence allowed the identification of mcplD , which encodes a Phospholipase D , and mcmyo5 , encoding a probably essential cargo transporter of the Myosin V family , as required for a fully virulent phenotype of M . circinelloides . Knock-out mutants for those genes showed reduced virulence in both Galleria mellonella and Mus musculus models , probably due to a delayed germination and polarized growth within macrophages . This study provides a robust approach to study virulence in Mucorales and as a proof of concept identified new virulence determinants in M . circinelloides that could represent promising targets for future antifungal therapies . Mucormycosis is a fungal infection caused by species of the order Mucorales that represents the third most common angio-invasive fungal infection after candidiasis and aspergillosis . Due to the unusual antifungal drug resistance of Mucorales , mucormycosis is considered one of the most important medical complications in immunocompromised patients [1] . Among current antifungal drugs , fluconazole , voriconazole , posaconazole and itraconazole are potent agents of choice used in aspergillosis and candidiasis that , unfortunately , present poor activity against mucormycosis [2] . More specifically , amphotericin B , an old-known macrolide antifungal compound with severe adverse effects , and more recently isavuconazole are used against mucormycosis although they only achieve partial activity [3–5] . As a consequence of this lack of efficient antifungal drugs , mortality rates of mucormycosis remain higher than 50% and reach up to 90% in disseminated infections [6 , 7] . Another negative aspect of mucormycosis is its emerging condition . Only a few years ago , mucormycosis was considered a rare infection limited to immunocompromised patients suffering diabetes , organ transplant or other diseases associated with immunosuppression [8] . However , the current improvement in the diagnostic techniques has revealed an alarming number of mucormycosis cases in immunocompetent patients that have severe trauma ( e . g . burn patients , traumatic injuries ) , since it is now rarely misdiagnosed as aspergillosis [9] . Thus , the isolation of new strains that are capable of infecting healthy individuals and the increasing number of reported cases have raised the alarm on this emerging disease . Together , the lack of effective treatments and the emerging character of this devastating disease are urgently demanding new strategies to prevent and/or treat mucormycosis . The development of therapies to treat mucormycosis is restricted by the lack of knowledge about the disease and the organisms that cause the infection . One of the main reason explaining the scarce information about mucormycosis is the high reluctance of Mucorales to modern molecular genetics techniques . Among Mucorales , Rhizopus oryzae and Mucor circinelloides are two study models in which genetic transformation is available [10 , 11] . Study of pathogenesis in these two models has revealed iron uptake , spore size , spore coat proteins and dimorphism as virulence determinants in mucormycosis [12–18] . In M . circinelloides , along with genetic transformation , the application of molecular tools has allowed the dissection of its RNAi mechanism , which has become a useful tool for functional genetics in this fungus [19–22] . Besides its applications as a genetic tool , the RNAi mechanism of M . circinelloides has a regulatory role that controls complex physiological processes such as growth , sexual and asexual sporulation and death by autolysis [19 , 23–27] . Moreover , the extensive study of the RNAi mechanism in M . circinelloides led to the discovery of the first link between this endogenous regulatory mechanism and the unusual antifungal drug resistance of Mucorales [28] . This novel mechanism generates spontaneous resistance to the antifungal drug FK506 by epigenetic RNAi-mediated post-transcriptional silencing of the fkbA gene encoding the protein FKBP12 , which is the natural target of FK506 . As a result , the lack of target blocks the action of FK506 and the fungus becomes resistant to this drug , suggesting that similar mechanisms could be behind of the exacerbated resistance to antifungal drugs in Mucorales . The limited knowledge about Mucorales , mainly due to the phylogenetic distance and genetic differences of these basal fungi with other well-known fungi like Ascomycota and Basidiomycota [29] , together with the low efficiency of the current antifungal drugs , makes it urgent the development of novel strategies to study this group of organisms and , more specifically , the finding of new virulence determinants that could become future antifungal drug targets in Mucorales . Consequently , the main purpose of this work has been the establishment of a functional genomic approach based on the RNAi mechanism of M . circinelloides to select phenotypes relevant for the biology of Mucorales and related to virulence , and subsequently to identify the genes responsible for these phenotypes . RNAi has been used as a powerful reverse genetic tool to develop functional whole-genome studies in many organisms , including worms [30] , flies [31] and mammal cells [32] . In these reverse genetic approaches , a defined library is laboriously constructed by designing a silencing vector for each annotated gene of the studied organism . In these studies , the model organism requires an easy transformation method ready to be arrayed in large-scale assays in which each silencing vector/molecule is individually delivered . Unfortunately , this is not the case of M . circinelloides or any other emerging Mucoral . These inconveniences have led us to design a different approach that uses RNAi as a forward genetic tool in which a library representing M . circinelloides whole genome was constructed in a vector that silenced the cloned inserts . Transformation with this silencing library generated a collection of silenced transformants ready for phenotype screenings in a similar way as in classic chemical or insertional mutagenesis approaches . This transformant collection and the silencing library represent a new genetic tool in Mucorales for forward genetics and functional analysis at whole genome level . Using this approach we have isolated phenotypes related to virulence leading to the identification of two new virulence determinants in M . circinelloides , the enzyme Phospholipase D ( PLD ) and a Myosin 5 ( Myo5 ) motor protein , which are required for full virulence in Galleria mellonella and Mus musculus host models . Overall , this work illustrates a new approach to study virulence in Mucorales at the whole genome level . A vector capable of inducing RNAi from any random DNA fragment was designed previously to the construction of the gDNA library for phenotypic screening based on RNAi . RNAi can be triggered in M . circinelloides by using self-replicative plasmids containing either complete or fragmented genes with their own promoters , obtaining silencing frequencies ranging between 3% and 30% [21] . However , highest silencing frequencies ( nearly 95% ) can be achieved when the plasmid contains a strong promoter and hairpin structures that directly transcribe dsRNA [22] . To circumvent the limitations of constructing a hairpin producing vector for each gene of M . circinelloides genome , we designed a high-throughput silencing vector ( pMAT1700 ) with two convergent promoters and no terminator sequences that are flanking a multiple cloning site ( MCS ) in which random gDNA fragments can be cloned ( Fig 1A ) . In addition , a sequence of 0 . 5 kb of carB gene was cloned next to the MCS to be used as a reporter of silencing ( Fig 1A ) . This gene encodes a phytoene dehydrogenase involved in the production of β-carotene , a pigment responsible for the typical yellow color of M . circinelloides [33] . Triggering of RNAi after transformation by plasmids containing this reporter produces albino transformants that are easily detectable and also signalize silencing of any other sequence cloned next to it . Fragments of 0 . 5–4 kb were isolated from M . circinelloides gDNA partially digested with Sau3A and filled in with dGTP-dATP to avoid self-ligation . The genomic fragments thus obtained were ligated with vector pMAT1700 digested with XhoI and filled in with dCTP-dTTP to make their ends compatible with Sau3A filled fragments and avoid self-ligation , thus favoring the frequency of recombinant clones in the library [34] . Ligation mixtures were introduced into Escherichia coli cells to generate the genome-wide RNAi library ( Fig 1B ) consisting in roughly 83 , 000 clones , which determined a confidence level higher than 99% . Pooled plasmids were directly purified from the E . coli colonies and used to transform M . circinelloides MU402 ( pyrG- and leuA- ) strain . The empty vector pMAT1700 and a version of this vector lacking the carB fragment ( pMAT1701 ) were used as controls to monitor silencing efficiency . Up to sixty transformations following this approach were required to obtain a collection of 51 , 657 silenced transformants ( S1 Table ) , which ensured a 95% confidence level . Comparison of silencing frequencies obtained with the empty vector and the high-throughput silencing library showed a pronounced increase of carB silenced transformants among those obtained with the library ( 87% ) relative to the empty plasmid pMAT1700 ( 43% ) ( S1 Table ) . As expected , the plasmid pMAT1701 did not trigger silencing in any of the transformants ( S1 Table ) . The increase in silencing frequency among transformants obtained with the library could be explained if the 0 . 5 kb fragment of carB gene that is cloned between the two promoters was not long enough in the empty plasmid to allow efficient convergent transcription from both promoters . Nevertheless , once the convergent cassette assimilates new fragments in the library , the silencing efficiency increases close to the maximum previously observed with hairpin triggering molecules [22] . These results demonstrated a high silencing efficiency of our high-throughput library in M . circinelloides . The main purpose of generating a high-throughput functional genomic tool in M . circinelloides was to use it as a new approach to find unknown virulence determinants in Mucorales ( Fig 1B ) . In order to find candidate genes involved in M . circinelloides pathogenesis , we focused the screening of silenced strains on abnormal growth and morphology , since those aspects of fungal physiology have been related to pathogenesis in M . circinelloides and other fungi [14 , 35] . Special attention was paid to transformants growing as yeast-like colonies and showing altered dimorphism and strong reduction of the growth rate , as it is one of the few processes previously associated with virulence in M . circinelloides [13] . Plates from the transformations with the silencing library were directly screened for abnormal phenotypes ( 51 , 657 silenced transformants ) , resulting in the selection of fifteen transformants with different abnormalities . Later , the transformation plates were further incubated and vegetative spores were pooled together to obtain the collection of silenced spores harboring the high-throughput silencing library described in the previous section . A total of 1x104 viable spores from this collection were grown in new plates for a second screening , resulting in the selection of eleven abnormal candidates . In addition , the second screening confirmed that silencing is maintained in the collection of silenced spores , since the frequency of silenced colonies ( 79±4% of albino colonies ) was similar to the previously described in the original transformants ( S1 Table ) . Growth rate and sporulation efficiency of the twenty six isolated candidates from the two screenings were quantified and classified into five categories based on the different morphological abnormalities that they presented ( Fig 2 and Table 1 ) . The first category , the most abundant with 16 isolates , presented a reduced growth ( RG1-16 ) compared with control transformants , but wild-type sporulation . Two transformants presenting a highly reduced growth ( HRG1 and HRG2 ) were included in the second category , as they showed clear differences with the first category , including a reduced vegetative sporulation . The third category comprised five transformants that showed a strong lack of vegetative sporulation ( LVS1-5 ) . In addition to the lack of sporulation , some of these five transformants also presented a reduced growth similar to the first category ( Table 1 ) . The fourth category contained only one transformant that showed a yeast-like growth ( YLG1 ) . This transformant presented the slowest growth , forming small colonies similar to yeasts rather than mycelial colonies . The morphology of YLG1 under the optical microscope also showed strongly deformed cell walls incapable of forming regular filaments ( Fig 2 ) . These filaments appeared to be septated , although one of the main characteristics of Mucorales is their coenocytic mycelium . This contradictory observation could be explained if this transformant is immersed in a hyphae-yeast transition state in which the tip of the hyphae produces yeast cells that resemble a septated structure before the yeast cells are liberated ( Fig 2 , yeast-like growth ) . The last category included two transformants showing a satellite growth phenotype ( SG1 and SG2 ) . These two transformants grew slower than control transformants , producing long sporangia that bent to the media to form new colonies , acquiring this unusual satellite phenotype in transformation plates at pH 3 . 2 ( Fig 2 ) . When SG1 and SG2 were grown in MMC medium at pH 4 . 5 ( a rich medium but selective for uracil auxotrophy , [36] ) , they showed reduced growth and sporulation , but not the satellite phenotype . HRG and SG transformants also presented abnormal mycelia under the optical microscope , showing swollen hyphae with abnormal branching ( Fig 2 ) . Accordingly with the mechanism of silencing previously described in M . circinelloides [21] , the twenty six transformants showed a reversible phenotype and they lost the abnormalities when they were grown in a non-selective medium for several vegetative cycles , confirming that the phenotypes were caused by the silencing of some genes harbored in the plasmids . As this work focuses on the identification of new virulence determinants in M . circinelloides , we performed virulence tests with all the selected transformants in a heterologous host , Galleria mellonella , which was previously established as a host model for M . circinelloides [14] . The viability of larvae infected with two thousand spores was monitored at one day intervals for all the transformants except YLG1 , which was unable to produce spores for this assay and , therefore , yeast-like cells were used for the infection assay [13] ( Fig 3 and S1 Fig ) . Among the twenty six transformants , only three isolates were significantly less virulent than the virulent control strains , the two HRG transformants ( Fig 3A , HRG1 and HRG2 ) and the single YLG transformant ( Fig 3B , YLG1 ) . The isolation of these three transformants with reduced virulence confirmed that our RNAi-based functional genomics strategy can be used to select phenotypes related to virulence and pathogenesis in M . circinelloides . The self-replicative nature of M . circinelloides plasmids used to construct the RNAi libraries facilitates the identification of the silencing sequences responsible for the phenotypes in the selected transformants , since library plasmids are maintained as episomes . Thus , the gDNA sequence present in the silencing plasmids can be identified by PCR amplification and sequenced using oligonucleotides flanking the cloning site . Alternatively , silencing plasmid can be re-cloned in E . coli and sequenced . In order to validate this hypothetical forward genetic approach in Mucorales , five independent transformants ( HGR1 , HGR2 , YLG1 , SG1 and SG2 ) were selected for gene identification and validation of the silencing phenotype . Three transformants ( HGR1 , HGR2 and YLG1 ) were selected due to their avirulent phenotype , whereas the transformants presenting the satellite growing phenotype ( SG1 and SG2 ) were selected to demonstrate that genes involved in other physiological processes can also be identified following this approach . Amplifications from gDNA of these five transformants generated PCR products only in YLG1 , SG1 and SG2 . After purification and sequencing of these PCR products , the DNA sequences were analyzed and compared to the genome database of M . circinelloides v1 . 0 and v2 . 0 ( http://genome . jgi-psf . org/Mucci1/Mucci1 . home . html and http://genome . jgi-psf . org/Mucci2/Mucci2 . home . html , respectively ) . The two strains sharing the satellite growth phenotype , SG1 and SG2 , exhibited both equal size PCR products and DNA sequences , indicating that these two transformants harbored the same plasmid . The analysis of the sequence amplified from this plasmid revealed the presence of three different ORFs: ID 84675 ( CLIP-associated proteins ( CLASPs ) , v1 . 0 ) , ID 156742 ( intracellular protein transport , v2 . 0 ) and ID 145873 ( DNA repair protein RAD51/RHP55 , v2 . 0 ) ( Table 2 ) . The analysis of the sequence obtained from transformant YLG1 also unveiled a DNA insert containing two different ORFs: ID 51513 ( myosin class V heavy chain , v1 . 0 ) and ID 166338 ( no description in either v1 . 0 or v2 . 0 ) . For the analysis of transformants HGR1 and HGR2 , plasmid re-cloning in E . coli was required , revealing that both transformants shared a plasmid with the same insert sequence ( pMAT1726 ) . In this case , the sequence of the plasmid insert harbored only one ORF , ID 134906 , which encoded a Phospholipase D like protein ( v2 . 0 ) . The analysis of the sequences found in the plasmids of the five selected transformants has been summarized in Table 2 , which shows that six different candidate genes could be responsible for three selected phenotypes . In order to identify which genes are behind the phenotypes , a silencing validation experiment was performed for each of the six candidate genes . Five new silencing validation plasmids were engineered by cloning a 1 kb fragment of each candidate gene in the MCS of pMAT1700 ( Table 2 ) . After transformation of the recipient wild type strain with these five plasmids and pMAT1726 , only three plasmids reproduced the three phenotypes previously observed in the original transformants obtained with the high-throughput silencing libraries ( Table 2 ) . Silencing of gene ID 84678 resulted in the satellite growing phenotype previously observed in the transformants SG1 and SG2 , whereas the yeast like growth phenotype of YLG1 was reproduced only by silencing of gene ID 51513 . As expected , silencing of the only gene ( ID 134906 ) found in the transformants HRG1 and HRG2 resulted in the highly reduced growth phenotype . To confirm that the phenotypes obtained after the introduction of plasmids harboring sequences of genes IDs 84675 , 51513 and 134906 are due to the lack of function of these genes through a canonical RNAi mechanism , we checked the mRNA levels and the production of siRNAs for the three candidate genes in both the original transformants containing the plasmids from the high-throughput RNAi libraries and the transformants obtained with the validation plasmids ( Fig 4 ) . All transformants for the three genes showed a reduction of mRNA levels and a production of siRNA for the corresponding gene , confirming the expected mechanism of action of the RNAi high-throughput library . These results demonstrated that the RNAi high-throughput library can be used as a new means to perform forward genetics and functional genomics in the study of virulence of Mucorales . The approach of RNAi-based functional genomics in M . circinelloides resulted in the identification of two genes that could be new virulence determinants in Mucorales ( Fig 3 ) . The role of these two genes was confirmed through the generation of the corresponding knockout strains and the study of their phenotype and virulence in a heterologous host model . The gene ID 51513 ( v2 . 0 ) encodes a Myosin class V protein that contains the three characteristic domains of this protein family: a motor domain , the IQ motifs and the cargo-binding globular tail . Thus , the gene encoding M . circinelloides Myosin 5 was denominated mcmyo5 . In order of adding more evidence to the identity of mcmyo5 gene , we performed a detailed phylogenetic analysis that included myosin proteins identified in other fungi ( S3 Table ) . This analysis revealed that gene mcmyo5 encodes a myosin protein that is perfectly clustered among other fungal myosin 5 proteins ( S2A Fig ) . The second gene , ID 134906 ( v2 . 0 ) , encodes a Phospholipase D like protein ( accordingly denominated mcplD ) , that contains the characteristic domains ( C2 , PX , PH ) , the active site and other functionally important parts of the enzyme [37] . Similarly to mcmyo5 , we performed a detailed phylogenetic analysis that included phospholipase proteins identified in other fungi ( S4 Table ) . This analysis revealed that gene mcplD encodes a phospholipase protein that is perfectly clustered among other fungal phospholipases type D ( S2B Fig ) . In addition , the gene ID 84675 ( v1 . 0 ) was also mutated , as mentioned above , to prove that the strategy presented in this work is also valid to study other fungal processes different than virulence , and also as a control to prove that not all growth defects are related to reduced virulence . This gene , named as mcclasp , encodes a CLIP-associated protein like ( CLASPs ) , as the CLASP N-terminal domain is the main conserved region , which shares an 87% identity with a hypothetical CLASP protein of Mucor ambiguous . The disruptions of these three genes were carried out through the construction of knockout vectors designed to replace each candidate gene with pyrG gene , which was used as a selective marker ( S3 Fig ) . These knockout vectors contained an engineered cassette with adjacent regions of the target genes flanking the pyrG gene ( S3A Fig ) and were used to transform MU402 strain ( pyrG- , leuA- ) . After transformations , candidates presenting the phenotype previously associated with silencing of each gene were isolated and the disruptions analyzed by Southern blot analysis ( S3B Fig ) . The two transformants selected from mcplD disruption ( MU466 and MU467 ) and the transformant selected from mcclasp disruption ( MU464 ) only showed the DNA fragments corresponding to the correct integration of the disruption fragment at the corresponding loci ( S3B Fig ) , indicating that they were homokaryons for the mutant allele . In order to confirm the identity of the product encoded by the gene mcplD , activity of the enzyme PLD was measured in the mutant ΔmcplD and compared to the wild type strain ( “Phospholipase D Assay Kit” , from Sigma-Aldrich ) . This assay showed a significant reduction ( p = 0 . 0017 ) of PLD activity of almost 30% in the mutant strain , but not a total lack of PLD activity ( S4 Fig ) . These results could be explained if there are other proteins with similar activity in the crude extracts of this mutant . Regarding the deletion of mcmyo5 gene , two transformants showing the yeast-like growth phenotype were selected after transformation with a replacement cassette for the gene mcmyo5 . One of these transformants , MU468 , probably harbored a chromosomal rearrangement at the mcmyo5 locus , whereas the second one , MU465 , showed the correct pyrG insertion of 4 . 1 kb replacing mcmyo5 gene ( S3B Fig ) . However , it was impossible to obtain a homokaryotic knockout strain for the gene mcmyo5 , as the transformant containing the mutant allele maintained some wild type nuclei even after ten vegetative cycles on selective media ( 3 . 4 kb fragment in S3B Fig ) . These results suggested that mcmyo5 gene may play an essential role in the viability of M . circinelloides and a homokaryotic state of the mutant nuclei might be lethal . The heterokaryotic strain containing mcmyo5 mutant nuclei was named Δmcmyo5 ( - ) ( + ) . The phenotype of each knockout strain was equivalent to those observed in the silencing transformants ( Fig 5A ) . The three mutants showed a reduction of growth and sporulation rates , as well as an increase in the production of β-carotene ( Fig 5C , 5D and 5E , respectively ) . The accumulation of β-carotene in the three mutants might be due to the growth stress present in these strains , since diverse stress factors have been previously linked to the production of β-carotene in other organisms [38] . Regarding virulence , infection assays in G . mellonella larvae with sporangiospores from mutants ΔmcplD and Δmcclasp , and yeast cells from mutant Δmcmyo5 ( - ) ( + ) , revealed a significant reduction in virulence of mutants Δmcmyo5 ( - ) ( + ) and ΔmcplD but not in Δmcclasp ( p = 0 . 0007 , p = 0 . 0002 and p = 0 . 4420 , respectively ) ( Fig 5B ) , as expected from the results previously obtained with the strains containing silencing vectors ( Fig 3 ) . The moth G . mellonella is a convenient model to study virulence when numerous candidates have to be tested . However , the immune system of this invertebrate model presents several differences compared to vertebrates , especially with warm blooded animals like mammals . Thus , the avirulent phenotype of mutants Δmcmyo5 ( - ) ( + ) and ΔmcplD was tested in a mouse model , where temperature and immune system components and action mechanisms are similar to humans . Yeast cells and spores of mutants Δmcmyo5 ( - ) ( + ) and ΔmcplD ( respectively ) were injected in immunodepressed mice and survival was daily monitored during twenty days after the infection with inocula of both 1x105 ( S5 Fig ) and 1x106 ( Fig 6 ) . Both inocula generated similar results , confirming the avirulent phenotype of mutant ΔmcplD in the murine model with a strong statistical significance ( p = 0 . 0081 in S5A Fig and p = 0 . 0065 in Fig 6A ) . However , although a reduced virulence was also observed for the heterokaryotic strain Δmcmyo5 ( - ) ( + ) compared to the wild type R7B strain , difference was not statistically significant ( p = 0 . 1595 in S5B Fig and p = 0 . 0526 in Fig 6B ) . This was probably due to the long time-course of the virulence assay in mice , since heterokaryotic Δmcmyo5 ( - ) ( + ) cells might segregate to a wild type phenotype by losing mutant nuclei when grown under non-selective conditions . In fact , growing of mutant Δmcmyo5 ( - ) ( + ) in non-selective culture medium gave rise to patches of wild-type phenotypes after few days of incubation ( S6A Fig ) . To test this hypothesis , retrieved CFUs from infected organs of both agonizing mice that showed signs of an imminent death and apparently healthy mice were analyzed in a Southern blot assay that distinguishes between the mutant and wild type genotypes ( S6B Fig ) . Quantification of the proportion of wild type and mutant nuclei in these retrieved CFUs showed correlation between the segregation to wild type genotype and the restitutions of virulence , which supported the role of the gene mcmyo5 in the pathogenesis of M . circinelloides ( S6C Fig ) . In order of acquiring more insights about the virulence of the strains ΔmcplD and Δmcmyo5 , the fungal burden was quantified in the relevant organs of mice infected with wild type R7B and both mutant strains . Quantification of fungal gDNA on relevant target organs ( brain and lung ) revealed prevalence of R7B in tissues from mice infected with both yeast and spore forms , showing a more significant presence in lung tissues ( Fig 6C and 6D ) . In particular , the presence of R7B in lung tissue was higher at day 2 post infection ( Fig 6C ) than at five days ( Fig 6D ) , indicating a decrease of fungal biomass in mice over time . Such fungal burden decrease was less accentuated on infection with R7B yeasts , suggesting that this fungal form is more persistent in mice during the infection progression . Despite these differences in fungal load , symptoms and mortality rates were similar after infection with R7B spores and yeasts ( Fig 6A and 6B ) . Conversely , a low amount of fungal DNA in mice infected with NRRL3631 and mutant strains ΔmcplD and Δmcmyo5 was detected , even below the limit of detection ( 0 . 005 ng ) after five days of infection ( Fig 6C and 6D ) . These results , together with survival outcomes , indicated a greater capacity of R7B strain to infect and invade mice tissues , causing higher mortality rates than the mutants ΔmcplD and Δmcmyo . The determinants of virulence in M . circinelloides have been studied during the initial interaction of spores with macrophages , in which the main factor distinguishing virulent and avirulent strains was the size of the spores [14] . Therefore , spore and yeast cell sizes of virulent and avirulent strains were determined . The size of yeast cells produced by the avirulent control strain NRRL3631 ( + ) was pronouncedly reduced compared with yeasts produced by virulent control strain R7B ( - ) ( S7B Fig ) , in the same manner as occurred with the size of the spores [14] ( S7A Fig ) . However , the sizes of the spores or yeast cells of the mutant strains ΔmcplD , Δmcmyo5 ( - ) ( + ) and Δmcclasp were not significantly reduced when compared to the virulent strain R7B ( S7 Fig ) . These results suggested that the reduction of virulence in the strains ΔmcplD and Δmcmyo5 ( - ) ( + ) might be due to other factors that are independent of the initial size of the fungal spore or yeast cells . In order to find these factors , the interaction between macrophages and the mutant strains ΔmcplD and Δmcmyo5 ( - ) ( + ) was also studied . Spores and yeast cells ( from ΔmcplD and Δmcmyo5 ( - ) ( + ) , respectively ) were co-cultured with the mouse macrophage cell line J774A . 1 ( ATCC , TIB-67 ) , during four hours . At this time of interaction , all the spore/yeast cells have been phagocytized by macrophages and virulent strains initiate germination and polar growth trying to escape before being inactivated [14] . Thus , we quantified the germination rate and polarity index ( a quotient between cell length and cell width [39] ) as a measure of virulence of the different strains tested here . A germination delay and a reduced polarity index were observed in mutants ΔmcplD and Δmcmyo5 ( - ) ( + ) relative to wild type ( Fig 7 ) . Mutant ΔmcplD presented a germination delay and polarity index similar to the avirulent strain NRRL3631 , whereas mutant Δmcmyo5 ( - ) ( + ) showed a reduction of the polarity index even more pronounced than the avirulent control strain , as well as a similar germination delay . Mutants grown in absence of macrophages also presented the same delay in germination and polar growth . The knockout strain in the mcclasp gene showed a non-significant reduction of the polarity index and no changes in the germination rate when compared to the wild type . As mutant Δmcclasp is not affected in virulence , these results highlight the relevance of spore germination and hyphal growth rates within macrophages for M . circinelloides virulence . Here , we have developed a new approach based on RNAi high-throughput libraries that allows the identification of genes responsible for virulence in M . circinelloides . This work represents the first application of a functional genomic approach to identify virulence determinants in Mucorales . RNAi-based reverse genetics has allowed successful whole-genome functional studies in animals ( see Introduction ) , but this technology presents several restrictions in fungi that have prevented its use at whole genome level . Following this reverse genomic approach , the only known study carried out in fungi was firstly reported in a plant pathogen , Magnaporthe oryzae , in which the function of a calcium-signaling family of 37 genes was studied using silencing plasmids for each gene of this family [40] . Our platform presents an opposite approach in which a collection of phenotypes are generated by transformation with a whole-genome RNAi library and afterward the genes responsible for a particular phenotype are identified . This approach represents the first forward genetic strategy to study gene function at the whole genome level in Mucorales . Following this strategy , a general screening of a collection of silenced M . circinelloides transformants led us to the isolation of twenty-six strains showing a wide range of distinct phenotypes in fungal processes such as virulence , growth and sporulation . One advantage of this approach versus traditional chemical or insertional mutagenesis is that essential genes could be isolated , since RNAi usually reduces the expression of the target gene rather than a total inhibition . Another advantage of this RNAi-based functional genomic platform is the possibility of designing conditional screenings . The screenings shown here were performed under non-specific growth conditions , as it was intended to prove the general utility of the platform to apply functional genomics and to show how genes related to virulence can be identified from general screenings . However , the collection of silenced transformants offers the opportunity to carry out specific screenings under particular conditions to select phenotypes related to virulence , such as yeast growth , thermotolerance , protease over-production , etc . Moreover , another attractive advantage of this approach is its exportability to other Mucorales or fungi from different groups like Ascomycota and Basidiomycota , since the only conditions required are the existence of a functional RNAi mechanism and an efficient transformation method , which are both present in many fungal groups [41] . The application of the RNAi-based functional genomic platform has facilitated the identification of the genes mcplD as an essential factor to maintain full virulence in M . circinelloides , both in a heterologous model like G . mellonella and a murine host model . Knockout mutants in mcplD showed deficient growth accompanied with sporulation reduction and increased production of β-carotene , and more importantly , reduced virulence in the host models G . mellonella and M . musculus . The gene mcplD codes for a Phospholipase D enzyme ( PLD ) , a well-known protein that is highly conserved in different organisms . This enzyme catalyzes the hydrolysis of the phosphodiester bond of glycerophospholipids to generate phosphatidic acid and a free headgroup [37] . Phosphatidic acid functions as an intracellular lipid messenger that activates different target kinases , which in turn activate a broad range of cellular processes such as receptor signaling , control of intracellular membrane transport , and reorganization of the actin cytoskeleton [37] . This pleiotropic function of PLD could explain the complex phenotype observed in the M . circinelloides mutant for the mcplD gene . In addition , PLD has been described as a major virulence factor in Corynebacterium pseudotuberculosis , being involved in macrophage death and systemic dissemination of this pathogen [42] . In fungi , Aspergillus fumigatus PLD regulates its internalization into lung epithelial cells , and the pld gene of Purpureocillium lilacinum is significantly up-regulated during infection of Meloidogyne incognita eggs [43 , 44] . In M . circinelloides , mcplD may regulate some signaling pathways involved in germination and hyphal growth , since mutants in this gene showed delayed germination and reduction of the polarity index . The other gene related to virulence that has been found in this study , mcmyo5 encodes a processive cargo transporter belonging to the Myosin V Class ( Myo5 ) . Myosins play important roles in morphogenesis of filamentous fungi , since they are involved in the establishment and/or maintenance of polarity [45] . Among Myosins , Myo5 supplies a constant transport of organelles , membranous cargo , secretory vesicles , mRNA , lipid and protein vesicles on actin tracks [46] . In M . circinelloides , Myo5 might play an essential role in viability , since the knockout mutant was viable only as a heterokaryon containing a small proportion of wild type nuclei . This heterokaryotic strain was unable to produce regular hyphae and presented a yeast-like phenotype with no polar growth . Likely , the lack of a continuous transport mediated by Myo5 impairs the correct formation of the mycelium . Since filamentous growth is a major determinant of virulence in M . circinelloides [13] , the yeast-like knockout strain Δmcmyo5 ( - ) ( + ) presented a pronounced reduced virulence in G . mellonella . Similarly , in the dimorphic plant pathogenic fungus Ustilago maydis , a single Myosin class V protein encoded by myo5 was involved in hyphal growth and pathogenicity [47] . However , the reduction of virulence shown by the Δmcmyo5 ( - ) ( + ) heterokaryotic mutant in a murine host model did not reach the significance level stablished , although the reduction in the fungal burden was similar to the mutant mcplD . These partially contradictory results obtained from the two host models could be precisely due to the heterokaryotic state of mutant Δmcmyo5 ( - ) ( + ) . The virulence assays in G . mellonella are performed during eight days , whereas in M . musculus the assays are prolonged until the twentieth day , which could be time enough for the heterokayotic strain under non selective conditions to segregate and lose mutant nuclei , reverting to the wild type phenotype . According to this hypothesis , wild type patches segregating from Δmcmyo5 ( - ) ( + ) mutant cells growing under non selective culture conditions are easily observed after several days of incubation . In addition , a correlation between the segregation to wild type genotype and the restitutions of virulence was observed after genotyping the nuclei proportion of several retrieved CFUs , which further supported a role of gene mcmyo5 in the virulence of M . circinelloides . Similar results were obtained in Rhizopus oryzae when FTR1 gene was disrupted by double cross-over homologous recombination , but multinucleated R . oryzae could not be forced to segregate to a homokaryotic null allele [48] . The heterokaryotic strain Δmcmyo5 ( - ) ( + ) showed the strongest reduction of polar growth and germination rates when phagocyted by macrophages , since it was tested during only four hours in non-selective medium , which is not time enough for segregation . Our results from the in vitro analysis and the intravenous infection model showed the potential role of mcmyo5 in virulence of M . circinelloides . Although Myo5 is a highly conserved protein , which disqualifies it as a specific antifungal target , the fungal cargo domain and the proteins that interact with this domain could represent a promising target for future antifungal developments . The analysis of germination and polar growth of Δmcmyo5 ( - ) ( + ) and ΔmcplD mutants and their interaction with mouse macrophages revealed a delayed germination and reduced polarity index of those strains relative to the wild type strain , although the size of the infecting particles ( spores or yeast cells ) was not reduced in these mutants . These results suggested that a big size of the infecting particle ( spore or yeast cells ) is not enough to counteract a delayed germination and reduced polarity index . Time of germination and polarity index are two values that measure the velocity of the pathogen growing inside the macrophage and escaping from it . Thus , a possible explanation of the reduced virulence observed in ΔmcplD and Δmcmyo5 ( - ) ( + ) mutants might be that delayed germination and reduced polarity index concede macrophages time enough to inactivate the pathogen before it escapes from its cytoplasm . Along with the size of the spore previously described [14] , our work demonstrated that the time required for germination and the hyphal elongation rate , measured as polarity index , are two new factors to be considered in the analysis of M . circinelloides virulence . Besides genes related to virulence , a third gene named mcclasp was selected from the RNAi-based functional genomic screenings for further studies . Mutant Δmcclasp presented a complex phenotype affecting several fungal processes like vegetative growth , carotene production and sporulation in a similar manner to the Δmcmyo5 ( - ) ( + ) and ΔmcplD mutants , although the Δmcclasp strain was not affected in virulence . Besides that , the main differences between Δmcclasp and those mutants were the formation of sporangiophores , which were normal in length in Δmcclasp mutants , allowing the formation of satellite colonies in low pH media , along with a less pronounced reduction of growth and sporulation . The gene product of mcclasp is similar to CLASP proteins that are involved in the attachment of microtubules to the cell cortex in animals and plants , thereby contributing to self-organization of cortical microtubules [49] . During mitosis , CLASP proteins control the interactions of astral microtubules with the cell cortex , helping the proper positioning and orientation of the spindle [50] . This important role of CLASP proteins during cell division might be behind the reduced growth and decreased sporulation observed in the Δmcclasp mutants . In addition , the role of CLASP proteins in the stability of microtubules is essential for the motility of motor proteins , such as kinesins and dyneins . Kinesins participate in the maintenance of the polarity of filamentous fungi ( reviewed by Harris , 2006 ) , which could explain the reduction in the polarity index of the Δmcclasp mutant relative to the wild type strain . However , unlike Myo5 , kinesins does not seem to be involved in polarity establishment in M . circinelloides , since strains without CLASP protein are still able to generate hyphal growth , although their elongation rate is lower than the wild type strain . Mutant Δmcclasp showed a reduced growth rate and vegetative sporulation and an increase in β-carotene production compared to control strain R7B ( Fig 5C , 5D and 5E , respectively ) , similarly to the phenotypes observed in ΔmcplD and Δmcmyo5 mutants . However these phenotypes are not associated with reduced virulence in mutant Δmcclasp , indicating that growth defects are not necessarily linked to attenuated virulence and suggesting a possible specific role of mcplD and mcmyo5 genes in pathogenesis . The identification and analysis of mcclasp gene demonstrated that along with virulence , other fungal processes can be studied and genetically dissected with the RNAi-based functional genomic platform developed in this work . In summary , the absence of classic molecular genetic tools and the scarce information about virulence and pathogenesis in Mucorales encouraged us to develop a robust system for RNAi-based functional genomics in M . circinelloides . It is a new genetic tool that can be used in the study of a wide range of biological processes , including the identification and study of genes related to virulence and pathogenesis . As a result of its implementation , we have identified new virulence determinants in Mucorales that could represent new targets for future antifungal therapies . The leucine auxotroph R7B , derived from the ( - ) mating type M . circinelloides f . lusitanicus CBS 277 . 49 ( syn . Mucor racemosus ATCC 1216b ) , was used as the wild type strain . Strain MU402 is a uracil and leucine auxotroph derived from R7B used as recipient strain of the silencing library [36] . The M . circinelloides f . lusitanicus strain of the ( + ) mating type NRRL3631 was used in virulence assays as an avirulent control . M . circinelloides cultures were grown at 26°C in complete YPG medium or in MMC medium as described previously [36] . Media were supplemented with uridine ( 200 μg/ml ) when required . The pH was adjusted to 4 . 5 and 3 . 2 for mycelial and colonial growth , respectively . Transformation was carried out as described previously [10] . Macrophage cells , J774A . 1 ( ATCC , TIB-67 ) , were cultured in L15 medium ( Capricorn Scientific GmbH ) supplemented with 10% FBS at 37°C and without CO2 supplementation . Plasmid pMAT1726 was recovered from gDNA of HRG1 and HRG2 transformants . In order to construct dsRNA-expressing vectors with the target candidate genes , plasmid pMAT1700 was used as cloning vector . Insert fragments corresponding to the 5’ end of each candidate gene ( 0 . 5–2 kb ) were amplified with primers containing NotI and XhoI restriction sites to facilitate cloning into pMAT1700 ( S2 Table ) . Plasmid pMAT828 harbors a 2 kb fragment of gene ID 51513 which was PCR-amplified using primer pairs FYL1 and RYL1 ( S2 Table ) . Plasmid pMAT798 contains a 0 . 9 kb fragment of gene ID 166338 amplified by PCR reactions using primer pairs FYL1 . 2 and RYL1 . 2 ( S2 Table ) . To identify the candidate gene responsible for the satellite growth phenotype , three different plasmids were constructed: pMAT823 , pMAT824 and pMAT825 . These plasmids contain 1 . 2 kb , 0 . 9 kb and 0 . 5 kb fragments corresponding to genes ID 84675 , ID 156742 and ID 145873 , which were amplified using primer pairs FYL10 . 1/RYL10 . 1 , FYL10 . 2/RYL10 . 2 and FYL10 . 3/RYL10 . 3 , respectively ( S2 Table ) . To calculate the coverage of the genomic libraries in the genome of M . circinelloides and confidence levels , we followed the formula: N = ln ( 1-P ) /ln ( 1-f ) ; where N is the necessary number of recombinants , P is the desired probability that any fragment of the genome is represented in the library at least one time , and f is the fractional proportion of the genome in a single recombinant . “f” can be further shown to be f = i/g , where i is the insert size and g is the genome size [51] . To disrupt the candidate genes , a pyrG selective marker ( 2 kb fragment amplified from gDNA using primers F-pyrG and R-pyrG ( S2 Table ) was fused with adjacent sequences of the candidate coding regions using fusion PCRs , generating a gene replacement fragment . This fragment was cloned into pGEMT-easy vector ( Promega ) and used to disrupt the candidate genes via homologous recombination . Plasmid pMAT833 was constructed to disrupt the mcclasp gene . It contains a 4 . 2 kb fragment that includes the pyrG gene flanked by 1 . 3 kb of upstream and downstream sequences of mcclasp gene , amplified with primers FYL10U/RYL10-pyrG and FYL10-pyrG/RYL10D ( S2 Table ) , respectively . The 4 . 2 kb fusion fragment was amplified with internal primers FYL10 and RYL10 ( S2 Table ) . Plasmid pMAT832 was constructed to disrupt mcmyo5 gene , following the same strategy described for pMAT833 , but using primers FYL1U/RYL1-pyrG and FYL1-pyrG/RYL1D to amplify 1 . 3 kb of upstream and downstream sequences of the mcmyo5 gene , respectively ( S2 Table ) . In the case of gene mcplD , plasmid pMAT1733 was constructed also following the same fusion strategy , but with the specific primers FPLD/RPLD-pyrG and RPLD/FPLD-pyrG , for two fragments of 0 . 95 kb from upstream and downstream regions of mcplD ( S2 Table ) , respectively . Genomic DNA from M . circinelloides mycelia was extracted as previously described [36] . Recombinant DNA manipulations were performed as reported [52] . Total RNA was extracted from mycelia grown during 48 hours at 26°C in liquid MMC pH 4 . 5 medium under light conditions using RNeasy Plant Mini Kit following the supplier’s recommendation ( Qiagen ) . Southern blot and Northern blot hybridizations were carried out under stringent conditions [22] . DNA probes were labeled with [α-32P] dCTP using Ready-To-Go Labeling Beads ( GE Healthcare Life Science ) . For Southern and Northern blot experiments , DNA probes were directly amplified from genomic DNA using the primer pairs FPLD/RPLD-pyrG , FYL10/RYL10-pyrG and FYL1N/RYL1-pyrG for genes mcplD , mcclasp and mcmyo5 , respectively ( S2 Table ) . For siRNA analysis , small RNA samples were extracted from mycelia grown 72 hours in liquid MMC medium pH 4 . 5 at 26°C using the miRVana kit ( Ambion ) , following the instructions of the supplier . Northern blots for siRNAs were performed as previously described using antisense specific riboprobes generated by in vitro transcription of the DNA probes described above ( MAXIscriptsT7 , Ambion ) [21] . Quantifications of signal intensities were estimated from autoradiograms using a Shimadzu CS-9000 densitometer and the ImageJ application ( rsbweb . nih . gov/ij/ ) . Computational phylogenetic analyses were performed using Phylogeny software ( http://phylogeny . lirmm . fr ) [53] . Multiple protein sequence alignments were conducted with ClustalW program and phylogenetic trees were inferred by maximum likelihood statistical methods using a bootstrapping procedure of 1000 iterations . Vegetative sporulation , growth rate , carotene production and virulence measurements were carried out as previously described [14 , 19 , 36 , 54] . Interactions between different strains of M . circinelloides and J774A . 1 macrophage cells were carried out in L15 medium , during 4 hours at 37°C . Regarding polarity index , ten images were taken from each interaction and a total of fifty germinating spores were measured from each image with ImageJ [55] . From the same images , germination was calculated considering germinated spores all those that presented a protuberant bud from the spherical spore . For the phospholipase D activity measurements , spores of the wild type strain and mutant mcplD were grown in MMC medium at pH = 4 . 5 during six hours . The mycelia from five biological replicates were filtrated , washed and weighted to perform the assay with exactly 100 mg of biomass from each strain . The virulence assays in G . mellonella were performed by injection of 5 μl of phosphate buffered saline ( PBS ) containing 2000 spores or 20 , 000 yeast cells into the wax moth larvae ( 10 per strain ) . For the murine host model , groups of 8 four-week-old OF1 male mice ( Charles River , Criffa S . A . , Barcelona , Spain ) weighing 30 g were used . Mice were immunosuppressed 2 days prior to the infection by intraperitoneal ( i . p . ) administration of 200 mg/kg of body weight of cyclophosphamide and once every 5 days thereafter . Animals were housed under standard conditions with free access to food and water . Mice were challenged intravenously ( i . v . ) via the lateral tail vein with a suspension consisting on 1x105 sporangiospores or 1x105 yeast cells per animal . Animals were checked twice daily for 20 days . Surviving animals at the end of the experimental period or those meeting criteria for discomfort were euthanized by CO2 inhalation . Significance of mortality rate data was evaluated by using the Kaplan-Meier ( Graph Pad Prism 4 . 0 for Windows; GraphPad Software , San Diego California USA ) . Differences were considered statistically significant at a P value of <0 . 05 . For absolute DNA quantification and genotyping , organs were ground up on liquid nitrogen and gDNA was extracted as previously described [56] . For DNA quantification by real-time PCR ( qRT-PCR ) specific primers of M . circinelloides chitin synthase gene ( ID153118 ) and mice β2 microglobulin gene ( ID12010 ) were used ( S2 Table ) . Samples analyses were carried out in triplicate in 15 μl PCR reactions containing 180 ng of test sample gDNA form three individuals using SybrGreen kit ( Fast SYBR Green Master Mix -ABI ) in a StepOne Real-Time PCR System ( ABI ) . gDNA from non-infected mice was used as negative control . Relative amount of fungal and mice gDNA was quantified on the basis of their standard curves , elaborated with known fungal DNA concentrations ( 0 . 005 ng—10 ng ) in a background of 150 ng of non-infected mice gDNA and mice DNA concentrations ( 1 ng—200 ng ) and their corresponding amplification cycle threshold ( Ct ) . Animal care procedures were supervised and approved by the Universitat Rovira i Virgili Animal Welfare and Ethics Committee . The experimental animal facilities are registered under reference T9900003 of the Generalitat de Catalunya in compliance with the regulations of Real Decreto 53/2013 , of February 1st ( BOE of 8 February ) . Procedures included into the project number 280 were supervised and approved by L . Loriente Sanz ( ID 39671243 ) of the Veterinary and Animal Welfare Advisory of the Universitat Rovira i Virgili Animal Welfare and Ethics Committee ( Reus , Spain ) .
Mucormycosis is an infectious disease caused by organisms of the order Mucorales . It is a lethal infection that is raising the alarm in the medical and scientific community due to its high mortality rates , unusual antifungal drug resistance and its emerging character . Among the reasons explaining the nescience about this disease is the lack of knowledge on the biology of the organisms that cause mucormycosis , which is encouraged by the reluctance of these species to genetic studies . In this work , we have developed an RNAi-based functional genomic platform to study virulence in Mucorales . It is a powerful tool available for the scientific community that will contribute to solve the reluctance of Mucorales to genetic studies and will help to understand the genetic basis of virulence in these organisms . Secondly , and as a proof of concept , we have used this genetic tool to identify two new virulence determinants in Mucor circinelloides . Lack of function of these determinants delays germination and growth of spores , conceding time to macrophages for the inactivation of the pathogen . The two genes identified , mcplD and mcmyo5 , represent promising targets for future development of new antifungal therapies against mucormycosis .
You are an expert at summarizing long articles. Proceed to summarize the following text: Colonization of the human nose by Staphylococcus aureus in one-third of the population represents a major risk factor for invasive infections . The basis for adaptation of S . aureus to this specific habitat and reasons for the human predisposition to become colonized have remained largely unknown . Human nasal secretions were analyzed by metabolomics and found to contain potential nutrients in rather low amounts . No significant differences were found between S . aureus carriers and non-carriers , indicating that carriage is not associated with individual differences in nutrient supply . A synthetic nasal medium ( SNM3 ) was composed based on the metabolomics data that permits consistent growth of S . aureus isolates . Key genes were expressed in SNM3 in a similar way as in the human nose , indicating that SNM3 represents a suitable surrogate environment for in vitro simulation studies . While the majority of S . aureus strains grew well in SNM3 , most of the tested coagulase-negative staphylococci ( CoNS ) had major problems to multiply in SNM3 supporting the notion that CoNS are less well adapted to the nose and colonize preferentially the human skin . Global gene expression analysis revealed that , during growth in SNM3 , S . aureus depends heavily on de novo synthesis of methionine . Accordingly , the methionine-biosynthesis enzyme cysteine-γ-synthase ( MetI ) was indispensable for growth in SNM3 , and the MetI inhibitor DL-propargylglycine inhibited S . aureus growth in SNM3 but not in the presence of methionine . Of note , metI was strongly up-regulated by S . aureus in human noses , and metI mutants were strongly abrogated in their capacity to colonize the noses of cotton rats . These findings indicate that the methionine biosynthetic pathway may include promising antimicrobial targets that have previously remained unrecognized . Hence , exploring the environmental conditions facultative pathogens are exposed to during colonization can be useful for understanding niche adaptation and identifying targets for new antimicrobial strategies . Staphylococcus aureus is a major cause of human invasive infections ranging from superficial skin and soft tissue infections to severe disseminated diseases such as sepsis and endocarditis [1] . S . aureus is also a human commensal and part of the microbiota in healthy individuals , which facilitates its access to sterile tissues via open wounds and catheter entry sites . S . aureus can be isolated from various human body surfaces such as the pharynx , axillae and perineum but its main ecological niche and reservoir is known for long to be the human nose [2]–[4] . In contrast , coagulase-negative staphylococci ( CoNS ) , such as Staphylococcus epidermidis , have a much lower virulence potential and use different areas of the skin as their major habitats [5] . The basis of staphylococcal host and niche-specificity has remained unknown . Analysis of nasal carriage over long time periods has identified three types of S . aureus carriers [6] . About 20% of the human population can be regarded as non-carriers , who are never or only in very rare instances colonized with low bacterial numbers . In contrast , intermittent carriers show alternating periods of non-carrier status and colonisation by various S . aureus strains . The number of bacteria per isolation can be highly variable . The third group of roughly 20% persistent carriers is characterised by the presence of S . aureus in nearly all nasal swabs , usually at high bacterial numbers and with one specific strain per person over time . Recently , it has been suggested to distinguish only between carriers and non-carriers because of similar S . aureus nasal elimination kinetics and anti-staphylococcal antibody profiles in intermittent- and non-carriers [7] . Recent studies have shown that being an S . aureus carrier bears a higher risk of invasive S . aureus infections , predominantly by the carriers' own strain [8] , but a lower risk of infection-associated mortality compared to being a non-carrier [9] . The reasons for the underlying predisposition , which may involve individual differences in epithelial ligands for bacterial adhesins , local host defense , or availability of nutrients in the nose , have remained unclear . While some polymorphisms in immunity-related genes are weakly associated with the carrier status , the human predisposition appears to have multifactorial reasons [10] . On the bacterial side several factors required for nasal colonization have been identified . Wall teichoic acid ( WTA ) polymers at the staphylococcal surface [11] , [12] and cell-wall anchored proteins , such as ClfB [13] , [14] and IsdA [15] , have been found to be required for nasal colonization and adhesion to nasal epithelial cells . While the epithelial receptor for WTA is still unknown , ClfB and IsdA bind to cytokeratin 10 and loricrin , major components of human squamous epithelial cells , and IsdA also binds to the matrix protein involucrin [15] , [16] . Recently , up-regulation of WTA-biosynthetic genes tagO and tarK and of clfB and isdA during nasal colonisation has been shown in nasal swab samples from human volunteers [17] and in the cotton rat model of S . aureus nasal colonisation [18] , underscoring the importance of these factors in nasal colonization . S . aureus encounters iron-limiting conditions in the nose because isdA expression is strictly dependent on iron limitation [19] , and haemoglobin has recently been shown to promote S . aureus nasal colonization [20] . The human body can be regarded as a chemostat , where the nutrients required for bacterial growth are replenished over time and allow growth of bacteria within human microenvironments [21] . While several S . aureus nasal adhesion factors have been studied in the past , nothing is known about the growth conditions in nasal fluid such as the availability of carbon and nitrogen sources and the metabolic activities of S . aureus in its nasal habitat . Knowledge of metabolite availabilities and utilisation patterns could direct the identification of important metabolic enzymes that are essential during infection or colonization by S . aureus and could serve as targets for new antibiotics . Along this line enzymes from the folic acid biosynthetic pathway or the isoleucyl-tRNA synthetase are valuable targets for widely used anti-staphylococcal antibiotics such as cotrimoxazole or mupirocin , respectively . Mupirocin is frequently used to eliminate S . aureus from the noses of high-risk patients [22] , but the increasing resistance to mupirocin [23] and almost all antibiotics used to prevent or treat staphylococcal infections puts urgency to the development of new antimicrobial compounds . For this purpose the most critical and ‘drugable’ metabolic pathways of S . aureus need to be identified . In this study we elucidated the abundance of potential nutrients for S . aureus in the human nose by metabolomics analysis of nasal secretions . We found a high diversity but low concentrations of metabolites and no significant differences in the composition of secretions from S . aureus carriers and non-carriers . A synthetic nasal medium ( SNM3 ) was composed based on the metabolomics data . S . aureus growth in SNM3 led to similar gene expression patterns as during in vivo colonization . While S . aureus isolates grew steadily in SNM3 , CoNS did not , indicating that S . aureus is particularly well adapted to life in the human nose . Analysis of global gene expression in SNM3 revealed that the methionine-biosynthetic pathway may be a critical target for new anti-colonization drugs . In support of this notion an inhibitor of methionine biosynthesis had antimicrobial activity against S . aureus in SNM3 but not in complex media . The inhibitor's staphylococcal target gene was strongly up-regulated during human nasal colonization , and deletion of the target gene led to reduced colonisation ability of the respective mutant in the cotton rat colonisation model . Thus , deciphering the in vivo metabolism of pathogens represents a valuable strategy for defining new antimicrobial targets . The abundance of potential nutrients in nasal secretions has never been described . In order to explore the metabolic lifestyle of S . aureus during nasal colonization , the amounts of small organic compounds in secretions from eight volunteers who were not carriers of S . aureus , were analyzed by metabolomics ( Fig . 1A , Table 1 ) . Similar amounts of amino acids and organic acids were found in the micromolar range in the eight samples . Urea was by far the most abundant organic substance at concentrations of 2 . 5–7 . 5 mM . Glucose exhibited the highest concentrations among carbohydrates with large variation between 35 µM and ca . 1 mM , while only very low amounts of other mono- or disaccharides were detected . Most of the proteinogenic amino acids and ornithine were present at average concentrations between 50 and 150 µM , while tryptophan and cysteine were detected only at very low concentrations around 10 µM . Some amino acids were not found ( methionine , glutamine , tyrosine , isoleucine , asparagine , and aspartate ) . Whereas the carboxylic acids fumarate , malate and citrate were detected at about 5–25 µM , pyruvate and succinate were usually present at much higher concentrations ( Table 1 ) . No lactate and only trace amounts of several other substances , including fatty acids , cholesterol and pyrimidines , were found ( Fig . 1B ) . To confirm the observed results and to investigate potential differences between S . aureus carriers and non-carriers , the metabolite composition in nasal secretions from six S . aureus non-carriers were compared with those from seven S . aureus carriers . The metabolite patterns of carriers exhibited a similar degree of variation as in non-carriers but no significant difference between the two groups of donors for any of the detected compounds ( Fig . 1C ) . Thus , the human S . aureus carrier status is not associated with a notable difference in nasal nutrient supply , and the metabolic activities of S . aureus do not seem to have a major impact on the overall metabolite concentrations in the nose . The average nutrient concentrations found in human nasal secretions were used to compose a synthetic medium for simulating S . aureus growth in the nose ( Table 1 ) . Amino acids and glucose were added to SNM at the upper limit of the detected concentration ranges found in the samples but not at higher amounts than twice the mean concentration values . The amounts of inorganic ions in nasal secretions have previously been described [24] , and these values served as a basis for the salt content of SNM . The synthetic medium was buffered with 10 mM phosphate buffer ( pH 7 . 2 ) , which corresponds to the previously described nasal phosphate content [25] . The concentrations of essential cofactors such as vitamins and trace elements in nasal secretions were below detection limits . Since earlier studies had shown that S . aureus requires minimal amounts of these compounds [26] , highly diluted standard vitamin and trace element solutions were included in SNM . Moreover , because recent gene expression data have shown that S . aureus encounters iron-limited conditions in the human nose [17] , iron was omitted from the trace element solution , and SNM was supplemented with 200 µM of the iron-complexing agent 2 , 2′-bipyridin , which has been shown to confer iron limitation in S . aureus [27] . The concentrations of inorganic salts , 2 , 2′-bipyridine , trace elements , and cofactors in SNM are listed in Table 2 . The community-associated methicillin-resistant S . aureus ( CA-MRSA ) clinical isolate USA300 LAC and the laboratory strain Newman were used to evaluate if S . aureus is able to grow in SMN . Despite the very low amount of 238 mg amino acids per liter , both strains showed reproducible but moderate growth in SNM . This is in agreement with the rather low bacterial numbers found in swabs from the human nose [28] . In contrast , much higher bacterial densities have been found in microbiomes which are in contact with ingested food e . g . in the human mouth or gut [29] , [30] . When we increased the concentration of amino acids , organic acids and glucose in SNM , the maximal bacterial densities also increased until they reached a plateau at approximately 20-fold nutrient concentration for S . aureus USA300 ( Fig . 2A ) . Because epithelial secretions are continuously produced and removed , nutrient concentrations should remain more or less constant at the surface of nasal tissues , while they are continuously decreasing in a test tube culture . In agreement with this notion S . aureus Newman reached 2 . 4-fold higher bacterial numbers when grown in SNM in a continuous flow system compared to static SNM ( Supplementary Figure S1 ) . Because several of the subsequent experiments involved growth on SNM agar plates or in multiple parallel cultures , which could not be performed in a continuous flow system , we used SNM with threefold increased amino acid , organic acid and glucose concentration ( SNM3 ) in subsequent experiments . In order to validate the capacity of SNM3 to simulate living conditions for staphylococci in the human nose , 87 different staphylococcal strains from the anterior nares of 37 human volunteers were isolated , and growth of the corresponding 18 S . aureus , 57 S . epidermidis , and 12 other CoNS strains ( Staphylococcus capitis , Staphylococcus lugdunensis , Staphylococcus warneri , Staphylococcus hominis ) in SNM3 was compared . All S . aureus grew in SNM3 liquid cultures without long lag-phases and reached their highest densities after about 12 . 5 hours ( Fig . 2B ) . When serial dilutions of the same strains were spotted on SNM3 agar , which should correspond better to the sessile lifestyle on the nasal epithelial surface than liquid SNM3 , identical CFUs as on complex medium agar ( basic medium , BM ) were found for all S . aureus except for two strains ( Fig . 3A ) . This indicates that SNM3 offers efficient growth conditions for the vast majority of nasal S . aureus strains . In contrast , most of the S . epidermidis and other CoNS grew in liquid SNM3 only after long lag-phases and often with much longer generation times compared to S . aureus ( Fig . 2B ) . Moreover , only a very small percentage of cells ( one to ten out of a million viable cells ) of the S . epidermidis and other CoNS strains were able to form colonies on SNM3 agar ( Fig . 3A ) . This property appeared to be a stable trait because sub-cultivation of such outgrowing clones resulted in much higher numbers of colonies on SNM3 compared to the parental clones ( data not shown ) . Hence , most S . aureus appear to be metabolically well adapted to life in the human nose , whereas the vast majority of CoNS exhibited arrested growth with only a small minority of cells starting multiplication on SNM3 agar . These differences reflect recent findings that the nose of permanent S . aureus carriers usually contains substantially lower numbers of CoNS than S . aureus [7] , [31] . To investigate if S . aureus is simply better adapted to dilute nutrient concentrations than CoNS , a selection of strains , whose ability to form colonies on SNM3 was in the median range , was tested for colony formation on SNM agar with three , five , ten , and twenty-fold concentrated nutrients . As shown in Figure 3B , concentrating nutrients in SNM agar plates ten to twenty-fold improved the outgrowth of most of the tested S . epidermidis and some other CoNS strains , but only some strains reached similar growth capacities as S . aureus , while most formed colonies at several magnitudes lower numbers than S . aureus even at the highest nutrient concentrations . Thus , CoNS appear to be much less capable of adapting to diluted nutrient concentrations than S . aureus and exhibit enormous intra-species variation in their capacities to grow on SNM3 agar , even when nutrient concentrations were strongly increased . Two recent studies have described if and how efficiently critical S . aureus genes are transcribed during nasal colonization of humans or cotton rats [17] , [18] . In order to evaluate if growth in SNM3 leads to similar transcriptional profiles as found during in vivo colonization , we compared marker gene expression of S . aureus USA300 actively growing in SNM3 or BM by quantitative RT-PCR ( qRT-PCR ) ( Fig . 4 ) . Bacteria grown to stationary phase in BM ( BM-stat ) were also included because this growth condition has recently been used to assess intranasal expression of relevant S . aureus marker genes [17] , [18] . The global virulence regulator RNAIII , which responds to the concentration of a secreted agr autoinducer peptide ( quorum sensing ) , and the agr-controlled psmß have been shown to be only moderately expressed in the nose [17] , [18] , and artificial induction of the RNAIII transcript reduces the nasal colonization capacity of S . aureus in the cotton rat model [20] . These previous findings corresponded to the low expression of RNAIII and psmß in BM and SNM3 compared to expression in BM stat . Also , the keratin-binding adhesin gene clfB was expressed in SNM3 at a similar level as in growing BM cultures , corresponding to the expression profile found recently in human noses [17] . Moreover , expression of the iron-regulated isdA and the lytic transglycosylase gene sceD , found to be up-regulated in the nose compared to BM or BM-stat cultures [17] , [18] , was also enhanced in SNM3 . Taken together , these data indicate that the composition of SNM3 provides suitable conditions for in vitro simulation of S . aureus growth and gene expression in the nasal habitat . SNM3-grown S . aureus cultures enabled us to monitor global gene expression under conditions reflecting nasal colonization and to compare these with expression profiles from previous studies , which have usually used S . aureus grown in complex media . RNA from S . aureus USA300 , actively growing either in SNM3 or BM , was hybridized to Affymetrix microarrays and analyzed with respect to basic cellular and metabolic pathways ( data deposited under GEO Series accession number GSE43712 ) . Multivariate data analysis was used to show differences or similarities between the transcriptomic data . Principal component analysis ( PCA ) confirmed that the three biological replicates performed for each of the two conditions led to very reproducible results , with substantial differences in SNM3 or BM-derived transcription profiles and a PCA mapping value of 77 . 7% ( Fig . 5A ) . Expression of 521 signals corresponding to 341 genes differed more than two-fold upon growth in SNM3 vs . BM with p-values below 0 . 05 ( Fig . 5B ) . Thus , complex media represent a very artificial situation for S . aureus that differs profoundly from colonization-related conditions . A total of about 12 . 6% of the genes categorized in the “Clusters of Orthologous Groups of proteins” ( COG ) database [32] as being involved in “cellular processes and signalling” ( Fig . 5B ) showed more than two-fold expression differences between the two growth conditions , with 22 genes being up- and only six down-regulated . Most pronounced in this group was the two- to threefold higher expression of various capsule biosynthesis genes ( functional category M ) in SNM3 compared to BM ( Supplementary Fig . S2 ) . This finding is in agreement with the crucial role of the capsule in S . aureus nasal colonization recently shown in a rodent model [33] . The up-regulated signals also included the genes for the iron-regulated IsdC surface protein and the osmoprotectant system OpuCC and OpuD . In the group of genes categorized for “information storage and processing” 10 . 6% of the genes were differentially expressed , 13 genes were up- and 20 down-regulated including various transcriptional regulators . As expected , most of the major differences were found among genes governing the central “metabolism” ( 26 . 6% of all genes assigned to this group ) , in which 54% of all genes from the functional group E ( “amino acid transport and metabolism” ) differed more than twofold in expression between the chosen growth conditions . Here , 79 genes were up- and only three down-regulated in SNM3 compared to BM ( Supplementary Fig . S2 ) . Significant up-regulation of amino acid biosynthesis genes was observed for glutamate , histidine , lysine , valine , leucine , isoleucine and methionine in SNM3 compared to BM . Remarkably , all of these amino acids , except for methionine and isoleucine , are integral components of SNM3 . Whereas isoleucine can easily be generated from threonine , methionine has to be synthesized from aspartate , which was also not detectable in nasal secretions and therefore was not included in SNM3 . Via O-acetyl-L-homoserine , aspartate reacts in a transsulfuration reaction with cysteine to form L-cystathionine and L-homocysteine ( Fig . 6 ) . The genes for cystathionine-γ-synthase ( metI , SAUSA300_0360 ) and cystathionine-β-lyase ( metC , SAUSA300_0359 ) , whose gene products are responsible for these enzymatic reactions , were 26- to 32-fold up-regulated and exhibited the strongest up-regulation of all genes in SNM3 . Among the methionine-biosynthetic genes those for MetE ( 5-methyltetrahydropteroyltriglutamate-homocysteine S-methyl-transferase ) and its homologue MetF SAUSA300_0358 ( bifunctional homocysteine S-methyltransferase ) , responsible for the conversion of L-homocysteine to L-methionine , were 13 to 14-fold up-regulated . The strong up-regulation of two L-methionine ABC-transport systems ( SAUSA300_0435-0437 and SAUSA300_0796-0798 ) underscored the importance of methionine supply during growth in SNM3 . In addition to the biosynthetic operons of the above mentioned amino acids , many ABC-type dipeptide/oligopeptide ( opp genes ) as well as metal ion transporters ( especially for iron-siderophores ) , were strongly up-regulated in SNM3 ( Supplementary Fig . S2 , functional category P ) . The presence of iron-limiting conditions in SNM3 was reflected by distinct up-regulation of the operon for biosynthesis of staphyloferrin B ( also called staphylobactin , sbnABCDEFGHI; SAUSA300_0118 to SAUSA300_0126 ) , a potent siderophore facilitating the extraction of iron from human transferrin [34] . The ferric uptake regulator ( Fur ) controls the expression of iron-regulated genes via binding to a consensus sequence in the promoter regions in staphylococci [35] . Besides the sbn operon additional Fur-regulated genes and operons , as listed at the RegPrecise regulon site ( http://regprecise . lbl . gov/RegPrecise/regulon . jsp ? regulon_id=6608 ) , exhibited slightly to moderately altered expression in SNM3 compared to BM . These included the isd genes ( iron-regulated surface determinant system; also called sir; staphylococcal iron-regulated proteins ) and those for the ferritin storage protein , the TatAC system and the ferrichrome ABC-transporter SstABCD ( supplementary Figure S3 ) . To evaluate the validity of the microarray results , gene expression of selected S . aureus USA300 genes , which were strongly up-regulated in SNM3 , was reinvestigated by qRT-PCR . Besides metI ( methionine biosynthesis; SAUSA300_0360 ) and the methionine transporter gene metN ( SAUSA300_0435 ) , expression was analyzed for hisC ( histidine biosynthesis; SAUSA300_2610 ) , aspartate kinase ( SAUSA300_1225 ) , oligopeptide transporter oppB ( SAUSA300_0201 ) and staphyloferrin B biosynthesis gene sbnC ( SAUSA300_0120 ) . As shown in Figure 7 , all of the investigated genes exhibited significant up-regulation in SNM3 compared to BM , thereby confirming the microarray data . The strong expression of methionine-biosynthetic genes prompted us to investigate if the absence of methionine is significantly limiting the growth of S . aureus USA300 in SNM3 . However , the step-wise increase of methionine in SNM3 had no effect on colony formation on SNM3 agar ( Fig . 3C ) and only a marginal impact on S . aureus growth in liquid SNM3 ( data not shown ) . Hence , methionine limitation does not compromise S . aureus growth , probably as a result of efficient ways to synthesize methionine . We also investigated if methionine limitation might be a reason for the inefficient outgrowth of CoNS on SNM3 agar . For this purpose , we analyzed colony formation of various nasal CoNS isolates and the laboratory strain S . epidermidis 1457 on SNM3 agar with increasing methionine concentrations ( Fig . 3C ) . However , even high methionine amounts of 100 µM increased colony formation of S . epidermidis and CoNS strains only slightly , and only a small minority of strains benefitted strongly from the addition of methionine . This finding suggests that the inability of CoNS to thrive on SNM3 agar is probably the result of complex differences in the metabolic or regulatory properties of S . aureus and CoNS . In order to analyze the importance of the capacity of S . aureus to synthesize methionine , the gene of the methionine-biosynthetic enzyme MetI was inactivated in S . aureus Newman . The resulting mutants showed no growth defects in complex medium . However , they were completely unable to grow in SNM3 , while addition of methionine restored growth of the mutants , thereby demonstrating a crucial role of MetI under colonization-related conditions ( Supplementary Fig . S4 ) . Antimicrobial targets for new decolonization drugs are urgently needed because of increasingly emerging mupirocin resistant S . aureus [23] . The synthetic compound DL-propargylglycine has been shown to inhibit the bacterial cytathionine-γ-synthase MetI leading to a block of methionine biosynthesis [36] . We hypothesized that it may have antimicrobial activity against S . aureus under conditions where MetI plays a crucial role . DL-propargylglycine hardly affected growth of S . aureus USA300 in BM ( minimal inhibitory concentration , MIC>10 mg/ml ) , whereas it inhibited growth of all tested S . aureus strains in SNM3 ( Table 3 ) . These data suggest that MetI inhibitors might in fact be useful to limit the growth of S . aureus in vivo . Supplementation of SNM3 with methionine abrogated the antimicrobial activity of DL-propargylglycine ( MIC>10 mg/ml ) confirming that DL-propargylglycine inhibits S . aureus growth by blocking an indispensable step of methionine biosynthesis . In order to analyze if the critical genes found to be up-regulated by S . aureus USA300 in SNM3 vs . BM exhibit similar expression profiles during nasal colonization , their transcription was measured> by qRT-PCR in nasal swabs from six documented S . aureus carriers . Their corresponding nasal S . aureus strains were subsequently grown in SNM3 and BM for RNA isolation , and all samples were analyzed by qRT-PCR ( Fig . 8 ) . For some samples expression of certain genes was not detectable , possibly as a result of sequence variation at primer binding sites or low RNA concentrations . The expression patterns in SNM3 and in vivo were overall very similar , and none of the analyzed genes exhibited significant differences in vivo and in SNM3 compared to BM . Gene expression in the six nasal isolates was generally more variable in vivo than in SNM3 , suggesting that some parameters of the nasal living conditions may vary to a certain extent between donors . In most of the samples isdA , sceD , and oppB expression analysis confirmed up-regulation in vivo and in SNM3 compared to BM . While all strains that yielded detectable qRT-PCR signals for aspartate kinase and hisC were consistently up-regulated in SNM3 compared to BM , this was only found in some of the in vivo samples . This variability indicates that in vivo expression of these genes depends on the individual host . Expression of sbnC was high in SNM3 and in vivo compared to complex medium , confirming that iron-limiting conditions were indeed present under both conditions . Notably , expression of metI was strongly up-regulated in SNM3 compared to BM in all six strains ( median about 100-fold , Fig . 8 ) , and the in vivo metI expression was nearly the same as in SNM3 . Cotton rats have been shown to be a suitable model for S . aureus nasal colonization [11] , [37] . When S . aureus Newman and the isogenic ΔmetI mutant were used to inoculate the noses of cotton rats , a strongly reduced colonization capacity of the mutant was observed compared to the parental strain ( Figure 9 ) . Thus , MetI has a critical role during nasal colonization , and the methionine-biosynthetic pathway may include previously unrecognized targets for new antimicrobial strategies . The metabolomics analysis of nasal secretions reveals that the human nose represents an environment with rather limited nutrient availability . The concentrations of glucose and amino acids are substantially lower in nasal secretions compared to human plasma of healthy individuals ( glucose about 0 . 04–1 mM vs . 4–8 mM; amino acids about 0 . 65–2 . 2 mM vs . 2 . 6 to 4 . 3 mM , respectively ) [38] . The sputum covering lung epithelia of cystic fibrosis ( CF ) patients , which are frequently infected by S . aureus [39] , [40] , contains similar concentrations of glucose and even higher concentrations of free amino acids than plasma ( 1 . 3 to 4 . 5 mM and 4 . 7 to 24 . 7 mM , respectively [41] . These differences in nutrient availability suggest that S . aureus requires different metabolic activities during colonization of the human nose or infection of sterile tissues . It is interesting to note that lactate , an abundant compound on skin with concentrations around 2 . 5 mM [42] and a product of S . aureus energy metabolism [43] , was undetectable in nasal secretions . Accordingly , metabolites in the nasal habitat differ from those on skin , and S . aureus metabolism does not seem to affect much the nasal metabolome . Our data indicate that the concentration of many nutrients in nasal secretions varies between donors , but none of the differences could be associated with the S . aureus carrier status . Thus , factors other than nutrient supply , such as differences in epithelial immunity or ligands for S . aureus adhesins , may be responsible for the predisposition to the S . aureus carriage status . Various chemically defined media have been developed over the past decades with the purpose to achieve maximal growth and high capsule or protein expression in S . aureus [44]–[47] . In contrast , our aim was to simulate the in vivo situation in the human nose , to use a representative synthetic nasal medium for comparing colonization capacities of different staphylococcal strains and to elucidate colonization-related bacterial metabolic processes . Expression profiles of representative S . aureus genes upon growth in the artificial nasal medium SNM3 corresponded well to those found in the human nose indicating that SNM3 provides suitable conditions for simulating S . aureus growth during nasal colonization . Of note , global transcriptomes of bacteria from SNM3 and complex media differed extensively indicating that complex media can hardly reflect in vivo-related gene expression . In accord with this finding , transcriptome data of S . aureus grown in human blood and serum have also yielded major differences compared to previously described expression profiles from complex-media grown bacteria [48] , [49] . Thus , SNM3 will allow to investigate bacterial metabolic processes during nasal colonization in detail and to monitor S . aureus competition with other resident bacteria under in vivo-like conditions . Nevertheless , results obtained with SNM3 in genome-wide in vitro experiments should be validated in in vivo models . In accordance with the limited nasal nutrient supply , the human nasal microbiome has been shown to be much less complex [31] , [50] than those of the upper or lower digestive tract , where bacteria are in regular contact with ingested food [30] , [51] . Hence , bacteria in the human nose can be expected to compete fiercely for available nutrients and depend on mechanisms allowing them to thrive even with very low amounts of nutrients . In line with this notion all tested nasal S . aureus isolates grew well in SNM3 , and almost all viable cells formed colonies on SNM3 agar . S . aureus is obviously adapted to growth in moist environments with very dilute nutrients , while human skin is usually dry except for atopic dermatitis patients , whose skin structure and permeability is severely perturbed [52] . In contrast , CoNS colonize healthy skin as their major habitat [53] , which is in accordance with our finding that most of the tested CoNS isolates had major problems to form colonies on SNM3 agar and grew in liquid SNM3 only after long lag phases . It has remained unclear if CoNS use the human nose as a preferred or only as a transient habitat . While permanent S . aureus carriers are usually colonized by one specific single clone , multiple S . epidermidis strains can be found per nose and the pattern of strains is highly variable over time [54] . Our results indicate that CoNS are usually much less adapted to growth in nasal secretions than S . aureus . In line with our data a recent study has found a negative association between the colonisation with S . aureus and the abundance of S . epidermidis . It has been speculated that these bacterial species compete with each other during colonization of the nares [31] . Our data supports the notion that most S . aureus strains can overgrow CoNS because of better metabolic adaptation . In addition to differences in the abilities to utilize dilute nutrients , competition between S . aureus and CoNS may also involve different capacities to produce bacteriocins such as lantibiotics [55] , [56] , to induce and resist host antimicrobial peptides such as defensins [57] , which have been detected in nasal secretions [58] , and to produce factors that compromise the ability of other staphylococci to adhere to epithelial surfaces such as the S . epidermidis Esp protease [59] . Metabolomics analyses have been proposed to facilitate the identification of new antimicrobial targets and have recently helped to define the staphylococcal pyruvate dehydrogenase as a target for a new class of organobismuth antimicrobials [60] . We demonstrate here that a combined metabolomics and transcriptomics approach can lead to the identification of targets that are specifically important during in vivo-like conditions . Gene expression analysis of S . aureus grown in SNM3 , or from in vivo samples , revealed that various amino acid biosynthesis operons are up-regulated during colonization . This implies a general importance of several anabolic pathways under such conditions . Since SNM3 does not contain isoleucine , it can be assumed that the global transcription repressor CodY plays an important role for the up-regulation of a number of genes under the conditions used for microarray experiments , especially those for isoleucine biosynthesis [61] , [62] . It has recently been shown that CodY also influences S . aureus metICFE-mdh expression , which is essentially regulated by a T-box riboswitch that recognizes uncharged initiator tRNAfMet [63] . Despite the obvious influence of CodY on the gene expression pattern no clear signs of stringent response , like down-regulation of ribosomal protein rpsL , could be detected [64] . The absence of methionine in nasal secretions and SNM3 was reflected by the strong up-regulation of S . aureus methionine import and , most conspicuously , methionine biosynthesis genes in SNM3 and in the noses of human volunteers . In a similar approach , metabolomics analysis of sputum from CF patients has recently allowed to develop a synthetic sputum medium , leading to the unexpected finding that Pseudomonas aeruginosa depends on L-alanine as carbon source during CF lung infections . Furthermore , the high concentrations of aromatic amino acids in CF-sputum and the corresponding synthetic medium have been implicated in high-level production of pyocyanin [41] , [65] . This cytotoxic respiratory inhibitor is important in competition of P . aeruginosa with S . aureus in the CF lung , since the latter is strongly inhibited because of its pyocyanin-sensitive cytochrome bd oxidase [66] . The methionine biosynthetic enzymes fulfil important criteria for antimicrobial drug targets , because they are absent from human cells that need to take up methionine from exogenous sources . However , such enzymes have hardly been regarded as antimicrobial targets before , because their importance has probably been missed when growing bacteria in complex media in antimicrobial screening programs . Nevertheless , this pathway has recently been proposed as a potential staphylococcal “Achilles heel” [63] . In our study the synthetic compound DL-propargylglycine , described as an inhibitor of cystathionine-γ-synthase ( MetI ) , which is unique to microorganisms and plants [36] , [67] was used . The activity of DL-propargylglycine against S . aureus and the inability of metI mutants to grow in SNM3 underscore the potential use of MetI or other methionine-biosynthetic enzymes as targets for new nasal decolonisation drugs . These drugs could be alternatives to eradicate e . g . mupirocin-resistant S . aureus . In agreement with this notion , the S . aureus metI gene was strongly upregulated in human noses , and the metI mutant was compromised in nasal colonization of cotton rats . However , DL-propargylglycin itself does not seem to be suitable as a drug , because it also inhibits the human cystathionine-γ-lyase . This enzyme converts exogenous methionine to cysteine via the transsulfuration pathway [68] , [69] , leading to reduced production of the gaseous messenger molecule hydrogen sulfide with various eminent consequences [70] . While the rather low activity against S . aureus and the severe impact on mammalian metabolism precluded the use of DL-propargylglycine in animal models , derivatives or new compounds with higher selectivity and activity against bacterial MetI or other methionine-biosynthetic enzymes may become promising lead substances for the development of new antimicrobial drugs . Notably , all these enzymes were significantly up-regulated in SNM3 ( Fig . 6 ) . The nasal secretion study and sample collection procedures were approved by the clinical ethics committee of the University of Tuebingen ( No . 109/2009 BO2 ) and informed written consent was obtained from all volunteers . Secretion samples and nasal swabs were taken exclusively from healthy adults . The staphylococcal laboratory strains used in this study are S . aureus USA300 LAC [71] , S . aureus Newman [72] , and S . epidermidis 1457 [73] . Beside these characterised strains a set of 87 staphylococcal isolates from nasal swabs from 37 healthy volunteers were used . The collection of nasal isolates includes 18 S . aureus , 57 S . epidermidis , six S . capitis , three S . lugdunensis , two S . warneri and one S . hominis strain . Identification was accomplished according to an established scheme [74] by sequencing of a variable ca . 931-bp PCR fragment of the glyceraldehyde-3-phosphate dehydrogenase gene ( gap ) , amplified with primer pair gap-F and gap-R ( Table S1 ) . Ambiguities were clarified by additional sequencing of a variable part of the dnaJ gene , amplified with primer pair dnaJ-F and dnaJ-R ( Table S1 ) . BM ( 1% tryptone , 0 . 5% yeast extract , 0 . 5% NaCl , 0 . 1% glucose and 0 . 1% K2HPO4 , pH 7 . 2 ) was used as standard complex medium . The composition of the chemically defined medium SNM , corresponding to nasal secretions , is listed in Tables 1 and 2 . The content of inorganic ions in human nasal secretions listed in Table 1 has been published earlier [24] , [25] and the described buffer , salt and co-factor concentrations were included in SNM as listed in Table 2 . In contrast to the published data calcium was omitted from the medium , because it led to precipitates . Trace elements and cofactors were added from 1000-fold concentrated stock solutions . The iron-complexing agent 2 , 2′-bipyridine ( Merck , Darmstadt , Germany ) was added at a final concentration of 200 µM . SNM3 contained the same concentrations of inorganic salts , urea , trace elements , and cofactors as SNM while amino acids , organic acids , and glucose as listed in Table 1 were threefold concentrated . BM and SNM agar plates contained 1 . 5% agar . For growth curves bacteria from overnight cultures grown in BM were centrifuged , washed with PBS , and diluted in SNM3 to an initial OD600 nm of 0 . 02 in microtiter plates ( MTP ) and grown in a TECAN Infinite 200 PRO reader ( Tecan Group Ltd . , Switzerland ) with shaking ( 180 rpm ) at 37°C with continuous measurement of optical densities . For MIC determination 24-well MTP plates , essentially inoculated as described above , were grown for 48 hours at 37°C and 160 rpm . For the calculation of growth , inoculation density was subtracted from final optical density and the MICs , defined as the concentration of DL-propargylglycine ( Sigma-Aldrich , Taufkirchen , Germany ) at which 75% growth inhibition occurred , were calculated . For monitoring bacterial growth in three to 20-fold SNM cultures 12-ml medium in 100-ml buffled Erlenmeyer flasks were inoculated with an SNM3 over-night culture to an initial OD578 nm of 0 . 02 and incubated with vigorous shaking at 160 rpm at 37°C , until the final OD578 nm was determined after 90 h growth . For RNA isolation all media were inoculated with SNM3 overnight cultures , grown at 37°C with vigorous shaking at 160 rpm in 250-ml buffled Erlenmeyer flasks . For continuous cultures sterile fresh medium was added from a reservoir to the growing culture with a peristaltic pump . A second pump was used to remove consumed medium , including bacteria , from the culture . 100 ml SNM in 500-ml glass bottles ( Schott ) were inoculated to an OD578 nnm of 0 . 02 and incubated with shaking at 140 rpm at 37°C with a sterile filter in the bottle lid to allow aeration . About 4 ml fresh medium were added and simultaneously removed from the culture per hour resulting in exchange of the complete culture volume within approximately 24 hours . Non-continuous batch cultures were performed in the same way , except that continuous medium exchange was omitted . Nasal secretions were taken with the help of slight vacuum suction with suction catheters ( model 14 Ch , Bicakcilar Healthcare Products , Turkey ) mounted on sputum collection traps ( P . J . Dahlhausen & Co . GmbH , Germany ) . After short centrifugation the samples were immediately frozen at −80°C . Metabolites from frozen nasal secretions were extracted with methanol/chloroform/water 4/4/2 . 85 after addition of internal standards ( ribitol and norvaline ) , samples were vortexed twice for 10 s and then centrifuged ( 4°C , 10 min , 13000 rpm ) . Supernatants were transferred to new glass vials and dried by lyophilisation . Dry samples were derivatized for GC-MS analyses and measured according to a previously described method [75] . Briefly , metabolites were identified by matching retention time and identification ion of pure chemical standards , measured under the same conditions . Ratios of peak areas to the respective internal standard ( ribitol ) were used for absolute and relative quantification . Calibration curves of pure substances were measured over a wide range of concentrations under the same conditions and were used for the calculation of the total , micromolar metabolite concentration . Because arginine is unstable when derivatized for GC-MS , its content was determined by HPLC using ortho-phthaldehyde ( OPA ) pre-column derivatization . OPA was diluted to a final concentration of 1 mg/ml with 1 M sodium-borate-buffer , pH 9 . 0 . Nasal secretion samples were sonicated for 20 s in a sonication water bath to reduce viscosity and diluted 1∶1 with distilled water . Subsequently , each sample ( 6 µl ) was mixed with 1 . 5 µl OPA for 90 s and immediately injected and separated on an Agilent 1200 series HPLC-system using a Grom-SIL OPA-3 ( 5 µm ) , 4 . 0×150 mm column ( Grace Davison , Lokeren , Belgium ) . A linear gradient from 100% buffer A ( 25 mM sodium-phosphate buffer , 0 . 7% tetrahydrofuran , pH 7 . 2 ) to 100% buffer B ( 50% buffer A , 35% methanol , 15% acetonitrile ) in 24 min was run at a flow rate of 1 . 1 ml/min . Arginine was detected at 450 nm and quantified against a standard calibration curve with arginine concentrations of 10 µM , 100 µM , 1 mM , and 10 mM . Data were analyzed by Agilent ChemStation software . Relative abundance is given in cases were no dilution series of pure standard compounds was measured . Data analysis was performed within the GC-MS software Chemstation ( Vers . E . 02 . 00 Service Pack 2 , Agilent ) . Statistical analysis was accomplished with Aabel 3 . 0 . 4 ( Gigawiz ) and SIMCA P+ 12 . 0 . 1 , for principal component analysis ( PCA ) , and for partial least square ( PLS ) analysis log-transformed peak areas with UV scaling were used . For qRT-PCR 40-ml cultures were inoculated to an optical density of 0 . 02 at 578 nm ( ca . 2×107 CFU/ml ) and grown for three hours . RNA was isolated by a modified protocol of Bhagwat et al . adapted to large culture volumes [76] . Briefly , cells were immediately killed and RNA was stabilized by the addition of 1/9 vol . of an ice-cold 9∶1 ethanol∶phenol solution ( equilibrated with Tris/EDTA-buffer ( TE ) ; Applichem , Darmstadt , Germany ) . After 5 min on ice cells were harvested ( 20 min , 4500×g , 4°C ) , resuspended in 1 ml TRIZOL solution ( Invitrogen - Life Technologies Corporation , Darmstadt , Germany ) , and lysed with 0 . 5 ml zirconia-silica beads ( Karl Roth GmbH , Karlsruhe , Germany; 0 . 1 mm-diameter ) in a high-speed benchtop homogenizer ( FastPrep-24 , MP Biomedicals , Germany ) . Subsequently , RNA was isolated as described in the instructions provided by the manufacturer of the RNA isolation kit ( ExpressArt RNAready , AmpTec GmbH , Germany ) . In order to get rid of potential RT-PCR inhibitors , a first washing step with 0 . 5 ml ‘Inhibitor removal buffer’ was applied ( High Pure PCR Template Preparation Kit , Roche Applied Science ) . DNA was removed by on-column DNAse treatment according to the ExpressArt RNAready protocol . RNA for microarrays was isolated from 100-ml cultures , inoculated to an OD578 nm of 0 . 005 and grown until OD578 nm of 0 . 02 . RNA was stabilized as described above by ethanol∶phenol addition . After centrifugation , the cell pellet was washed with 2 ml ice-cold ethanol∶acetone ( 1∶1 ) to remove phenol traces . A second washing and stabilisation step was applied by resuspending the cells in 2 ml RNAprotect bacteria reagent ( QIAGEN GmbH , Germany ) ∶TE-buffer ( 2∶1 ) . After centrifugation cells were lysed in 100 µl TE buffer containing 10 µl lysostaphin ( 2 mg/ml , Genmedics GmbH , Germany ) by incubation for 3 min at room temperature . After the addition of 350 µl RLT buffer ( RNeasy Kit , QIAGEN GmbH , Germany ) cells were lysed with 0 . 2 ml zirconia-silica beads as described above and RNA was purified with the RNeasy Kit according to the manufacturer's instructions . RNA for qRT-PCR from nasal swabs was isolated essentially as described above for in vitro cultures . The cotton swabs ( MSP Schmeiser , Horb , Germany ) were soaked in sterile PBS and used to carefully wipe the anterior nares of volunteers and afterwards directly resuspended in 1 ml TRIZOL . For confirming the carrier status additional swabs were taken , resuspended in PBS , and serial dilutions were plated on BM agar . The presence of S . aureus was confirmed with the Slidex Staph Plus latex agglutination test ( bioMérieux Deutschland GmbH , Nürtingen , Germany ) according to the manufacturer's instructions . Since some of the volunteers had quite low numbers of S . aureus in their nose ( <1 . 000 S . aureus/swab ) , the isolated RNA amounts were sometimes not sufficient for qRT-PCR . In such cases RNA was amplified once with the ‘Bacterial Nano mRNA amplification Kit’ ( AmpTec GmbH , Hamburg , Germany ) as described by the manufacturer . Relative quantification of various transcripts was performed as described previously [18] . Briefly , isolated RNA from cultures and nasal swabs was transcribed into complementary DNA using SuperScriptIII Reverse Transcriptase ( Invitrogen ) and 200 ng of random hexamer primers ( Fermentas , St . Leon-Rot , Germany ) . For relative quantification standards were generated by PCR with the primers listed in Table S1 with S . aureus USA300 genomic DNA as template . All agarose gel-purified PCR products were used in 10-fold serial dilutions . For the genes metN and sbnC PauI ( Fermentas ) -digested and purified chromosomal DNA of S . aureus USA300 was used as standard . Complementary DNA was diluted 1∶10 and quantitative real-time PCR ( qRT-PCR ) was performed with primers listed in Supplementary Table S1 , using the LightCycler instrument ( LightCycler 480 , Roche ) in combination with SYBR Green I ( QuantiFast SYBR Green PCR Kit , QIAGEN ) . Master mixes were prepared according to the manufacturer's instructions . Relative gene expression level was calculated by the method of Pfaffl with PCR efficiency correction [77] . 5 µg of RNA from three biological replicates per condition were applied to GeneChip microarrays ( Affymetrix ) and processed according to the manufacturer's protocol . The biological replicates yielded highly reproducible expression profiles . The GeneChip S . aureus genome array was provided by MFTServices ( www . mftservices . de ) , a Core Lab provider authorized by Affymetrix Inc . ( Santa Clara , CA ) . GeneChip hybridization , washing , staining , and scanning were performed as described by the manufacturer . The images were processed with Expression Console ( Affymetrix ) . The raw data from the array scans were normalized by median-centering genes for each array , followed by log transformation . Expressed genes were identified using Affymetrix GeneChip Operating Software ( GCOS , Ver . 1 . 1 ) . To identify genes that are differentially expressed in treated samples compared to controls , the Partek software version 6 . 6 was used . To select the differentially expressed genes , we used threshold values of ≥2 . 0- and ≤−2 . 0-fold change between the conditions . The false discovery rate ( FDR ) significance level with Benjamini-Hochberg was <5% . The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus [78] and are accessible through GEO Series accession number GSE43712 ( http://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? acc=GSE43712 ) . For the construction of a markerless mutant of S . aureus Newman , the flanking regions of metI were amplified with primer pairs cgsF1 up/cgsF1 down or cgsF2 up/cgsF2 down . After digestion of PCR product F1 with EcoRI/BglII and of PCR product F2 with BglII/NheI the two fragments were ligated into the previously described vector pBASE6 [64] , digested with EcoRI and NheI and used to transform E . coli DC10B [79] . The resulting plasmid pBASE6-ΔmetI was isolated and directly transferred to S . aureus Newman , where the homologous recombination process resulted in metI mutants , which were confirmed by PCR analysis . For the colonisation of cotton rats spontaneous streptomycin-resistant mutants of S . aureus Newman wild type and ΔmetI were selected on BM agar plates with 500 µg/ml streptomycin . The cotton rat model was used as described earlier [11] . Cotton rats were anesthetized and instilled intranasally with 10 µl of 1×108 colony-forming units ( CFU ) of S . aureus . Six days after bacterial instillation the animals were euthanized and noses were removed surgically . The noses were vortexed in 1 ml of PBS containing 0 . 5% Tween for 30 s . Samples were plated on appropriate agar plates ( B-medium , sheep blood containing 250 µg/ml streptomycin and HiCrome Aureus Agar ( Fluka ) ) and the bacterial CFU was determined . All animals received drinking water with 2 . 5 mg/ml streptomycin continuously , starting three days prior to the experiment to reduce the natural nasal flora . All animal experiments were conducted in accordance with German laws after approval ( protocol T1/10 ) by the local authorities ( Regierungspraesidium Tuebingen ) .
Many severe bacterial infections are caused by endogenous pathogens colonizing human body surfaces . Eradication of notorious pathogens such as Staphylococcus aureus from risk patients has become an important preventive strategy . However , efficient decolonization agents are rare , and the living conditions of colonizing pathogens have hardly been studied . Using a combined metabolomics and transcriptomics approach , we explored the metabolism of S . aureus during colonization of its preferred niche , the human nose . Based on nasal metabolite profiles , a synthetic nasal medium ( SNM3 ) was composed , enabling steady growth of S . aureus but not of staphylococcal species preferentially colonizing the human skin . Marker gene expression was similar in SNM3 and the human nose , and genome-wide expression analysis revealed that amino acid biosynthesis , in particular that of methionine , is critical for S . aureus during colonization . An inhibitor of methionine biosynthesis had anti-staphylococcal activity in SNM3 but not in complex media , and transcription of the S . aureus target enzyme was strongly up-regulated in human noses . Furthermore , mutants defective in methionine biosynthesis exhibited strongly compromised nasal colonisation capacities in a cotton rat model . Altogether , our results indicate that the elucidation of in vivo metabolism of pathogens may lead to the identification of new antimicrobial targets and compounds .
You are an expert at summarizing long articles. Proceed to summarize the following text: How learned experiences persist as memory for a long time is an important question . In Drosophila the persistence of memory is dependent upon amyloid-like oligomers of the Orb2 protein . However , it is not clear how the conversion of Orb2 to the amyloid-like oligomeric state is regulated . The Orb2 has two protein isoforms , and the rare Orb2A isoform is critical for oligomerization of the ubiquitous Orb2B isoform . Here , we report the discovery of a protein network comprised of protein phosphatase 2A ( PP2A ) , Transducer of Erb-B2 ( Tob ) , and Lim Kinase ( LimK ) that controls the abundance of Orb2A . PP2A maintains Orb2A in an unphosphorylated and unstable state , whereas Tob-LimK phosphorylates and stabilizes Orb2A . Mutation of LimK abolishes activity-dependent Orb2 oligomerization in the adult brain . Moreover , Tob-Orb2 association is modulated by neuronal activity and Tob activity in the mushroom body is required for stable memory formation . These observations suggest that the interplay between PP2A and Tob-LimK activity may dynamically regulate Orb2 amyloid-like oligomer formation and the stabilization of memories . Synthesis of new protein is important for the formation of stable memory [1] . The Cytoplasmic Polyadenylation Element Binding ( CPEB ) proteins are a family of RNA binding proteins that regulate the translation and subcellular distribution of a specific set of cellular mRNAs in various cell types including neurons [2] . Previous studies found that some CPEB family members play a causal role in long-term change of synaptic activity and in stabilization of memory [3]–[9] . For example , in marine snail Aplysia , in the absence of a neuron-specific ApCPEB , serotonin mediated enhancement of synaptic transmission fails to persist beyond 24 h [7] , [10] . Likewise , the Drosophila CPEB , Orb2 , is required specifically for long-term memory but not for learning or short-term memory [3]–[5] . In humans , a particular CPEB family member , CPEB3 , has been linked to episodic memory formation , suggesting a conserved role of CPEB in synaptic plasticity and memory [11] . Interestingly , ApCPEB and Orb2 form self-sustaining amyloidogenic oligomers ( prion-like ) in response to the neurotransmitters serotonin in Aplysia and octopamine or tyramine in Drosophila [5] , [6] , [12] , [13] . More importantly , the oligomeric CPEB is required for the persistence of synaptic facilitation in Aplysia [6] and for the stabilization of memory in Drosophila [5] . These observations led us to propose that the persistent form of memory recruits an amyloidogenic oligomeric form of neuronal CPEB to the activated synapse , which in turn maintains memory through the sustained , regulated synthesis of a specific set of synaptic proteins [5] . However , considering the dominant and stable nature of amyloids , a central question is how the conversion of neuronal CPEB to the amyloidogenic state is regulated to confer activity dependence and restrict it to the relevant neuron/synapse . The Drosophila Orb2 gene has two protein isoforms , Orb2A and Orb2B , and the oligomers are composed of both Orb2A and Orb2B . In the adult brain , in comparison to the Orb2B protein , the Orb2A protein is expressed at an extremely low level [4] , [5] . In spite of its low abundance , the Orb2A protein is critical for Orb2 oligomerization , and Orb2A forms oligomers more readily than Orb2B . More importantly , a mutation that impedes Orb2A oligomerization selectively affects persistence of memory [5] , and the Orb2A prion-like domain is sufficient for long-term memory formation [4] . These observations suggested a model in which the rare Orb2A protein either acts directly as a seed to induce activity-dependent amyloid-like oligomerization of the constitutive Orb2B protein or Orb2A oligomerization indirectly affects oligomerization of Orb2B [5] . In either case the amount and localization of Orb2A protein would therefore be a key determinant of when and where amyloid-like conversion would occur . Here we find that Orb2A has a very short half-life and the Orb2 interacting protein Transducer of Erb2 ( Tob ) , a known regulator of cellular growth , stabilizes Orb2A and induces Orb2 oligomerization . Expression of dsRNA against Tob in the mushroom body neurons does not affect learning , but impairs long-term memory formation . Tob recruits the neuronal protein kinase Lim Kinase ( LimK ) to the Tob-Orb2 complex to induce Orb2 phosphorylation . Phosphorylation regulates Tob-Orb2 association as well as the stability of both proteins , and Protein Phosphatase 2A ( PP2A ) is a key regulator of the phosphorylation status of Tob and Orb2 . Intriguingly , inhibition of PP2A stabilizes Orb2A , but destabilizes Orb2-associated Tob , providing a mechanism for temporal restriction on Orb2A stabilization . Since PP2A and LimK activity can be regulated in a synapse-specific manner [14] , [15] , the phosphorylation-dephosphorylation of Orb2 and Tob provides a putative mechanism of restricting the Orb2 oligomerization to the activated synapse . Tob is also known to regulate the function of CPEB family members [16] . Therefore , the Tob-Orb2 association-dissociation may also regulate Orb2 function in the nervous system . A regulator of Orb2 oligomerization could potentially fall into at least two distinct categories: an activator that associates with Orb2 and facilitates conversion to the oligomeric state or a repressor that binds to Orb2 and prevents its oligomerization . To identify both types of regulators we used a proteomics approach to perform a comprehensive search for Orb2 interacting proteins in the adult Drosophila brain . The Orb2 proteins were expressed pan-neuronally as C-terminal HA-tagged proteins ( ElavGal4: UAS-Orb2AHA or Orb2BHA ) , and the Orb2 complex was immunopurified using anti-HA antibodies from RNaseA-treated adult head extract ( Figure 1A and B ) . Previously we observed that the C-terminal tags are inaccessible in the Orb2 oligomeric state [5]; thus , the anti-HA antibody preferentially immunopurified the Orb2 monomers . Therefore , to identify proteins that interact with oligomeric Orb2 , we also immunopurified Orb2AHA with an anti-Orb2 antibody ( Figure 1B and C ) . The factors associated with Orb2 were identified using Multidimensional Protein Identification technology ( MudPIT ) ( Table S1 ) [17] . We found 61 proteins that were significantly enriched ( p<0 . 05 ) in the Orb2 immunoprecipitates compared to eight independent control immunoprecipitates ( Figures 1D and S1A and Table S1 ) . Eleven proteins were overrepresented in Orb2 IP compared to the controls , albeit not to statistical significance ( Table S1 ) . To determine the validity of the proteomic approach , we randomly sampled 20 candidate proteins ( out of 72 proteins ) by pair-wise interaction in S2 cells ( Figure 1E and Figure S1B ) . Approximately 50% ( 11 out of 20 proteins ) of the proteins thus tested formed a complex with at least one of the Orb2 proteins in an RNA-independent manner ( Figure 1E and Figure S1B ) . Therefore , the proteomics approach indeed identified specific components of an Orb2 protein complex in the adult Drosophila brain . The candidate proteins either interact directly with Orb2 or indirectly as part of a larger Orb2 protein complex . A gene ontology ( GO ) analysis revealed that the Orb2 proteome is significantly enriched for proteins involved in translation initiation , mRNA binding , and synaptic activity ( Figure 1F ) . The enrichment of these protein complexes supports the idea that Orb2 is involved in regulation of synaptic protein synthesis . The Orb2A protein is undetectable by Western analysis , and a genomic construct expressing Orb2A-EGFP suggests it is ∼100 times less abundant than Orb2B protein in the adult brain [5] . Moreover , monomeric Orb2A has a very short half-life compared to Orb2B ( Figure 2A and Table S2 ) . Taken together , these observations suggest that availability of the Orb2A protein could be an important determinant of efficient Orb2A oligomerization and/or function . In the course of our interaction studies in S2 cells , we noticed one of the candidate proteins , Tob , may influence the Orb2A protein level ( Figure 1E ) . To determine Orb2A and Orb2B stability independent of each other , we used Drosophila S2 cells , in which Orb2 is normally not expressed and Tob is expressed at low levels . S2 cells were transfected with only HA-tagged Orb2 or coexpressed with Flag-tagged Tob . To determine half-life , total Orb2 or Tob protein levels were measured at several time points following treatment with cycloheximide ( CHX ) , which blocks new protein synthesis . The coexpression of Tob nearly doubled the half-life of monomeric Orb2A ( Figure 2A ) . However , Tob had no significant effect on Orb2B ( Figure 2B ) , indicating that association with Tob does not automatically enhance half-life . Likewise , incubation with dsRNA against Tob reduced the level of Orb2A protein but not Orb2B ( Figure S2A ) . Earlier studies with Tob family members have suggested that the stability of Tob itself can be regulated [18] , [19] . We found a fourfold increase in Tob half-life in the presence of either Orb2A or Orb2B compared to Tob alone ( Figure 2C and Table S2 ) . These results suggest that not only does Tob stabilize Orb2A , but Orb2 proteins have stabilizing effects on Tob . The recombinant Drosophila Tob interacts with in vitro transcribed and translated Orb2 proteins , suggesting direct interaction between these proteins ( Figure S2B ) . In mammals , the Tob family consists of six members , with Drosophila Tob most closely related to the mammalian Tob1 and Tob2 proteins [20] . We found both Aplysia CPEB and mouse CPEB3 interact with the closely related Tob1 and Tob2 , and Tob2 increases the steady-state level of ApCPEB and CPEB3 ( Figure S2C ) . Recently , others have reported a direct interaction between mouse CPEB3 and Tob1 [16] , suggesting that Tob is an evolutionarily conserved interactor of CPEB proteins . Tob is required for long-term potentiation of hippocampal CA1–CA3 synapses , a cellular correlate of long-term memory in mammals [21] , and Tob activity is modulated by bone morphogenetic proteins or BMPs [22]–[24] . These observations suggest Tob could function as an extracellular signal-dependent regulator of Orb2 in the nervous system . Does Tob influence Orb2 oligomerization in the adult fly brain ? To answer this , we increased Tob level in the fly brain using the Gal4-UAS system and assessed Orb2 oligomerization by immunopurification . Overexpression of Tob-TdTomato ( Elav-Gal4: UAS-TobTdTom ) , but not the fluorophore alone ( Elav-Gal4: UAS-TdTom ) , increased the levels of 10% SDS and boiling-resistant oligomeric Orb2 in the fly brain ( Figure 2D ) . The amount of Orb2 oligomers in Tob-expressing flies increased nearly 2-fold compared to control flies ( fold increase in oligomers normalized to monomer ± SEM , 1 . 95±0 . 27 , p<0 . 05 , t test ) . Tob has been implicated in a number of cellular processes , including transcriptional regulation and RNA metabolism [23] , [25]–[28] , raising the possibility that the increase in Orb2 oligomerization is a secondary effect of Tob overexpression . We generated a series of deletion mutants of Tob and found that deletion of a conserved 28 amino acid motif , TobΔ28 ( Figure S3A ) , decreased the interaction between Tob and Orb2 in both S2 cells ( Figure S3B ) and the adult fly brain ( Figure S3C ) . However , it had no effect on the association between Tob and the deadenylase Pop2 ( Figure S3D ) or with the transcriptional repressor , Mad ( Figure S3E ) . Overexpression of TobΔ28 ( Elav-Gal4: UAS-TobΔ28TdTom ) in the adult brain did not enhance Orb2 oligomerization ( fold increase in oligomers normalized to monomer ± SEM , 0 . 5±0 . 14 ) ( Figure 2D ) . Does Tob enhance oligomerization of Orb2A , Orb2B , or both ? EGFP-tagged Orb2A and Orb2B formed stable oligomers in the adult fly brain and the oligomers associated with Tob ( Figure S3F ) . To determine the effect of Tob on Orb2A and Orb2B , we coexpressed TdTomato-tagged Tob with EGFP-tagged Orb2A or Orb2B in the adult fly brain . To distinguish from the endogenous Orb2 , we quantified changes in the number of fluorescent puncta , since the abundance of fluorescence puncta co-relate with extent of oligomerization ( Figure 2E ) [5] . The number of Orb2A-EGFP puncta increased ∼2-fold in the presence of Tob ( number of puncta/100 µm2 ± SEM , Orb2A: 4 . 41±2 . 88 , N = 12; Orb2A+Tob: 8 . 06±3 . 69 , N = 15 , t test , p = 0 . 012 ) but not in the presence of TobΔ28 ( 3 . 58±1 . 62 , N = 11 , t test , p>0 . 5 ) ( Figure 2F ) . Unlike Orb2A , Orb2B∶EGFP by itself remained mostly diffused and Tob overexpression had no significant effect on the rare Orb2B puncta ( number of puncta/100 µ∶m2 ± SEM: Orb2B , 2 . 40±2 . 11 , N = 12 , Orb2B+Tob , 1 . 07±1 . 30 , N = 9 , t test , p = 0 . 155 ) ( Figure 2F and Figure S3G ) . In addition to being more numerous , the size of Orb2A puncta also increased significantly when Tob was overexpressed ( size of puncta ± SEM; Orb2A , 0 . 39±0 . 08 µm2 , Orb2A+Tob , 0 . 51±0 . 07 µm2 , p = 0 . 0003 ) , an effect not seen with the rare Orb2B puncta ( Orb2B , 0 . 43±0 . 05 µm2 , Orb2B+Tob , 0 . 38±0 . 06 µm2 , p = 0 . 20 ) ( Figure 2G ) . Taken together , these observations suggest that Tob-Orb2 association promotes Orb2 oligomer formation either by increasing the Orb2A protein levels and/or enhancing oligomerization . Is Tob involved in activity-dependent oligomerization of Orb2 ? Previously we and others have observed that a neurotransmitter such as tyramine or dopamine regulates Orb2 oligomerization [4] , [5] . Therefore , we checked whether tyramine modulates Orb2-Tob interaction . To this end , we fed-starved flies 10 mM tyramine and after 4 h immunopurified the Tob-Orb2 complex from tyramine-stimulated or -unstimulated adult fly brain using a Drosophila Tob-specific antibody ( Figure S4A ) . Tyramine stimulation increased the Tob-bound oligomeric Orb2 nearly 4-fold ( fold increase in oligomers normalized to monomer ± SEM , 3 . 82±0 . 88 , n = 5 , t test , p<0 . 05 ) ( Figure 3A ) , and the oligomers are resistant to boiling in the presence of 10% SDS and 2 M urea , consistent with it being amyloid-like ( Figure 3B ) . The neurotransmitter serotonin ( 5-HT ) had less effect on Tob-Orb2 association ( Figure S4B ) , consistent with our earlier observation that Orb2 oligomerization is influenced by tyramine and not by 5-HT [5] . Use of Orb2B-specific antibody ( Figure 3A , right panel ) indicated Tob-Orb2B association is enhanced by tyramine stimulation . To determine whether Tob-Orb2A association is also modulated by neuronal activity , we used a genomic construct that encompasses only Orb2A coding region and carries EGFP at the C-terminal end ( pCasperOrb2AEGFP ) [5] . In Tob immunoprecipitate from tyramine-treated samples , we see EGFP reacting bands that correspond to the size of the monomeric- ( ∼87 KDa ) and oligomeric-Orb2AEGFP ( Figure 3C ) . Since it is difficult to determine which neuronal populations are activated by tyramine feeding , we also directly activated the mushroom body neurons ( c747-Gal4 , MB247-Gal4 ) with the temperature-sensitive dTrpA1 channel [29] . The flies were transiently exposed to 30°C ( dTrpA1 active ) for 25 min and then returned to 22°C ( dTrpA1 inactive ) . Compared to flies carrying only dTrpA1 or Gal4 , flies carrying both transgenes ( C747Gal4::UAS-dTrpA1 or MB247Gal4:UAS-dTrpA1 ) , there was enhanced Tob-Orb2 association ( Figure 3D ) . Taken together these observations suggest that neuronal activity that enhances Orb2 oligomerization also enhances Tob-Orb2 association . Because Tob was initially identified as a transcriptional regulator [23] , [24] , we asked whether Tob is restricted to the cell body or distributed throughout the neuron , including the synaptic region . Immunostaining of the adult fly brain revealed that , as expected , Tob is present mostly in the cell body ( Figure S4C ) . However , at low levels Tob staining was also detected in the synaptic-neuropil regions ( Figure 3E , mushroom body lobes ) . Previously we established a method to purify synaptosomes from adult Drosophila head [5] . In Western blotting of synaptosome fractions ( Figure S4D , left panel ) Tob was found in the synaptic membrane fraction , similar to Orb2 ( Figure S4D ) . In Δ80QOrb2 flies , which has significantly less Orb2 protein compared to wild-type flies [5] , the distribution of Tob was unaffected , suggesting synaptic localization of Tob is independent of Orb2 ( Figure S4D ) . Similar to the fly brain , Tob was also detected in the synaptic membrane fraction prepared from the mouse brain ( Figure S4E ) . Activity-dependent association with Orb2 and presence in the synaptic region suggest that Tob may act to regulate Orb2 function and/or oligomerization in the synapse . Because Tob is constitutively present in the adult fly brain , we wondered how Tob-mediated oligomerization of Orb2 could be temporally regulated by neuronal activity . Phosphorylation is known to regulate the activity of both Btg/Tob [30]–[32] as well as the CPEB family members [33]–[35] . Consistent with these observations , protein phosphatase 1 ( PP1-87B ) and protein phosphatase 2A ( PP2A ) regulatory subunit twins were found in the Orb2 protein complex ( Table S1 ) , suggesting that Orb2 may also be regulated via phosphorylation and/or that Orb2 recruits these phosphatases to regulate phosphorylation of other proteins ( such as Tob ) in the complex . Blotting of Orb2 immunoprecipitates from the adult brain with phospho-tag™ [36] , a biotin-tagged dinuclear metal complex that selectively binds to phospho-proteins , detected a small amount of phosphorylated monomeric Orb2B protein ( Figure 4A ) . Similar to the fly brain , when expressed ectopically in S2 cells , both Orb2A and Orb2B are phosphorylated , albeit at very low levels ( Figure 4B ) , suggesting Orb2 proteins are transiently phosphorylated in a regulated manner or kept primarily in an unphosphorylated state by the phosphatase . We observed that Tob is also phosphorylated in the adult fly brain ( Figure 4C ) . To avoid a secondary consequence of prolonged inhibition or activation of phosphatases or kinases in the nervous system , we took advantage of the phosphorylation of Orb2 and Tob in S2 cells to determine the acute role of phosphorylation . To determine if phosphorylation has any effect on Tob-Orb2 association , we blocked dephosphorylation using calyculin ( CY ) , a cell-permeable serine-phosphatase inhibitor that blocks protein phosphatase 2A ( PP2A ) at 0 . 5–1 . 0 nM concentration and protein phosphatase1 ( PP1 ) at ≥2 nM concentration [37] . We observed that an hour after treatment with 1 nM CY , the amount of Orb2A associated with Tob was reduced ( Figure 4D ) . The reduction in association was not due to reduction in Tob or Orb2A protein level an hour after treatment with CY ( Figure 4D ) . Because phosphatases influence a large number of proteins in the cell , the reduction of Tob-Orb2 association could be a secondary consequence of phosphatase inhibition . To test more directly the effect of phosphorylation , in a reciprocal experiment , we first treated cell lysates expressing Tob and Orb2A with calf intestinal phosphatase ( CIP ) and then isolated the Tob-Orb2 complex ( Figure 4E ) . We observed that prior dephosphorylation enhanced the association of Tob with Orb2A ( Figure 4E ) . Likewise , when the Orb2-Tob complex was immunopurified with anti-Orb2 antibody and probed with phospho-tag™ , only phosphorylated Orb2 , but not the hyperphosphorylated Tob , was detected in the immunoprecipitate ( Figure 4F ) . Taken together , these results indicate phosphorylation regulates Tob-Orb2 association . Hypophosphorylation promotes Tob-Orb2A association , and hyperphosphorylation reduces it . Because the Tob-Orb2 association alters the half-life of both proteins and phosphorylation affects their association , we examined the effect of phosphatase inhibition on the half-life of both proteins . When Tob was expressed by itself there was modest change in stability in the presence of CY ( Table S2 ) compared to the untreated samples ( Figure 5A ) . Interestingly , the increase in Tob stability that occurred when co-expressed with either Orb2A ( Figure 5B ) or Orb2B ( Figure 5C ) was ∼50% reduced when the phosphatases were inhibited ( Table S2 ) . The destabilization of Tob was observed only in the presence of the PP2A/PP1 inhibitor CY or okadaic acid ( 1 nM ) but not the PP1 selective inhibitor tautomycin ( 10 nM ) ( Figure S5A ) [37] , [38] . Moreover , the extent of Tob phosphorylation appears to be specifically linked to Orb2 complex formation ( Figure 5D ) . The Orb2 proteins , but not the other homologue of CPEB in Drosophila , Orb1 , enhance phosphorylation of Tob , although Tob interacts with both Orb2 and Orb1 ( Figure S5B and C ) . These results suggest un- or hypophosphorylated Tob binds Orb2 . Association of Tob with Orb2 and PP2A inactivation leads to additional phosphorylation of Tob-Orb2 , which results in dissociation and eventual destabilization of Tob . How does phosphorylation affect Orb2 ? Treatment of S2 cells with PP2A/PP1 inhibitors CY ( 1 nM ) and okadaic acid but not PP1-specific inhibitor tautomycin ( 10 nM ) enhanced phosphorylation of both Orb2A and Orb2B ( Figure 5E and Figure S5D ) . Treatment with alkaline phosphatase , which removes phosphate from serine/threonine , and λ phosphatase , which removes phosphate from serine/threonine as well as tyrosine residues [39] , revealed that upon inhibition of PP2A , Orb2 proteins are phosphorylated at multiple sites ( Figure 5F ) . One of the outcomes of these multiple phosphorylations is a significant increase in Orb2A half-life , from 1 h to >24 h , t ( 1/2 ) Orb2A , 1 . 13±0 . 08 , Orb2A+CY , 35 . 5±17 . 5 h; p = 0 . 010 , and doubling of the Orb2B half-life , t ( 1/2 ) Orb2B , 4 . 32±0 . 53 , Orb2B+CY , 8 . 09±2 . 95 h , p = 0 . 05 ( Figure 5G ) . As decreases in PP2A activity increased Orb2 level , likewise increases in PP2A activity by overexpression of PP2A catalytic subunit microtubule star ( Mts ) that associates with Orb2 ( Figure S5E ) resulted in a ∼4-fold decrease in Orb2A ( 0 . 23±0 . 01 , n = 5 ) and a ∼2-fold decrease in Orb2B ( 0 . 51±0 . 02 , n = 3 ) protein level ( Figure 5H ) . Increases or decreases in protein phosphatase 1 87B ( PP1 ) activity had no effect on Orb2A or Orb2B abundance ( Figure 5E and Figure S5F ) . These results suggest like Tob , Orb2 phosphorylation is regulated by PP2A . However , unlike Tob , inhibition of PP2A stabilizes Orb2 , particularly Orb2A . How does Tob promote Orb2A stabilization and/or enhanced Orb2 oligomerization ? Because phosphorylation enhances Orb2 stability , one possibility is that Tob prevents PP2A from accessing Orb2A . However , the association of PP2A catalytic subunit Mts or regulatory subunit Tws with Orb2 was not affected by increased levels of Tob , and the effect of PP2A on Orb2A half-life was not dependent on Tob level ( Orb2A , 25 . 6±14 . 7 h , p = 0 . 02 , and Orb2B , 19 . 5±8 . 3 , p = 0 . 01 ) ( Figure S6A ) . However , we found Tob promotes Orb2 phosphorylation by recruiting LimK to Tob-Orb2 complex . In our effort to identify kinases that phosphorylate Tob , we initially focused on MapK , as in mammals and in C . elegans Tob is phosphorylated by Map Kinase ( MapK ) [19] , [30] , [31] and MapK sites are conserved in Drosophila Tob ( Figure S6B ) . However , in an in vitro kinase assay , MapK did not phosphorylate recombinant Drosophila Tob , although as expected mammalian Tob1 and Tob2 were phosphorylated ( Figure S6C ) . We searched for other kinases and focused on the neuronal kinase LimK for several reasons . First , Tob activity is regulated by BMPs , and in the nervous system LimK is a key mediator of BMP signaling [40]–[44] . Second , neuronal activity regulates the synaptic concentration of LimK [15] . Finally , LimK is required for synapse formation [40] , [45] , [46] , which is reminiscent of the function of ApCPEB [10] and Orb2 ( our unpublished observation ) . In an in vitro kinase assay , we found LimK efficiently phosphorylates recombinant Drosophila Tob as well as the mammalian Tob1 and Tob2 ( Figure 6A ) but weakly phosphorylates maltose binding protein or Tob family member Btg . Tob is a LimK substrate because in the adult fly head ( Figure 6B ) as wells as in S2 cells ( Figure S6D ) LimK associates with Tob . Next we sought to determine whether Tob phosphorylation by LimK is influenced by Orb2 . We performed in vitro LimK assays on immunopurified Tob-Orb2 complex or on Tob alone ( Figure 6C ) . To our surprise , we observed that Orb2 is phosphorylated by exogenously added LimK in the presence of Tob ( Figure 6C ) . The Tob-Orb2 immunoprecipitate from cells contains other proteins in addition to Tob and Orb2 , and therefore Orb2 may be phosphorylated by other kinases in the presence of LimK . To test such a possibility , we incubated recombinant-soluble Orb2B protein and LimK in the presence or absence of recombinant MBP-tagged Tob . We observed phosphorylation of Orb2B by LimK in the presence of Tob ( Figure 6D ) . Furthermore , LimK copurified with both Orb2A and B only in the presence of Tob . However , in the presence of TobΔ28 , which binds efficiently to LimK ( Figure S6D ) but not to Orb2 , there was a marked reduction in the LimK-Orb2 complex ( Figure 6E ) . Together , these data suggest that Tob is a substrate for LimK and that Orb2 proteins become a substrate of LimK when associated with Tob . Does LimK affect Orb2 oligomerization ? To determine whether LimK regulates activity-dependent oligomerization of Orb2 in the adult brain , we examined Orb2 oligomer formation in a LimK hypomorphic mutant LIMK1EY08757 [40] . In the LIMK1EY08757 adult brain , the level of monomeric Orb2B protein level was similar to that of wild-type flies ( Figure 7A ) . We exposed wild-type and LimK mutant flies to 10 mM tyramine and immunopurified either the Orb2 oligomers ( Figure 7B ) or the Orb2 oligomers associated with Tob ( Figure 7C ) . In the unstimulated brain extract , little or no oligomeric Orb2 was observed in the LimK mutant flies ( Figure 7B and C ) . More importantly , unlike wild-type flies , LimK mutant flies did not undergo a tyramine-dependent increase in Orb2 oligomerization ( Figure 7B and C ) . To determine whether an increase in LimK activity enhances Orb2 oligomerization , we analyzed Orb2 puncta formation in the larval neuron , where unlike the adult brain , ectopic expression of LimK did not cause any observable developmental problem . We found that Orb2A-EGFP coexpressed with active LimK ( ElavGal4::UAS-Orb2A-EGFP; UAS-LimK ) has twice the number of puncta ( 16 . 10±1 . 36 , N = 24 ) compared with flies coexpressing a kinase dead version of LimK , LimKKD ( ELAV::UAS-Orb2A-EGFP; UAS-LimKKD ) ( 8 . 71±1 . 74 , N = 6 , p<0 . 05 ) or flies expressing only Orb2A-EGFP ( 6 . 79±1 . 01 , N = 12 , p<0 . 001 ) ( Figure 7D ) . From these several results , we conclude that Tob serves two functions for Orb2A . First , it binds and stabilizes unphosphorylated Orb2A , and second , it allows Orb2A to be phosphorylated by LimK . Each of these events results in an increase in the effective concentration of Orb2A , which induces Orb2A and/or Orb2A-Orb2B oligomerization . Because Orb2 oligomerization is important for long-term memory and Tob affects Orb2 oligomerization , we wondered whether Tob activity is important for long-term memory . To this end , we used the male courtship suppression paradigm in which a virgin male fly learns to suppress its courtship behavior upon repeated exposure to an unreceptive female ( Figure 7E ) [47] . Previously we and others have found male courtship suppression memory is dependent on Orb2 activity [4] , [5] . The TobRNAi was expressed under mushroom-body-specific driver 201Y Gal4 , which drives expression primarily in the γ-lobe neurons [48] . Expression of Orb2 in γ-lobe in an otherwise orb2 null background is sufficient to rescue the long-term memory defect [3] , [4] . We found that male flies expressing TobRNAi ( 201Y:Gal4-UAS-TobRNAi ) in the γ-lobe showed courtship suppression after training in the short term ( 5 min ) , but the courtship suppression was lost when measured at 24 h or 48 h after training ( Figure 7E ) . In contrast , flies harboring just the RNAi ( UAS-Tob RNAi ) or Gal4 ( 201Y:Gal4 ) had no impairment in courtship suppression 5 min or 24 to 48 h after training . These results are consistent with the idea that Tob activity is important for long-term courtship suppression memory . Our previous work suggested that conversion of neuronal CPEB to an amyloid-like oligomeric state provides a molecular mechanism for the persistence of memory [5] , [6] . However , it is not known how Orb2 oligomerization is regulated so that it will occur in a neuron/synapse-specific and activity-dependent manner . Here we report that factors that influence Orb2A stability and thereby abundance regulate Orb2 oligomerization . We find that Tob , a previously known regulator of SMAD-dependent transcription [23] , [24] and CPEB-mediated translation [16] , associates with both forms of Orb2 , but increases the half-life of only Orb2A . Stimulation with tyramine or activation of mushroom body neurons enhances the association of Tob with Orb2 , and overexpression of Tob enhances Orb2 oligomerization . Both Orb2 and Tob are phosphoproteins . Phosphorylation destabilizes Orb2-associated Tob , whereas it stabilizes Orb2A . Tob promotes Orb2 phosphorylation by recruiting LimK , and PP2A controls the phosphorylation status of Orb2A and Orb2B . PP2A , an autocatalytic phosphatase , is known to act as a bidirectional switch in activity-dependent changes in synaptic activity [14] , [49]–[51] . PP2A activity is down-regulated upon induction of long-term potentiation of hippocampal CA1 synapses ( LTP ) and up-regulated during long-term depression ( LTD ) [14] . Similarly , Lim Kinase , which is synthesized locally at the synapse [15] in response to synaptic activation , is also critical for long-term changes in synaptic activity and synaptic growth [46] . Based on these observations we propose a model for activity-dependent and synapse-specific regulation of amyloid-like oligomerization of Orb2 ( Figure 8 ) . We postulate that in the basal state synaptic PP2A keeps the available Orb2A in an unphosphorylated and thereby unstable state . Neuronal stimulation results in synthesis of Orb2A by a yet unknown mechanism . The Tob protein that is constitutively present at the synapse binds to and stabilizes the unphosphorylated Orb2A and recruits the activated LimK to the Tob-Orb2 complex , allowing Orb2 phosphorylation . Concomitant decreases in PP2A activity and phosphorylation by other kinases enhances and increases Orb2A half-life . The increase in Orb2A level as well as phosphorylation may induce conformational change in Orb2A , which allows Orb2A to act as a seed . Alternatively , accumulation and oligomerization of Orb2A may create an environment that is conducive to overall Orb2 oligomerization . In the absence of an in vitro Orb2A-Orb2B oligomerization assay , we could not distinguish between these two possibilities . For Tob , initial Orb2 association stabilizes Tob . However , association with Orb2 as well as suppression of PP2A activity leads to additional phosphorylation , which results in dissociation of Tob from the Orb2-Tob complex and destabilization . The destabilization of Orb2-associated Tob provides a temporal restriction to the Orb2 oligomerization process . The coincident inactivation of PP2A and activation of LimK may also provide a mechanism for stimulus specificity and synaptic restriction . We find that Orb2A and Orb2B are phosphorylated at multiple sites , including serine/threonine and presumably tyrosine residues . These phosphorylation events are likely mediated by multiple kinases because overexpression of LimK did not affect Orb2 phosphorylation to the extent observed with the inhibition or activation of PP2A , raising several interesting questions . In what order do these phosphorylations occur ? What function do they serve individually and in combination ? What kinases are involved ? Moreover , similar to mammalian CPEB family members , in addition to changing stability , phosphorylation may also influence the function of Orb2A and Orb2B . Does Tob regulate Orb2 function ? In mammals Tob has been shown to recruit Caf1 to CPEB3 target mRNA , resulting in deadenylation [16] , and CPEB3 is known to act as a translation repressor when ectopically expressed . We find Drosophila Tob also interacts with Pop2/Caf1 ( Figure S3E ) [25] and Orb2A and Orb2B can repress translation of some mRNA [52] . Orb2 has also been identified as a modifier of Fragile-X Mental Retardation Protein ( FMRP ) –dependent translation , and Fragile-X is believed to act in translation repression [53] . Therefore , the Tob-Orb2 association may contribute to Orb2-dependent translation repression , and the degradation of Orb2-associated Tob may relieve translation repression . Additionally , if the oligomeric Orb2 has an altered affinity for either mRNA or other translation regulators , Tob can affect Orb2 function by inducing oligomerization . However , the relationship between Tob phosphorylation and its function is unclear at this point . Does involvement of Tob both in transcription and translation serve a specific purpose in the nervous system ? Tob inhibits BMP-mediated activation of the Smad-family transcription activators ( Smad 1/5/8 ) by promoting association of inhibitory Smads ( Smad 6/7 ) with the activated receptor [18] , [24] , [54]–[56] . In Drosophila BMP induces synaptic growth via activation of the Smad-family of transcriptional activators , and subsequent stabilization of these newly formed synapses via activation of LimK [57]–[60] . Our studies suggest Tob and LimK also regulate Orb2-dependent translation , raising the possibility Tob may coordinate transcriptional activation in the cell body to translational regulation in the synapse . Please see Text S1 for a detail description of the proteomic analysis . The Orb2 lines have been previously described [5] , [52] . The following Drosophila strains were obtained from Bloomington Stock Center: mtsXE-2258 ( Stock 5684 ) , Pp2A-29BEP2332 ( Stock 17044 ) , P{EPgy2}LIMK1EY08757 ( Stock 17491 ) , UAS-LimK1HA ( Stock 9116 ) , and UAS-LimK1 Kinase dead ( Stock 9118 ) . The TobRNAi ( Stock 38299 ) on the second chromosome was obtained from Bloomington TRiP collection . The Gal4 lines were generously provided by Douglas Armstrong ( c547-Gal4 , c747-Gal4 ) [61] , Troy Zars ( MB247 , 201Y ) [48] , and Haig Keshishian ( elav-GeneSwitch ) [62] . The c547 drives expression primarily in the ellipsoid body , c747 , MB247 in all lobes of the mushroom body and 201Y primarily in the γ-lobe of the mushroom body . The elav-GeneSwitch drives expression pan-neuronally in an inducible manner . The UAS-dTrpA1 line was generously provided by Paul Garrity [29] . For expression using the GeneSwitch system , the flies were starved for 16–18 h and then transferred to 2% sucrose containing 200 µM RU486 ( mifepristone , SigmaM8046 ) for 12 h . Various genetic combinations were made by standard genetic crosses . Orb2AHA and Orb2BHA constructs were previously described [5] . The untagged Orb2 and Orb2-interacting protein constructs were made by cloning the full-length PCR products into TopoDonor vector ( Invitrogen ) and were subsequently transferred to p AWF using the Gateway cloning system ( Invitrogen ) . The Drosophila Tob cDNA was amplified by RT-PCR and cloned with Topo-TA ( Invitrogen ) . Flag-tagged Tob was created by the subsequent transfer to the mammalian expression vector , pCMV24 ( Invitrogen ) . Standard molecular techniques were then used to subclone into pMT ( Invitrogen ) for S2 cell expression and pUAST ( DGRC ) for use as a Drosophila transgene . To create TobΔ28 , containing an internal deletion of 28 amino acids ( AA235–262 ) , the amino terminal region and C-terminal regions were amplified separately and engineered to contain an internal NotI site . The two fragments ( EcoRI/NotI and NotI/SalI ) were cloned into pCMV24C . Standard techniques were then used to subclone into pMT and pUAST . For the imaging studies , the tdTomato cDNA was inserted in frame to the C-terminal to create pUAST-TobTdTom and pUAST-TobΔ28TdTom . For antigen production , the cDNA encoding Tob AA 267–564 were amplified by PCR and cloned into pRSETA ( Invitrogen ) in frame with the 6XHis tag . The mammalian cDNAs for Tob1 , Tob2 , Ana , and Btg were amplified by RT-PCR from mouse RNA and cloned with Topo-TA , which was subsequently used to produce pCMV24 . For production of recombinant proteins in E . coli , Tob , Tob1 , Tob2 , and Btg were reamplified using primers designed to produce an in-frame 6XHis tag at the C-terminus and then subcloned into pMal-c2X . A full-length cDNA encoding LimK , LD15137 was obtained from DGRC and amplified by PCR for Topo TA cloning . The insert was subsequently transferred to pAcV5 for S2 cell expression . LimKMT was engineered to mutate D500K by site-directed mutagenesis ( Stratagene ) . All sequences were confirmed against the NCBI sequence prior to use . The pCasperOrb2AEGFP construct is comprised of a genomic fragment 1446 nucleotides 3′ of the last Orb2B-specific exon and 1338 bp 5′ of the exonic sequence of the neighboring gene and therefore does not contain coding region of any of the Orb2 isoforms except Orb2A . The ∼8 . 27 Kb genomic fragment was cloned into the SpeI/XhoI site of pCasper4 , and EGFP was introduced at the C-terminal end by creating an in-frame SgrA1 site . Mammalian HEK293 cells were maintained in Dulbecco's modified Eagle's medium supplemented with 10% FBS . Transfections were performed using Lipofectin reagent ( Invitrogen ) . Drosophila S2 cells were maintained in Schneider's medium supplemented with 10% FBS with transfections performed using Effectene reagent ( Qiagen ) . The constructs used are as indicated in the figures . When examining quantitative changes , an empty vector was used to ensure equal quantity of DNA in each transfection . Imagequant software was used to determine densiometric changes , which were subsequently analyzed using Graphpad Prizm software . For immunoprecipitations from cell culture , 3×105 transfected cells were used for each immunoprecipitation . The expression constructs used are as indicated in the figures . Following transfection ( 36–48 h ) , the cells were washed in PBS and lysed in 500 µl of 1% Igepal buffer ( 50 mM Tris-Cl , 7 . 5 , 150 mM NaCl , 1% NP-40 [Igpal] , 1 mM DTT , EDTA free protease inhibitor ) and clarified by centrifugation at 14 , 000 rpm for 10 min . For immunoprecipitations from flies , adult heads were collected following flash freezing and vortexing , lysed in 1% Igepal buffer , and clarified by two rounds of centrifugation at 14 , 000 rpm for 10 min . Protein concentration was determined using a BCA kit ( Pierce Biotechnology ) , and between 1–4 mg of head lysate were used for each immunoprecipitation . The following antibodies were used for immunoprecipitation: anti-HA agarose ( Sigma ) , anti-Flag agarose ( Sigma ) , anti-Tob antibody ( raised in guinea pig 2163 ) , and anti-Orb2 ( raised in guinea pig-2233 and rabbit-273 , 402 ) in conjunction with Protein-A agarose ( Repligen ) . The anti-Tob antibody was raised in guinea pig against the C-terminal end of Tob ( Pocono Rabbit Farm ) , purified using Melon resin ( Pierce Biotechnology ) , and used at 1∶100 dilution . Immunoprecipitations performed using S2 cells were incubated for 2 h at 4°C with continuous rocking , and immunoprecipitations performed using head lysates were incubated for 2 h , and then the ProteinA agarose beads were added with additional 2 h incubation . Following four washes , samples were boiled for 5 min in SDS-PAGE gel loading buffer containing 10% SDS and 2 mM freshly prepared DTT . For immunoprecipitation of Orb2 ∼1 mg of total protein and for Orb2AEGFP ∼3 mg of total protein were used . Western analysis was performed following standard protocols . The following antibodies were used for Western analysis: anti-Flag-HRP ( Sigma , 1∶1 , 000 ) , anti-HA-HRP ( Roche , 1∶500 ) , anti-Tob ( guinea pig , 1∶1 , 000 ) , anti-Orb2 ( rabbit , 1∶2 , 000 ) , anti-Orb2 ( guinea pig , 1∶1 , 000 ) , anti-Orb2B ( rabbit , 1∶1 , 000 ) , and anti-EGFP ( MBL , 1∶1 , 000 ) . To examine endogenous Tob expression in wild-type CantonS flies and c547-Gal4::UAS-Orb2AEGFP and c547-Gal4::UAS-Orb2BEGFP flies , the proboscis was removed and the flies were decapitated . The heads were fixed for 2 h at 4°C in 4% paraformaldehyde ( PFA ) /PBS , incubated overnight in 20% sucrose/PBS , followed by 2 h in a 30∶70 mixture of 20% sucrose/PBS and OCT embedding media ( Tissue-Tek ) . The heads were then embedded in 100% OCT , and frontal cryosections were made of 12 µm . The sections were permeabilized in 1% TritonX containing PBS for 5 min followed by 10 min in 0 . 1% TritonX containing PBS ( PBST ) . The slides were blocked in 10% goat serum containing PBST for 1 h , followed by overnight incubation in 1∶50 dilution of melon-purified ( Pierce Biotechnology ) anti-Tob ( 2163 ) antibody . For the CantonS flies , 1∶50 dilution of nc82 ( Developmental Studies Hybridoma Bank ) was also added to mark the synaptic regions . Anti–guinea pig Alexa-Fluor 633 ( Invitrogen ) secondary antibody was used for Tob detection , and anti-mouse Alexa Fluor 488 ( Invitrogen ) was used for nc-82 detection . Images were acquired at 512×512 pixels with a Zeiss LSM 5 . 0 confocal microscope as 1 µm Z-stacks . Images shown are projections of 10 slices . To examine changes in aggregate number in the adult Orb2EGFP flies , the whole brain was dissected to remove the exoskeleton and air sacs . The brain was fixed in 4% PFA/PBS for 30 min at room temperature , washed three times with PBST for 10 min , and then the whole brain was mounted . Expression of Orb2EGFP and TobTdTom was driven using the ellipsoid body-specific driver , c547 . Images were acquired as above . To quantitate the changes in aggregate number , projections of 20 slices were made for each image centering on the central structure of the ellipsoid body . To examine changes in aggregate number in Lim kinase and Orb2EGFP-expressing animals , third instar larvae were filleted and fixed in 4% PFA/PBS for 10 min at room temperature , washed three times with PBST for 10 min , and mounted . Images of the neurites extending from the ventral ganglia were acquired as described . Projections of 10 slices were made . Axiovision software ( Zeiss , v . 4 . 7 . 1 ) was used to quantitate total area , aggregate number , and aggregate size . A commander script was written to identify the region of interest and the puncta within the region . All measurement parameters were kept constant for each image . pMT∶FlagTob by itself or in conjunction with pMT∶Orb2AHA or pMT∶Orb2BHA was transfected into S2 cells . Expression Tob and Orb2 were induced by adding 700 µM CuSO4 . Following 16 h , the cells were washed and incubated with 50 µg/ml cycloheximide . At the indicated times , samples were collected and later analyzed by Western blot using either anti-Flag or anti-HA antibodies . Densitometric measurements were carried out using ImageQuant and plotted ( percent remaining of time zero versus time ) using Prism Graphpad 5 . The decay curve was fitted using first-order kinetics . To determine the half-life of hyperphosphorylated Tob , a similar analysis was performed with the cells being treated with both cycloheximide and calyculin . To examine Tob phosphorylation , amylose-bound MBP-tagged proteins were incubated with 5 ng of recombinant LimK ( Upstate Biotechnology ) and 10 µCi of [γ–32P]ATP for 20 min at 30°C with semiconstant shaking . Control reactions were performed identically but in the absence of LimK . Kinase dilution buffer and reaction buffer were prepared according to the manufacturer's specifications . Following phosphorylation , the proteins were washed four times in PBS with 0 . 1% TritonX and once with PBS prior to loading an 8% SDS/PAGE . Following electrophoresis , the gel was dried and exposed from 4 h to overnight . To examine phosphorylation of recombinant Orb2B , His-tagged Orb2B was expressed in E . coli BL21 ( DE3 ) using a slow induction protocol , and a low amount of soluble protein was purified in Ni+2 column . Approximately 10 ng of Orb2B , MBP-tagged Tob was used in the kinase reaction . To examine phosphorylation of the Orb2-Tob complex , 6×105 S2 cells were transfected with pAct∶Orb2AHA or pAct∶Orb2B individually and in combination with pMT∶Tob . The cells were lysed in 1% Igepal buffer ( 50 mM Tris-Cl , 7 . 5 , 150 mM NaCl , 1% NP-40 [Igpal] , 1 mM DTT , EDTA free protease inhibitor ) and incubated for 15 min with 50 U/ml CIP . Following centrifugation at 14 , 000 rpm for 10 min , the supernatant was incubated for 2 h at 4°C with anti-HA agarose . The immunoprecipitates were washed twice with 1% Igepal buffer and once with a modified RIPA buffer ( 50 mM Tris , 300 mM NaCl , 0 . 1% SDS , 1% Igepal ) . The sample was then split into thirds , with one-third examined by Western blot to ensure equality in protein levels and the other two-thirds used for the in vitro kinase assay described above . For the Tob alone samples , 12×105 S2 cells were transfected with pAcOrb2AHA and pMTTob , and the complex was purified as above and dissociated in 1% Igepal buffer containing 1 M NaCl for 15 min at room temperature . The eluate was then normalized to 150 M NaCl and Tob purified by precipitation with anti-Flag agarose ( Sigma ) . Complete dissociation was ensured by Western analysis . The protein abundance studies were carried out in 4%–12% Bis-Tris SDS-PAGE ( Invitrogen ) and run in MES-SDS ( 50 mM MES , 50 mM Tris Base , 0 . 1% SDS , 1 mM EDTA , pH 7 . 3 ) buffer . In these buffer conditions and in the gradient gel , the phosphorylated bands migrate close to each other , which simplifies the quantification of band intensity . Also , in protein abundance studies , the total cell lysates were prepared , unless mentioned , in the absence of phosphatase inhibitors , again to ensure quantification of the total protein accurately . The 4%–12% gels were also used for the detection oligomeric Orb2 and phospho-tag™ blotting of Orb2- or Tob immunoprecipitate from the adult fly head . To measure phosphorylation status via mobility shift , we found that an 8% SDS-PAGE run in conventional Tris-Glycine buffer ( 25 mM Tris , 192 mM glycine , 0 . 1% SDS , pH 8 . 6 ) is more effective , and in 8% gel the different phosphorylated forms of Orb2 and Tob proteins were better separated . For detection of the phosphoproteins via phospho-tag™ , both 8% and 4%–12% SDS-PAGE were used . Flies were maintained using standard fly husbandry methods . For behavioral analysis , flies were maintained on standard cornmeal food at 25°C and 60% relative humidity on a 12 h/12 h light-dark cycle . Virgin males and females were collected at eclosion under CO2 anesthesia . Males were isolated and placed in individual food vials . All flies were aged for 5 d before behavioral training and testing . To increase the efficiency of RNAi , flies were shifted to 30°C for 3 d before training . The control flies were treated similarly . Canton S females ( 4 d old ) were mated the night before they were used in training . Males were assayed for courtship conditioning using a modified version of the spaced training described by McBride et al . ( 1999 ) [63] . For spaced training , individual males were placed in individual small food tubes ( 16×100 mm culture tubes , VWR ) with a mated female for 2 h . The female was removed , and the male was left alone for 30 min . A different mated female was placed in the tube with the male for another 2 h . The female was removed and the male again rested for another 30 min . A third mated female was introduced in the tube for 2 h and removed at the end of the trial . Control males were treated exactly the same way , except no mated females were introduced into the tube . Memory was tested 5 min , 24 h , and 48 h after training . All tests were performed in a 1 cm courtship chamber . Fresh mated females were used for all time points . All memory tests were recorded ( for 10 min ) and analyzed using a customized software . The courtship index of each male was obtained by manual and/or automatic analysis of the movies by an experimenter blind to the genotype and experimental conditions .
The formation of stable long-term memories involves the synthesis of new protein , however the biochemical basis of this process is unclear . A family of RNA binding proteins , Cytoplasmic Polyadenylation Element Binding ( CPEB ) proteins , are known to regulate synaptic activity and stabilization of memory . The Drosophila CPEB is called Orb2 , and its amyloid-like oligomers are critical for the persistence of long-lasting memories . Amyloid formation is often unregulated and stochastic in nature , and the amyloid state is usually dominant and self-sustaining . However , to serve as a substrate for long-lasting memory , the amyloid-like oligomerization of Orb2 must be regulated in a space- , time- , and stimulus-specific manner . Orb2 has two protein isoforms: Orb2A , which is present only in low abundance , and Orb2B , which is the abundant form . Orb2A is important for oligomerization as well as memory persistence . Previous studies suggested that Orb2A may act as a seed to induce oligomerization of the constitutive Orb2B isoform . Therefore , the availability of Orb2A protein would be an important determinant of Orb2 oligomerization . Here we have analyzed how Orb2 conversion to the oligomeric state is regulated . We find that Orb2A is a very unstable protein and that phosphorylation-dephosphorylation of this isoform via canonical neuronal signaling modules can regulate Orb2A stability , and thereby its abundance . We also show that Tob , a known regulator of CPEB-mediated translation , acts as a stabilizer for Orb2A and triggers Orb2 oligomerization . These observations suggest that amyloid formation can be regulated in a dynamic manner by controlling the availability of the seeding Orb2A protein .
You are an expert at summarizing long articles. Proceed to summarize the following text: Echinoderms , which are phylogenetically related to vertebrates and produce large numbers of transparent embryos that can be experimentally manipulated , offer many advantages for the analysis of the gene regulatory networks ( GRN ) regulating germ layer formation . During development of the sea urchin embryo , the ectoderm is the source of signals that pattern all three germ layers along the dorsal-ventral axis . How this signaling center controls patterning and morphogenesis of the embryo is not understood . Here , we report a large-scale analysis of the GRN deployed in response to the activity of this signaling center in the embryos of the Mediterranean sea urchin Paracentrotus lividus , in which studies with high spatial resolution are possible . By using a combination of in situ hybridization screening , overexpression of mRNA , recombinant ligand treatments , and morpholino-based loss-of-function studies , we identified a cohort of transcription factors and signaling molecules expressed in the ventral ectoderm , dorsal ectoderm , and interposed neurogenic ( “ciliary band” ) region in response to the known key signaling molecules Nodal and BMP2/4 and defined the epistatic relationships between the most important genes . The resultant GRN showed a number of striking features . First , Nodal was found to be essential for the expression of all ventral and dorsal marker genes , and BMP2/4 for all dorsal genes . Second , goosecoid was identified as a central player in a regulatory sub-circuit controlling mouth formation , while tbx2/3 emerged as a critical factor for differentiation of the dorsal ectoderm . Finally , and unexpectedly , a neurogenic ectoderm regulatory circuit characterized by expression of “ciliary band” genes was triggered in the absence of TGF beta signaling . We propose a novel model for ectoderm regionalization , in which neural ectoderm is the default fate in the absence of TGF beta signaling , and suggest that the stomodeal and neural subcircuits that we uncovered may represent ancient regulatory pathways controlling embryonic patterning . It is becoming increasingly apparent that most developmental processes are controlled by dozens or hundreds of regulatory genes assembled into complex gene regulatory networks ( GRNs ) , rather than by a small number of master genes . By describing the functional relationships between these genes , GRNs allow integration of various levels of information on the activity of transcription factors and signaling pathways that regulate developmental processes . Over the last few years , a number of GRNs have been elucidated , including regulatory networks that drive specification of germ layers or organs in various organisms [1]–[7] . Sea urchin embryos offer many advantages for GRN analysis [8] . Unlike vertebrates , sea urchin embryos have a relatively small number of cells ( about 800 cells in a gastrula ) are fully transparent , and their embryos , available in huge number , develop rapidly as free-swimming larvae . A panoply of techniques is available for the functional analysis of developmental genes including treatments with pharmacological inhibitors and exogenous ligands , microinjection of antisense morpholino oligonucleotides for gene loss of function , and overexpression of mRNA for gain of function . Analysis of the first full sea urchin genome sequence from Strongylocentrotus purpuratus has revealed that echinoderms have a vast genetic repertoire but a low level of genetic redundancy , with almost all developmental regulatory genes being present as single copy [9] . Furthermore the sea urchin embryo has a rich history of experimental embryology and a wealth of biological knowledge is available on various aspects of its development . Finally , echinoderms occupy a basal position within the deuterostome lineage and are more related to chordates than most other invertebrate phyla . These various properties mean that echinoderms are a key phylum to study the evolution of developmental mechanisms and to understand the evolutionary origin of certain features of the chordate body plan . Axis specification has been extensively studied in the sea urchin [10] . Pioneer studies on endomesoderm patterning have shown that it is possible to dissect a complex GRN without the use of classical genetics by combining cis-regulatory and functional analysis , embryological , cell biological and genomic/computational approaches [11] . However , while considerable knowledge is available regarding the functional relationships between genes controlling specification of the territories along the animal vegetal axis , much less was known until recently on the genes that regulate ectoderm patterning and morphogenesis of the embryo along the dorsal-ventral axis . This gap started to be filled recently by the identification in Paracentrotus lividus of the TGFβ Nodal , Univin , and BMP2/4 as key regulators of ectoderm patterning [12]–[15] . Nodal is expressed zygotically , starting at the 32-cell stage . Its expression is initially very broad then it is rapidly restricted to a discrete sector of the ectoderm that corresponds to the presumptive ventral ectoderm . The restricted expression of nodal is so far the earliest known regional difference in zygotic gene expression detectable along the dorsal ventral axis . However , experiments performed at the beginning of the century have shown that as early as the 8-cell stage , respiratory gradients , visualized by mitochondrial cytochrome oxidase activity , prefigure the dorsal-ventral axis of the early embryo [16] . In addition , orientation of the dorsal-ventral axis can be biased by using respiratory inhibitors or by culturing embryos in hypoxic conditions [17]–[19] . Recent studies reported that mitochondria are asymmetrically distributed in some batches of eggs of Strongylocentrotus purpuratus with the ventral side displaying the highest concentration , and that microinjection of purified mitochondria can bias orientation of the dorsal-ventral axis [20] , [21] . A possible link between the transcriptional activation of nodal and these redox gradients is suggested by the finding that the stress activated kinase p38 is required for nodal expression [22] . An attractive model therefore emerges in which an asymmetry in the distribution of mitochondria may generate a redox gradient , which would activate p38 anisotropically leading to the spatially restricted expression of nodal . However , strong experimental evidence supporting this model are presently lacking and experimental manipulations that perturb the redox gradient have very modest effects on the spatial expression of nodal [21] ( Thierry Lepage unpublished results ) . If the role of redox gradients in the establishment of nodal expression is still unclear , in contrast , the role of a reaction diffusion mechanism , which involves a short range Nodal positive autoregulation and a long range inhibition mechanism by the Nodal antagonist Lefty , is probably essential to convert a subtle initial anisotropy into a sharply defined pattern [12] . Overexpression of nodal strongly ventralizes the embryos and largely mimics the effects of treatments with nickel chloride [23] , knockdown of Nodal function using morpholinos or by overexpressing lefty , completely eliminates dorsal-ventral polarity and results in embryos with disorganized skeletal elements , no mouth and a straight archenteron . The same , strongly-radialized , phenotypes are obtained by blocking translation of the univin transcript which encodes a Vg1/GDF1 ortholog expressed maternally [14] , suggesting that Univin may either act upstream of nodal expression or that it may heterodimerize with Nodal as suggested in vertebrates [24] , [25] . Intriguingly , in the absence of Nodal , not only is the expression of ventral marker genes such as brachyury , goosecoid or lefty abolished , but the expression of dorsal marker genes such as tbx2/3 and of the novel transmembrane protein 29D is suppressed as well [13] . As a consequence , most of the ectoderm ( except the ectoderm surrounding the animal and vegetal poles ) of Nodal morphants differentiates into a thick ectoderm consisting of cuboidal ciliated cells that morphologically resembles the neurogenic ectoderm of the ciliary band . Injection of synthetic mRNA encoding either Nodal or an activated Nodal receptor into one blastomere of Nodal morphant embryos at the 8-cell stage is sufficient to rescue both the ventral and the dorsal side of these embryos , indicating that a distinct relay molecule specifies dorsal fates . This relay molecule was recently identified as BMP2/4 , which is transcribed in the ventral ectoderm downstream of Nodal signaling , has a strong dorsalizing activity when overexpressed , and mediates the “rescue” of dorsal structures when Nodal signaling pathway is ectopically activated in a cell-autonomous manner in a Nodal loss of function background [26] . Furthermore , despite its ventral transcription , BMP2/4 has been shown to trigger receptor mediated signaling exclusively on the dorsal side of the embryo . Based on this series of findings , a basic model for sea urchin embryo dorso-ventral patterning emerges in which the dorsal ectoderm is induced by BMP2/4 signals emanating from the opposite side of the embryo . The ventral side produces inducing factors such as Nodal and BMP2/4 but it is also a source of inhibitors such as Lefty , which restricts Nodal signaling to the ventral side , and Chordin , which prevents BMP2/4 signaling in the ventral ectoderm . In the absence of lefty function , Nodal signaling is unrestricted and propagates throughout a large belt of cells surrounding the embryo while in the absence of chordin , ectopic BMP2/4 signaling occurs on the ventral side and causes abnormal patterning of the embryo [12] , [26] . Therefore , in the sea urchin as in vertebrates patterning of the embryo critically relies on sequential inductive events mediated by Nodal and BMP2/4 and on the interplay between ligands and their antagonists . However , in the sea urchin embryo , both the ligands ( Nodal and BMP2/4 ) and their antagonists ( Chordin and Lefty ) are co-expressed in the ventral ectoderm , which may represent a D/V organizer , and D/V patterning requires translocation of BMP2/4 from the ventral side where it is produced to the dorsal side where it activates its receptor . Another pathway that plays a crucial role in ectoderm patterning is the Wnt pathway . Wnt signaling from the vegetal pole region is required to restrict formation of the animal pole domain . The animal pole domain is a small ectodermal territory made of thick ciliated ectoderm that forms in the apical region of the embryo . This six3 expressing neurogenic territory appears to be specified at mesenchyme blastula stage and is thought to be resistant to Wnt and TGF beta signaling [10] , [13] , [27] , [28] . When the Wnt pathway is blocked by overexpression of cadherin or of a dominant negative form of TCF , the animal plate expands towards the vegetal pole and most of the ectoderm differentiates into neuroectodem , which contains scattered serotonergic neurons normally restricted to the animal plate region [27] . In contrast , inhibition of Nodal/Vg1/Activin signaling with a pharmacological inhibitor of the Nodal receptor causes formation of a thickened ciliated ectoderm , but this ciliated ectoderm does not appear to be specified as animal plate ectoderm since serotonergic neurons remain localized to the animal pole in these embryos . Instead , this ectoderm may have a ciliary band like identity as first proposed by Duboc et al . [13] . This idea is supported by the finding that the ectoderm of Nodal morphants abundantly expresses the ciliary band marker tubulinß3 [13] and by the presence of ectopic neurons as revealed by staining for the pan-neural marker synaptotagmin [27] . However , more in depth analysis of the specification state of this ectoderm in the absence of Nodal signaling is required to further test this idea . Deciphering the gene regulatory network that controls patterning of the ectoderm is of special importance for several reasons . The first reason is that patterning of all three germ layers relies on the activity of a signaling center located in the ventral ectoderm and analyzing how this signaling center works is essential to understand how dorsal ventral polarity of the embryo is established . Another reason is that , despite a wealth of information available on establishment of D/V polarity during normal and regulative development , the GRN that controls specification of the main ectodermal territories ( ventral ectoderm , dorsal ectoderm and ciliary band ) remains incompletely described and the molecular mechanisms involved in regionalization of the embryo along the D/V axis in normal and perturbed embryos have just started to be investigated [29] . A third reason to study the D/V GRN comes from the basal evolutionary position of echinoderms within the deuterostome superclade , and of the notion that studying D/V axis formation in echinoderms will contribute to better understand the evolution of the patterning mechanisms that shaped the deuterostome body plan . Indeed , recent studies have shown that this GRN relies extensively on cell interactions mediated by TGF beta family members such as Nodal , Univin/Vg1 and BMP2/4 , molecules that play crucial roles during vertebrate development [13] , [14] , [26] . Finally , since major morphogenetic processes such as mouth formation , skeleton formation and elongation of the arms and apex of the larva occur along the D/V axis , dissecting the D/V GRN offers the promise to study how morphogenetic processes are encoded in the genomic program of development . This will help to fill the gap that presently exists between our understanding of cell fate specification and our knowledge of how genes work together to regulate morphogenesis . We previously described the core of the GRN that acts downstream of Nodal and is responsible for patterning of the ectoderm along the dorsal-ventral axis [13] . We showed that on the ventral side , Nodal acts at the top of this GRN by regulating the expression of lefty , bmp2/4 , goosecoid and brachyury while on the dorsal side BMP2/4 activates the expression of tbx2/3 . Although the functional relationships between these key genes was elucidated in this initial study , recent molecular screens conducted by us ( Thierry Lepage unpublished ) and others [30] revealed that many more downstream genes are likely involved in patterning of the ectoderm along the dorsal ventral axis . A large scale effort to dissect the ectoderm GRN in S . purpuratus was recently published by Su and colleagues who used the nanostring technology to monitor the effects of gene perturbations [29] . However , this technique , which measures RNA concentrations in whole embryos , lacks the spatial resolution that is required to analyze the changes in the complex spatial expression patterns of many developmental genes . To understand better how the ectoderm of the sea urchin embryo is patterned by Nodal and BMP2/4 signals and to expand our provisional GRN , we conducted a large-scale study . Using a combination of gain of function and loss of function studies , and taking advantage of the amenability of Paracentrotus lividus embryos to detailed phenotypic analyses and in situ hybridization studies , we analyzed at high spatial resolution the expression and regulation by Nodal and BMP2/4 of 18 transcription factors and 8 signaling molecules that displayed a restricted expression along the D/V axis . Using an assay with recombinant proteins , we identified direct targets of Nodal and BMP2/4 . Finally , by conducting a large-scale analysis of the epistatic relationships between these genes , we were able to start ordering them into a hierarchy and to identify key regulators acting downstream of Nodal and BMP2/4 . Not only our results uncover novel and probably ancient regulatory circuits that drive morphogenetic processes such as mouth formation and neural induction , but they elicit a model for patterning of the ectoderm in which two successive inductive events regionalize the ectoderm into three territories: the ventral ectoderm that is specified by Nodal , the dorsal ectoderm that is specified by BMP2/4 and the neurogenic ectoderm of the ciliary band , which forms between the ventral and the dorsal ectoderm in a region protected from Nodal and BMP signaling . In addition , these findings highlight a striking parallel between the mouse embryo and the sea urchin embryo by showing that in both models a neurogenic ectoderm is the default state of ectoderm differentiation in the absence of Nodal and BMP signaling . Our analysis provides a picture of this GRN significantly different from that proposed by Su et al . in S . purpuratus and stresses the importance of the spatial resolution level in the analysis of gene regulatory networks in early embryos . To elucidate the gene regulatory network that controls specification and patterning of the ectoderm in Paracentrotus , we first performed large scale in situ hybridization screens . In addition to a random screen initiated several years ago , which allowed us to characterize the expression of 4000 randomly selected cDNAs ( Thierry Lepage unpublished ) , we screened a P . lividus EST database against S . purpuratus sequences encoding transcription factors and signaling molecules and analyzed the expression of all those that were expressed during development of the sea urchin embryo [30]–[34] ( Table 1 ) . This allowed us to assemble a list of 36 genes displaying a robust expression in either the ventral ectoderm , the dorsal ectoderm or in the ciliary band territory ( Table 1 ) ( Figure 1A , 1B ) . Genes expressed in the animal pole domain were largely excluded from this analysis since most of them do not display a restricted expression along the D/V axis . The expression patterns of a number of the genes presented in this study had previously been described at various degrees in S . purpuratus [30]–[34] but they had never been described in Paracentrotus . In addition , the expression of several genes analyzed here , including smad6 , gfi1 , id , admp2 , BMP1 , and oasis has not been described previously in either species . The earliest asymmetrically distributed transcript that we identified in the in situ screens is the maternal transcript encoding mitochondrial cytochrome oxidase , with cleavage stage embryos frequently displaying a graded distribution of transcripts in the presumptive ectoderm ( Figure 1A1 ) . This asymmetrical distribution of a mitochondrial transcript likely reflects the asymmetrical distribution of mitochondria previously reported by Coffman and colleagues [20] , [21] . At the zygotic level , the first signs of tissue regionalization within the ectoderm are seen at 64/128 cell-stage with nodal and lefty transcripts starting to accumulate in the presumptive ventral territory ( Figure 1A2 , 3 ) [12] , [13] . The second wave of zygotic genes displaying a restricted expression along the D/V axis starts at the prehatching blastula with bmp2/4 and goosecoid starting to be transcribed in the ventral ectoderm rapidly followed by fgfr1 , chordin and nk2 . 2 at the swimming blastula stage ( Figure 1A4–8 ) [15] , . In Paracentrotus , there is no known example of genes displaying a restricted expression in the dorsal ectoderm before the swimming blastula stage . The first genes to be expressed in the dorsal ectoderm are nk2 . 2 and tbx2/3 , whose expression increases abruptly in the presumptive dorsal territory after hatching ( Figure 1A8 , 9 ) [31] , [40] , [41] . These genes are therefore good candidates as immediate early targets of Nodal or BMP2/4 signaling and are likely to play an early role in specification of these territories . Soon after ingression of the primary mesenchyme cells , when the embryo acquires its bilateral symmetry , a third wave of zygotic genes starts to be expressed . This includes the largest number of genes such as foxA , brachyury , foxG , Delta , NK1 , in the ventral ectoderm ( Figure 1A10–14 ) [34] , [42]–[45] , onecut/hnf6 and fgfA ( Figure 1A15–16 ) [46]–[48] in the lateral ectoderm and glypican5 , irxA , hox7 , dlx , smad6 , msx , id , oasis , admp2 and cyIII in the dorsal ectoderm ( Figure 1A17–26 ) [26] , [30] , [31] , [36] , [39] . Based on the timing of their expression , genes in this category are likely secondary targets of Nodal or BMP2/4 signaling . Starting at the early gastrula stage , additional genes start to be expressed with a restricted pattern along the D/V axis , with ptb transcripts accumulating in the ventral ectoderm ( Figure 1A27 ) , bmp1 , deadringer ( dri ) , otx , and rkhd being expressed in a broad domain encompassing the ventral ectoderm and ciliary band territory ( Figure 1A28–31 ) [13] , [49]–[52] , and gfi1 , pax2/5/8 , wnt8 , univin transcripts starting to be expressed in the presumptive ciliary band ( Figure 1A32–36 ) [32] , [36] , [48] , [53] . Similarly , atbf1 , unc4 , wnt5 start to be expressed in the dorsal ectoderm at the early gastrula stage . ( Figure 1A37–39 ) . Finally , at prism stage , tubulinß3 transcripts accumulate in the presumptive ciliary band while transcripts encoding the sea urchin specific transmembrane protein 29D accumulate in the presumptive dorsal ectoderm ( Figure 1A 40 , 41 ) [13] , [54] . At the mesenchyme blastula stage , foxG ( also known as Brain factor1 or Bf1 ) is expressed in two broad ventro-lateral stripes that largely overlap with the goosecoid expression territory ( Figure 1A12 ) , while Delta is first expressed in the ectoderm in a cluster of cells at the animal pole as well as in individual cells , possibly neurons , first on the ventral side then on the dorsal side , within the vegetal part of the foxG expression domain . At the prism/early pluteus stage , the pattern of foxG resolves into a thin belt of cells on the ventral side of the presumptive ciliary band ( Figure 1A42 ) [34] while Delta expression now occurs in a salt and pepper pattern within the ciliary band and facial ectoderm ( Figure 1A43 ) [55] . For simplification we divide the ectoderm into three main territories along the dorsal-ventral axis , however there are additional regional differences in gene expression that show that more than three regions can be defined ( Figure 1C ) . For example , the homeobox gene nk1 is expressed in the ventral-vegetal ectoderm in a region fated to become the ventral supra-anal ectoderm ( Figure 1C1 ) . Similarly , several dorsally expressed genes such as msx , id , oasis , admp2 or unc4 are strongly expressed in the dorsal-vegetal region fated to become the dorsal supra-anal ectoderm ( Figure 1A22–25; 38; Figure 1D2 , 3 ) . Thus , the ectoderm near the vegetal pole is divided into at least two sub domains along the D/V axis . Gene expression patterns also revealed that the ventral and dorsal ectodermal regions are progressively regionalized into different domains . This is best illustrated by the dynamics of goosecoid expression . goosecoid and brachyury are initially co-expressed within the ventral ectoderm ( Figure 1A5 , 11 ) , but during gastrulation , the expression domain of goosecoid is progressively cleared from the center of the ventral ectoderm ( Figure 1C3 ) . While goosecoid expression is progressively shifted at the periphery of the ventral ectoderm , forming a belt of cells abutting the ciliary band , brachyury and foxA remain expressed at the center of the ventral ectoderm , where the stomodeum will form ( Figure 1C2 ) . Similarly , analysis of gene expression within the dorsal ectoderm revealed the existence of nested patterns , with genes like nk2 . 2 , tbx2/3 and dlx ( Figure 1D4 , Figure 1A , 9 , 20 ) being expressed in a broader domain than genes like msx , wnt5 or smad6 ( Figure . 1A22 , 29 , 39; 1D3 , 4 see also [26] ) and genes like irxA being expressed in a sub domain of the dorsal ectoderm that excludes the dorsal apex ( Figure 1D1 ) . Finally , sub regions can also be recognized within the ciliary band territory starting at the early gastrula stage , with genes like fgfA , vegf , pax2/5/8 and sprouty being expressed in the ventral lateral region ( Figure 1A33 , 34; Figure 1D6 and data not shown ) [48] , [56] , genes like onecut/hnf6 or gfi1 being expressed in the entire presumptive ciliary band territory ( Figure 1C5; Figure 1D5 ) , and genes like foxG , which in vertebrates is expressed in and required for specification of the ventral telencephalon [57] , [58] , being expressed in a ventral subdomain of the ciliary band ( Figure 1A42 ) . Interestingly , several genes whose expression is later confined to the ciliary band are initially expressed much more broadly in the ectoderm ( Figure 1E ) . This is particularly apparent for glypican5 , fgfA , univin , and wnt8 , which are expressed in a large belt of ectodermal cells at blastula stage and also for the neural marker onecut/hnf6 which is first expressed ubiquitously , then in a broad ventro-lateral domain , and only later in the ciliary band ( Figure 1E1–6 ) [26] , [46]–[48] . This suggests that the expression of these ciliary band marker genes is initiated by broadly distributed transcription factors and later repressed on the ventral and/or dorsal sides by additional factors . As a first step to dissect the ectoderm gene regulatory network , we analyzed the regulation of these broadly expressed ciliary band genes . Since SoxB1 plays a critical role in ectoderm patterning in the sea urchin [59] and in the specification and maintenance of neural regions in vertebrates [60] , we tested if SoxB1 is required for expression of ciliary band marker genes ( Figure 1F ) . Injection of morpholinos against SoxB1 abrogated the expression of most markers of the neurogenic ectoderm of the ciliary band including onecut , gfi1 , foxG , egip , fgfA , pax2/5/8 , univin , wnt8 and strongly affected the spatial expression of dri and otx [14] ( Figure 1F1–20 ) . This result supports the idea that transcription of at least a subset of ciliary band marker genes is initiated by broadly distributed transcription factors such as SoxB1 and later restricted to the ciliary band by zygotic factors induced by Nodal and/or BMP signaling . We next tested how Nodal and/or BMP2/4 regulate the expression of the 36 genes identified in the in situ screen . We focused on Nodal and BMP2/4 since previous studies showed that these two ligands are essential for specification and patterning of the ventral and dorsal territories . We first analyzed the effects of overexpressing nodal or bmp2/4 on the expression of ectodermal markers . Embryos were injected with nodal or bmp2/4 mRNA and the expression of the ventral , dorsal , or ciliary band markers was monitored at different stages . In most cases , results were confirmed by treatments with recombinant mouse Nodal or BMP4 . Overexpression of nodal mRNA or treatments with recombinant Nodal protein dramatically expanded the expression of nodal , bmp2/4 , chordin , lefty , goosecoid and brachyury as reported previously ( Figure 2 ) [13] , [26] . Overexpression of Nodal also expanded the ectodermal domain of expression of foxA and fgfr1 at mesenchyme blastula stages . Similarly , the expression domain of nk1 , which is normally restricted to the ventral vegetal ectoderm , became radial in nodal overexpressing embryos . Genes expressed in the ciliary band behaved differently depending on the gene . In the case of deadringer , bmp1 and univin , which are expressed in the ciliary band and in the ventral ectoderm , overexpression of nodal expanded their expression to the whole ectoderm . In the case of wnt8 , which is expressed in two broad lateral stripes at gastrula stages , as well as in the case of fgfA and its downstream target pax2/5/8 , which are expressed in the ventral sub domain of the ciliary band , all expression was eliminated by exogenous nodal . However , in the case of foxG , egip , onecut/hnf6 , gfi1 , otx , exogenous nodal suppressed expression in most of the ectoderm except in the animal and/or vegetal most domains of the ectoderm . Overexpression of nodal increased the number of ventral-vegetal cells that normally express Delta at the early gastrula stage and , at 48h , produced ventralized embryos in which most Delta expressing cells were located at the animal pole and in the vegetal most ectoderm . Largely similar phenotypes were obtained following treatments with nickel chloride ( Figure S3 ) although we noted intriguing differences in the behavior of a few genes including wnt8 , univin , fgfA and pax2/5/8 , in response to these perturbations . Overall , these data are consistent with the idea that in nodal-overexpressing or nickel treated embryos , radially expressed Nodal promotes specification of ventral ectodermal fates and suppresses specification of the ciliary band in a large equatorial region but not in the animal pole region or in the ectoderm surrounding the blastopore . One likely reason that may explain why the vegetal ectoderm is refractory to Nodal overexpression or to nickel treatment is that in these embryos , Nodal signaling is restricted to the equatorial region [13] . The vegetal ectoderm may therefore be protected from Nodal activity by Lefty which is thought to diffuse farther than Nodal [12] , . Consistent with this idea , in Nodal treated embryos and in nickel treated embryos , nodal expression expands to a large belt of cells in the equator and a ciliary band differentiates in the vegetal most ectoderm while in lefty morphants , which also display unrestricted Nodal signaling , ciliary band marker genes such as tubulinß3 and onecut/hnf6 are expressed in the animal pole region but not in the vegetal ectoderm ( Figure 2 ) [12] . Taken together , these results suggest that a Lefty dependent inhibition of Nodal signaling is required for ciliary band formation in the vegetal pole region . Finally , as expected , overexpression of nodal eliminated the expression of all the dorsal marker genes we tested including , nk2 . 2 , tbx2/3 , smad6 , msx , atbf1 , wnt5 , admp2 , unc4 , hox7 , dlx , and 29D ( Figure 2 ) . Reciprocally , overexpression of bmp2/4 or treatments with recombinant BMP4 protein eliminated expression of all the ventral marker genes we tested including nodal , bmp2/4 , chordin , goosecoid , foxA , lefty ( not shown ) , brachyury , and nk1 ( Figure 3 ) . As in the case of nodal overexpression , misexpression of bmp2/4 or of the activated Alk3/6 BMP receptor ( Alk3/6QD ) [26] strongly suppressed the expression of the ciliary band markers such as bmp1 , foxG , onecut/hnf6 , otx , gfi1 , tubulinß3 , egip , dri , univin , wnt8 , fgfA and pax2/5/8 . However , unlike in the case of nodal overexpressing or nickel treated embryos , which conserved expression of ciliary band markers in the animal pole and in vegetal ectodermal regions , overexpression of bmp2/4 or of the activated type I BMP receptor ( Alk3/6QD ) efficiently eliminated the expression of all the ciliary band markers at the animal pole and in the vegetal most ectoderm as well as the expression of animal pole specific markers such as foxQ2 highlighting the very strong antagonism existing between high level of BMP2/4 signaling and specification of the animal pole and ciliary band cell fates . Finally , misexpression of BMP2/4 dramatically expanded the expression of all the dorsal marker genes including tbx2/3 , smad6 , nk2 . 2 , wnt5 , oasis , msx , irxA , dlx , atbf1 , hox7 , unc4 , admp2 , id and 29D . We next sought to determine which genes are direct targets of Nodal and BMP2/4 signaling . Based on the timing of expression of the ventral or dorsal markers genes , it was expected that only a subset would be direct targets of Nodal or BMP2/4 signaling . For example , only lefty , bmp2/4 , chordin , goosecoid , nk2 . 2 , fgfr1 and tbx2/3 are expressed at swimming blastula stage , the expression of most of the other starting only at mesenchyme blastula stage . We therefore tested whether the ventral marker genes are transcribed in direct response to Nodal and whether the dorsal marker genes are transcribed in direct response to BMP2/4 signaling or if transcription of these genes requires protein synthesis . To achieve this , we treated embryos at the hatching blastula , mesenchyme blastula or gastrula stages with recombinant mouse Nodal or BMP2/4 proteins in the presence or absence of a protein synthesis inhibitor ( Figure 4 ) , and analyzed the expression of all the ventral and all the dorsal marker genes . Short treatments with recombinant Nodal protein at blastula stage strongly induced expression of nodal , lefty , bmp2/4 , chordin , goosecoid , nk2 . 2 and fgfr1 throughout most of the ectoderm ( Figure 4A ) . These effects were observed even in the presence of a translational inhibitor suggesting that these genes are direct targets of Nodal signaling . In contrast , short treatments with Nodal at either mesenchyme blastula or gastrula stages failed to induce any ectopic expression of the other ventral genes such as foxA ( Figure 4A ) foxG , nk1 , or deadringer ( data not shown ) , which are expressed in the ectoderm starting at or after mesenchyme blastula . This suggests that these genes are indirect targets of Nodal signaling that cannot be induced during the short interval of the treatment . Interestingly , in the case of brachyury , a weak but consistent broadening of the ectodermal domain of expression was observed following treatment with Nodal . However , this effect was abolished by treatment with the protein synthesis inhibitor , consistent with this gene being an indirect target of Nodal signaling . Similarly , among all the dorsal marker genes we tested , 3 genes were strongly induced by treatments with BMP2/4 , even in the presence of protein synthesis inhibitors . These were tbx2/3 , nk2 . 2 and smad6 ( Figure 4B ) . Short treatments with high doses of BMP2/4 failed to induce expression of irxA , dlx , msx , atbf1 , hox7 , id , unc4 , oasis , wnt5 , admp2 or glypican 5 ( data not shown ) suggesting that these genes may be indirect targets of BMP signaling . The very good correlation between the results of this induction assay and the timing of expression of the downstream targets of Nodal and BMP2/4 indicates that this assay predicts with good confidence the direct , and probably also the indirect , target genes of these ligands at swimming blastula stage . It should be kept in mind however , that at later stages , this assay does not allow to rule-out completely the existence of a direct input from Nodal or BMP2/4 to downstream target genes . An alternative explanation for the fact that several genes appear to be refractory to induction by recombinant Nodal or BMP4 proteins is that after swimming blastula stage , the ventral and dorsal ectoderm may no longer be competent to switch their gene regulatory networks to a state that supports expression of dorsal or ventral genes respectively . We next attempted to determine if the activity of Nodal and BMP2/4 accounts for the restricted expression of all of the ventral and all the dorsal genes . Embryos were injected with a nodal morpholino and the expression of ventral , dorsal or ciliary band markers analyzed at successive stages ( Figure 5 ) . Expression of all the ventral marker genes that we tested including , bmp2/4 , goosecoid , fgfr1 , nk1 , chordin , brachyury , foxA and lefty disappeared in the Nodal morphants , consistent with previous results ( Figure 5B ) [13] , [29] , [38] . Injection of the nodal morpholino also largely prevented expression of foxG , confirming that this gene is induced downstream of Nodal signaling [29] . We also found that in Nodal morphants , the expression of all dorsal markers genes was strongly downregulated in most of the ectoderm , with responses falling into two categories: for some genes , e . g . glypican5 , oasis , msx , dlx , hox7 , wnt5 , smad6 , or unc4 , expression completely disappeared in the Nodal morphants ( Figure 5C ) . Others , e . g . tbx2/3 , id , irxA , nk2 . 2 , atbf1 , admp2 and 29D displayed residual expression in the vegetal-most ectoderm and/or in the PMCs indicating Nodal-independent expression of these genes in the presumptive dorsal vegetal ectoderm . A striking result was obtained when we analyzed the expression of ciliary band markers in the nodal morphants ( Figure 5D ) . The expression of most ciliary band markers dramatically expanded to most of the ectoderm following inhibition of Nodal signaling . This was the case for fgfA , bmp1 , univin , wnt8 , otx , pax2/5/8 , onecut/hnf6 , gfi1 , dri , as well as of the late ciliary band marker tubulinß3 and the ciliary band antigen 295 . Importantly , expression of Delta , which at pluteus stages identifies individual neurons of the facial ectoderm and ciliary band region [26] , [55] , was expanded to the whole ectoderm in Nodal morphants , strongly suggesting that most of the ectoderm is converted into neurogenic ectoderm in these embryos . Largely similar results were obtained using a pharmacological inhibitor of the Nodal receptor [62] ( Figure S4 ) . Taken together , these results show that Nodal signaling is essential for expression of all the ventral and of all the dorsal marker genes within the ectoderm . In the absence of Nodal , expression of all the ventral and dorsal marker genes is abolished and ciliary band genes are ectopically expressed throughout most of the ectoderm . We also examined the effect of knocking down BMP signaling on the expression of the ventral , dorsal and ciliary band markers ( Figure 6 ) . As expected , we found that expression of all the ventral markers that we tested was independent of BMP2/4 signaling: nodal , bmp2/4 , chordin , brachyury or foxA were expressed at similar levels and in similar domains in the controls and in the alk3/6 morphants ( Figure 6B ) . Removing BMP2/4 or Alk3/6 function affected the expression of dorsal marker genes in a way very similar to that caused by removing Nodal: expression of most genes including wnt5 , atbf1 , hox7 , msx , dlx , smad6 , tbx2/3 , unc4 was abolished while for irxA , nk2 . 2 and id , residual expression was still observed in the vegetal most ectoderm on the presumptive dorsal side ( Figure 6C ) . These results confirm that expression of all the dorsal ectodermal genes stringently relies on BMP2/4 signaling and that in the absence of Nodal or BMP2/4 signals , no other signals compensate for the lack of these inducers . Again , a striking result was observed when we analyzed the expression of ciliary band markers in the bmp2/4 or Alk3/6 morphants . For all of them , including gfi1 , onecut/hnf6 , otx , deadringer , pax2/5/8 , foxG , wnt8 , fgfA , univin , bmp1 and tubulinß3 , loss of BMP2/4 signaling caused a dramatic ectopic expression in the dorsal ectoderm ( Figure 6D ) . This ectopic expression transformed the normally bilateral expression domains of fgfA , pax2/5/8 , foxG , gfi1 , univin , and wnt8 into a horseshoe shaped domain covering the lateral and dorsal regions and caused the expression domain of deadringer and otx to become radial . These results reveal that in addition to promoting specification of dorsal cell fates , an essential function of BMP2/4 signaling is to repress ciliary band gene expression within the dorsal ectoderm . To establish the functional hierarchy between key ventral , dorsal and ciliary band genes , we designed morpholinos against 17 transcription factors and 8 signaling molecules expressed within the ectoderm with a restricted pattern along the dorsal-ventral axis . Among these 25 morpholinos , 19 ( alk4/5/7 , alk3/6 , brachyury , bmp2/4 , chordin , foxA , foxG , fgfA , goosecoid , irxA , lefty , tbx2/3 , dlx , msx , nodal , onecut/hnf6 , soxB1 , univin , wnt8 ) gave a clearly recognizable morphological phenotype ( Figure 5–9 ) . The expression of 15 transcription factors ( goosecoid , brachyury , foxA , nk1 , nk2 . 2 , tbx2/3 , msx , smad6 , hox7 , irxA , onecut , gfi1 , dri , pax2/5/8 , foxG ) and 8 signaling factors ( nodal , bmp2/4 , fgfA , chordin , wnt8 , univin , wnt5 , glypican5 ) was analyzed at different stages in the 17 morphant backgrounds while in the case of nodal and bmp2/4 morphants we analyzed the expression of an additional set of 17 marker genes ( Tables S1 , S2 ) . In addition , we overexpressed a subset of genes encoding transcription factors ( goosecoid , foxA , foxG , deadringer , nk2 . 2 , tbx2/3 , msx , smad6 ) and signaling molecules ( nodal , bmp2/4 , chordin ) and analyzed the expression of ventral , dorsal and ciliary band genes in these embryos . Since many of the genes identified in our screens including brachyury , foxA , otx , smad6 , tbx2/3 , wnt5 , oasis , univin , wnt8 , rkhd , ptb , fgfA , Delta are expressed not only in the ectoderm but also in the mesendoderm and since many other markers such as atbf1 , irxA , nk2 . 2 or egip , oasis , wnt5 , glypican5 , wnt8 , Delta , otx or bmp1 are expressed in more than one region and sometimes in both the ventral and dorsal ectoderm , we used in situ hybridization rather than QPCR to monitor the consequences of the perturbations . In situ hybridization is usually not used as the primary technique in large-scale projects such as gene regulatory network analysis since it is time and effort consuming and requires large numbers of injected embryos . However , we believe it is the only technique that provides the necessary spatial resolution to accurately analyze the expression of genes with complex expression patterns in perturbed embryos . Furthermore , when used with appropriate controls , in situ hybridization can provide a good estimate of the level of expression in perturbed embryos compared to controls . To provide a temporal view of the consequences of these perturbations and avoid secondary effects , the expression of the genes analyzed in response to nodal or bmp2/4 overexpression was examined at two different stages , soon after the onset of their restricted expression , and at a later stage , most often early or late gastrula stage depending on the gene analyzed . Information derived from these perturbations analyses was combined with earlier results to build a provisional gene regulatory network . The main features of this gene regulatory network are described below . Low levels of goosecoid transcripts are present maternally then their abundance increases sharply at swimming blastula stage , shortly after the peak of Nodal expression [35] ( Figure S2 ) . Expression of lefty , chordin , bmp2/4 , fgfr1 and goosecoid , was unchanged in the goosecoid morphants consistent with these genes being direct targets of Nodal signaling and with previous studies [38] ( Figure 7A and data not shown ) . Interestingly , at gastrula stages , strong ectopic expression of wnt8 , univin and foxG was detected in the ventral ectoderm of goosecoid morphants indicating that one function of Goosecoid is to repress expression of these three genes in the ventral ectoderm between blastula and gastrula stages . In contrast , ectodermal expression of foxA and brachyury , two likely indirect targets of Nodal required for mouth formation , was lost in the goosecoid morphants , consistent with the lack of stomodeum in these embryos ( Figure 7A ) [42] . Reciprocally , overexpression of goosecoid caused a dramatic expansion of foxA and brachyury ( Figure 7B ) . Therefore , in the sea urchin as in vertebrates , brachyury and foxA are targets of Nodal signaling but unlike in vertebrates , in the sea urchin , they are not primary targets of Nodal since their expression depends on the zygotic expression of goosecoid [63]–[65] . Overexpression of goosecoid also expanded the expression of deadringer as reported previously by Bradham et al . [22] , [66] . In contrast , the two dorsal marker genes hox7 and msx failed to be expressed in the goosecoid overexpressing embryos consistent with previous studies showing that goosecoid overexpression suppresses expression of dorsal genes such as tbx2/3 and spec1 [35] , [40] . Overexpression of goosecoid also abolished the expression of all the other ciliary band genes that we tested including wnt8 , univin , foxG , egip , gfi1 and onecut/hnf6 . Taken together these observations suggest that goosecoid plays a double function , first by allowing expression of stomodeal genes such as foxA and brachyury and second by suppressing the expression of ciliary band and dorsal genes . Once goosecoid and foxA have been turned on , Brachyury and FoxA cross regulate each other so that brachyury maintains foxA expression while foxA promotes brachyury expression ( Figure 7C , 7D ) . When the function of either of the two genes was blocked with a morpholino , expression of the other gene was lost and the resulting embryos developed without a stomodeum . The role of these cross regulatory interactions between brachyury and foxA may be to stabilize and lock the specification of the ventral ectoderm that has been initiated by Nodal as described in the endomesoderm GRN , for example between the transcription factors hex and tgif [7] . Inhibition of tbx2/3 function strongly perturbed establishment of dorsal-ventral polarity resulting in embryos with a rounded shape , which lacked ventral arms and had a strongly reduced dorsal region ( Figure 8A ) . Molecular analysis revealed that ventral markers such as chordin , foxA or brachyury were expressed in tbx2/3 morphants , albeit with reduced levels compared to controls ( Figure 8A ) . A similar slight reduction was observed for the ciliary band markers onecut/hnf6 , fgfA and pax2/5/8 . In contrast , inhibition of tbx2/3 function abolished the expression of several dorsal genes encoding transcription factors including msx , dlx , irxA and atbf1 while the expression of other genes such as smad6 , glypican5 , oasis and wnt5 appeared unaffected . These results identify tbx2/3 as a key regulator of dorsal gene expression downstream of BMP2/4 . Since loss of BMP2/4 or Alk3/6 signaling causes ectopic expression of ciliary band genes in the dorsal ectoderm , it follows that in unperturbed embryos , a transcriptional repressor must act in the dorsal ectoderm downstream of BMP2/4 to prevent expression of ciliary band genes . Of the four transcription factors expressed in the dorsal ectoderm that we tested , only in the case of one of them did we observe robust ectopic expression of a ciliary band gene . This gene is irxA . In embryos injected with morpholinos against the irxA transcript , onecut/hnf6 expression was strikingly expanded in the dorsal ectoderm ( Figure 8B ) . This effect was very robust and the territory in which the ectopic expression of onecut/hnf6 was observed was congruent with the expression territory of irxA . Interestingly , a small number of embryos injected with irxA morpholinos later developed with a thickened ectodermal region on the dorsal side that resembled an ectopic ciliary band ( Figure 8B ) . This suggests that IrxA is a repressor of ciliary band genes downstream of BMP2/4 . onecut/hnf6 is of one of the earliest marker genes expressed in the presumptive ciliary band . onecut/hnf6 morphants developed with a slightly reduced D/V axis but they clearly displayed a D/V polarity and a well-developed ciliary band ( Figure 8C ) . Nevertheless , we found that the expression of several marker genes of the ciliary band was affected in the onecut/hnf6 morphants . A reduced level of expression in the onecut/hnf6 morphants was observed in the case of pax2/5/8 , foxG and dri while in the case of gfi1 , no expression was detected . onecut/hnf6 is thus an upstream regulator of gfi1 . Gfi proteins are conserved in C . elegans ( Pag3 ) , Drosophila ( Senseless ) and mice ( Gfi1 ) . In all three species , these zinc finger proteins play conserved roles in neural development [67] . Mice mutant for gfi1 are deaf and ataxic while flies mutant for senseless lack sensory organs indicating that Gfi proteins regulate sensory organ development [67] , [68] . One can therefore anticipate that Gfi1 likely plays a role in neural development in the sea urchin embryo as it does in vertebrates and in flies . Since gfi1 is downstream of onecut , the ciliary band network therefore appears to be composed of at least two layers of zygotic factors . In this study , taking advantage of the detailed phenotypic analyses and robust in situ hybridization procedures available in Paracentrotus lividus , we analyzed with a high level of spatial resolution the expression , the regulation and the function of most of the zygotic transcription factors and signaling molecules displaying restricted expression within the ectoderm of the sea urchin embryo . This analysis allowed us to assemble a gene regulatory network , the D/V GRN , which describes the regulatory interactions between these genes and provides a framework for understanding the developmental program responsible for patterning the embryo along the dorsal-ventral axis . Several interesting conclusions emerged from the resultant GRN . First , it provides a clear demonstration that the activities of Nodal and BMP2/4 account fully for the spatially restricted expression of all the known genes of this network: Nodal controls the expression of all the genes expressed specifically in the ventral ectoderm , and through BMP2/4 , the expression of all the genes expressed specifically in the dorsal ectoderm . Both overexpression of these ligands and corresponding loss of function experiments produced very strong , all or none , effects consistent with the idea that Nodal and BMP2/4 are critical inputs that drive the D/V GRN . It should be noted that despite their essential roles , Nodal and BMP2/4 are certainly not the only ligands involved in D/V patterning of the ectoderm and other ligands more broadly expressed likely cooperate with Nodal and BMP2/4 to specify the ventral and dorsal regions . In particular , Nodal may bind to its receptor as a heterodimer with Univin , a GDF1/Vg1 ortholog , as shown in other models [24] , [25] while BMP2/4 may heterodimerize with BMP5/8 to specify the dorsal ectoderm as shown in vertebrates and in Drosophila [69] , [70] . Nevertheless , the key roles played by Nodal in this GRN together with the essential function of Nodal factors in D/V axis formation in vertebrates and basal chordates [71] reinforce the hypothesis that an ancestral function of Nodal may have been in the regulation of D/V axis formation in deuterostomes . A second key conclusion emerging from our D/V GRN is that in the sea urchin , Goosecoid is a key upstream element of a small regulatory circuit that controls mouth formation . In vertebrates ectopic expression of goosecoid promotes cell migration and induces incomplete secondary axes while loss of function studies implicate goosecoid in the function of the Spemann organizer and head formation [72] . The function of goosecoid during development of other deuterostome embryos has not been studied . In the sea urchin , previous studies reported that both overexpression and loss of function of goosecoid strongly perturbed establishment of the dorsal-ventral axis , however the target genes of goosecoid were not known and the role of this repressor within the ventral ectoderm remained largely unclear [35] , [38] , [40] . Our finding that goosecoid is a direct target of Nodal signaling strongly suggested that this gene could play a key role in specification of the ventral ectoderm downstream of Nodal . We have shown that Goosecoid likely regulates the expression of deadringer and foxG in the ventral ectoderm . Furthermore , we demonstrated that Goosecoid plays a critical role in mouth formation by regulating downstream target genes such as the stomodeal genes brachyury and foxA . This raises the possibility that an ancestral function of goosecoid may have been in the regulation of stomodeum formation . Consistent with this idea , goosecoid is expressed in the stomodeal region in both protostomes and deuterostomes and is co-expressed with brachyury and foxA in the oral region of cnidarians [73] . Since Goosecoid is a transcriptional repressor [74] , this suggests that zygotic goosecoid activates foxA and brachyury by repressing the expression of a transcriptional repressor , the identity of which is presently unknown ( Figure 10 ) . Similar double repression mechanisms have been described in different GRNs . For example , in the sea urchin the skeletogenic mesoderm GRN , the repressor pMar has been proposed to repress hes-C as well as unidentified repressors to allow expression of genes specific of the PMC lineage [75] , [76] . Similarly Schnurri , represses the expression of brinker to allow the expression of Dpp target genes in Drosophila imaginal discs [77] . One candidate for a repressor acting downstream of goosecoid is the transcriptional repressor ZEB1/Smad Interacting Protein 1 ( Sip1 ) [78] . In the sea urchin embryo , Sip1 is expressed early in the presumptive ectoderm and its expression is downregulated at blastula stage , coincident with the onset of goosecoid expression [31] ( see Figure S2 and S5 ) . Experiments are currently being carried out in different labs to test this hypothesis . Another important function of Goosecoid appears to be in the repression of ciliary band and dorsal genes . Overexpression of goosecoid potently repressed expression of ciliary band markers . Furthermore , knockdown of Goosecoid function caused ectopic expression of univin , wnt8 and foxG in the ventral ectoderm . However , additional repressors likely cooperate with Goosecoid in this repression since inhibition of goosecoid function , unlike inhibition of irxA on the dorsal side , was not sufficient to derepress ciliary band markers genes such as onecut within the ventral ectoderm . Tbx2/3 has a special status amongst dorsal genes since it is one of the earliest zygotic genes expressed on the presumptive dorsal side [40] , [41] . Previous studies had shown that tbx2/3 is expressed dynamically in a broad dorsal territory in all three germ layers and that its expression is regulated by BMP signaling [13] , [26] , [40] , [41] . Indeed we showed that tbx2/3 is a direct target of BMP2/4 signaling in the ectoderm and that its function is required for expression of several dorsally expressed transcription factors such as msx , dlx , irxA and atbf1 . Intriguingly , previous studies in Paracentrotus failed to detect any D/V polarity defect in tbx2/3 morphants [40] . In contrast , we found that tbx2/3 is essential for D/V axis formation in this species . The reasons for this discrepancy are unclear . Interestingly , in vertebrates , tbx2 is also a target of BMP4 signaling during D/V patterning of the optic cup [79] . Similarly , in hemichordates , which are positioned phylogenetically as the sister phylum of echinoderms , tbx2/3 is a target of BMP2/4 suggesting that key genes that drive the D/V GRN are conserved in these two closely related phyla [80] . In vertebrates , tbx2 and tbx3 , unlike brachyury , which is a transcriptional activator , act as transcriptional repressors due to the presence of a strong repressor domain in their C-terminal region [81] , [82] . It is therefore possible that the sea urchin Tbx2/3 protein also functions as a transcriptional repressor and that , like Goosecoid , it stimulates gene expression by relieving the repressive action of a transcriptional repressor . The identity of this hypothetical transcriptional repressor is presently unknown . One of the most important findings of this study is the identification of irxA as a gene which acts downstream of BMP signaling to repress the ciliary band gene onecut . We previously reported that inhibition of BMP2/4 or Alk3/6 function causes an expansion of the presumptive ciliary band territory towards the dorsal side , and that this expansion is accompanied by the ectopic expression of the neural gene onecut/hnf6 [26] . On the basis of this result we anticipated that one function of the BMP pathway in the dorsal ectoderm was to repress ciliary band gene expression and we postulated the existence of a BMP2/4 dependent repressor of ciliary band genes . We have now identified IrxA as one such repressor based on the following evidence . First , we showed that irxA expression is regulated by BMP2/4 signaling . Second , we showed that blocking irxA translation with morpholinos caused a robust ectopic expression of onecut in a sector of the dorsal ectoderm that coincides with the expression domain of irxA . Finally , it is established that Irx proteins can function as repressors by recruiting the Groucho Co-repressor [83] , [84] . Since irxA is downstream of tbx2/3 in the GRN , we might predict that blocking tbx2/3 function should also result in ectopic expression of ciliary band genes . Surprisingly , we never observed ectopic expression of ciliary band marker genes in tbx2/3 morphants . This observation is consistent with previous GRN studies , which reported that direct target genes are more strongly affected than indirect target genes or in other words , that when a perturbation affects the driver gene , it causes stronger effects on target genes than when the perturbation affects genes further upstream in the pathway [29] . However , the simplest explanation is that our tbx2/3 morpholino may not be completely effective and that residual irxA expression may prevent ectopic expression of onecut in these embryos . In vertebrates and in Drosophila , irx genes are involved in neural development [85] . In Xenopus for example , irx1 promotes neural development by repressing bmp4 expression in the neural plate . It was therefore surprising to find that in the sea urchin embryo , irxA acts downstream of BMP2/4 to negatively regulate neural marker genes . Nevertheless , the identification of irxA as a BMP2/4 dependent repressor of ciliary band gene expression strongly supports our proposal that the default state of the ectoderm in the absence of TGF beta signaling is the ciliary band and that the ectoderm is patterned by two successive inductive events that repress the ciliary band fate on the ventral and dorsal sides . The results obtained in this study largely support this idea that the default state of the ectoderm in the absence of Nodal and BMP signaling is a ciliary band-like ectoderm that expresses a number of neural genes and that Nodal and BMP2/4 restrict this ciliary band fate by specifying the ventral and dorsal ectoderm . The first hint that the default state of the ectoderm in the absence of TGF beta signaling is the ciliary band is that several genes whose expression is later restricted to the ciliary band territory are expressed throughout the ectoderm at earlier stages . This is for example apparent for fgfA , univin and wnt8 , which are expressed in a belt of cells that includes most of the presumptive ectoderm at blastula stages . The expression of fgfA , univin and wnt8 is subsequently repressed on the ventral and dorsal sides during gastrulation thereby restricting the expression of these genes to the ciliary band domain . Several additional lines of evidence support the idea that the default state of the ectoderm in the absence of TGF beta signaling is a ciliary band and neural fate and that alternative ectodermal fates must be induced by active signaling . First , overexpression of both nodal and bmp2/4 strongly antagonized the expression of ciliary band and neural markers such as onecut , foxG and gfi1 , with bmp2/4 leading to a very potent inhibition of ciliary band formation . Second , in the lefty morphants the ciliary band failed to form while in the absence of Nodal and BMP2/4 signaling , the ventral and dorsal ectodermal regions were not specified and most of the ectoderm differentiated instead into a thickened ciliated ectoderm that resembled the ciliary band ectoderm and expressed all tested ciliary band markers . These ciliary band markers were de-repressed throughout the ventral and dorsal ectoderm in the nodal morphants while in the absence of BMP2/4 , which acts as a dorsal inducer , or of alk3/6 , which is required to transduce BMP2/4 signals , only specification of the dorsal ectoderm was perturbed and ectopic expression of these ciliary band genes was detected only on the dorsal side . A third argument is that the presumptive ciliary band territory is also a region in which fgfA and vegf are expressed and where MAP kinase activity is high [48] , [56] , [94] . Studies in vertebrates have shown that the activity of the MAP kinase ERK inhibits both BMP signaling and neuralization by phosphorylating Smad1 in the linker region thereby preventing its nuclearization . We thus predict that during normal development of the sea urchin embryo , the high MAP kinase activity present in the lateral ectoderm promotes neural fates within the presumptive ciliary band by inhibiting the activity of pSMAD1/5/8 and pSMAD2/3 . Thus , in the absence of Nodal and BMP signaling , signals such as FGFA that are normally present at the level of the lateral ectoderm are ectopically expressed in the ventral and dorsal regions where they may promote ectopic neuron formation [26] , [27] . One last but crucial argument that supports our model of the ciliary band as a default state of the ectoderm in the absence of TGF beta signaling is that we identified irxA and possibly Goosecoid as repressors of a subset of ciliary band genes downstream of Nodal or BMP signaling . One read-out of Nodal and BMP2/4 signaling therefore appears to be active repression of the ciliary band fate as we had predicted [26] . Yaguchi and colleagues previously demonstrated that in the absence of Wnt signaling , most of the ectoderm differentiates as a neurogenic ectoderm that expresses markers of the animal pole [27] . Since many ciliary band genes are also expressed in the animal pole , it could be argued that the ectopic expression of ciliary band marker genes observed following inhibition of Nodal or BMP signaling also reflects an expansion of the animal pole domain . This can be ruled out for several reasons . First , we showed that the expression of animal pole markers such as foxQ2 , is unaffected in Nodal morphants or in embryos treated with a pharmacological inhibitor of the Nodal receptor . Second , Yaguchi et al . showed that the number and location of serotonergic neurons of the apical organ are unaffected by inhibition of Nodal signaling . Importantly , we showed that pax2/5/8 , which is expressed in the vegetal part of the ciliary band but not in the animal pole region behaved exactly like the other ciliary band marker genes and was strongly derepressed in the ventral and dorsal ectoderm of Nodal morphants . Taken together these observations indicate that the lateral ectoderm of the prospective ciliary band , not the animal pole domain , is expanded in the Nodal morphants . Our study suggests that specification of the ciliary band is likely initiated by a combination of maternal factors such as SoxB1 and by zygotic factors such as FGFA , Otx and Onecut/Hnf6 whose expression is initiated independently of the Nodal and BMP2/4 signals ( Figure 10 ) . These zygotic genes initially show a broad expression in the ectoderm , which then becomes restricted to the presumptive ciliary band by the activity of transcriptional repressors such as Goosecoid and IrxA expressed in the ventral or dorsal ectoderm downstream of Nodal or BMP2/4 . Collectively our results suggest that the neural ectoderm of the ciliary band forms in a territory that is devoid of Nodal and BMP2/4 signaling ( Figure 11 ) . On the dorsal side , inhibition of BMP signaling appears to be sufficient to trigger formation of the ciliary band as was observed in BMP2/4 or Alk3/6 morphants or in embryos injected with low doses of smad6 mRNA . Similarly , on the ventral side , inhibition of Nodal signaling is sufficient to initiate formation of a ciliary band since BMP signaling does not occur on the ventral side but on the dorsal side [26] . In this case , ectopic neural differentiation likely results from inhibition of ventral differentiation . This highlights that , in the sea urchin ectoderm , preventing ventral cells to differentiate downstream of Nodal signaling promotes neural differentiation just as efficiently as inhibiting BMP signaling on the dorsal side . Similarly , in zebrafish embryos , inhibition of Nodal signaling causes the transfating of prospective mesendodermal cells into neural cells [95] , [96] and in the mouse , lack of Nodal signaling causes precocious neural differentiation [97] . Therefore , in the sea urchin embryo like in vertebrate embryo models , neural differentiation can result both from inhibition of BMP signals as well as from inhibition of other signals that regulate the fate of early blastomeres and allocate cells to embryonic territories and germ layers . In summary , our results show that in the sea urchin embryo , the neurogenic territory of the ciliary band is not induced by an interaction between the ventral and dorsal territories as previously suggested [98] , but that it represents the default state of the ectoderm in the absence of Nodal and BMP signaling . Nodal and BMP2/4 may therefore be regarded as factors that are required to prevent premature differentiation of ectodermal cells into neural cells as much as factors that are required for specification of the ventral and dorsal ectoderm . Another recent GRN analysis of ectoderm specification in S . purpuratus was performed using nanostring technology [29] . A comparison of the architecture of the gene regulatory networks derived from this study and ours reveals the expected similarities but also some major differences . A common central element in the architecture of both networks is the critical dependence of dorsal genes on non-autonomous signaling by BMP2/4 , a feature already proposed previously [13] . Another point of convergence is that both studies pointed to goosecoid and tbx2/3 as important early zygotic genes downstream of Nodal and BMP2/4: both studies identified brachyury as a downstream target of Goosecoid , and dlx and irxA as downstream targets of Tbx2/3 . Finally , both studies identified foxG and deadringer as downstream targets of Nodal . The first important difference in the architecture of the two proposed networks is that whereas our study defines the default state of the ectoderm in the absence of Nodal and BMP signals as a ciliary band-like ectoderm , the network proposed by Su et al . largely ignores formation of the ciliary band . Another important difference between the two studies concerns the dependence of ventral genes on Nodal . Su et al . argued that only part of the oral ectoderm specification system is downstream of Nodal [29] . According to the authors , a number of regionally expressed genes including onecut/hnf6 , otx2 , lim1 , and foxA , are activated “specifically in the oral ectoderm…exactly the same with or without nodal” , leading them to speculate that hypothetical Nodal independent early oral ectoderm signals regulate these genes in the ventral ectoderm . We do not agree with this interpretation , since from our in situ analysis , it is clear that these genes cannot be considered as oral-specific markers . Furthermore , we showed that the expression of onecut/hnf6 , otx2 , lim1 , and foxA in the presumptive ectoderm region of Nodal morphants was not regionalized , consistent with the absence of any oral territory in these embryos . The expression of onecut/hnf6 and otx2 is first initiated in a territory much larger than the ventral ectoderm , before subsequently becoming restricted to either to the ciliary band ( onecut/hnf6 ) or to a broader territory that also includes the ventral ectoderm ( otx2 ) . We thus interpret the continued expression of onecut/hnf6 and otx2 in the ectoderm as reflecting adoption of a ciliary band character by the entire ectoderm . Concerning foxA , the nodal-independent detection of the mRNA reported by Su et al is undoubtedly due to the abundant expression of this gene in a distinct endodermal territory , which , unlike the oral ectoderm expression , is largely Nodal-independent . The foxA example highlights the importance of using methods that allow spatial resolution to analyze the expression of genes with complex expression patterns in epistasis experiments . According to Otim and colleagues and Su and colleagues , two genes , onecut/hnf6 and deadringer , play essential roles in the DV GRN . Using an “unconventional morpholino” that targeted a sequence 660 bp downstream of the first ATG but that did not target a splice junction , Otim et al . reported that “inhibition” of hnf6/onecut function eliminated D/V polarity and caused a radialized phenotype that strikingly resembled the Nodal loss of function . Using the same reagent , Su et al . expanded this analysis and further argued that a positive regulatory input from onecut/hnf6 is required for the expression of several key regulators such as nodal , goosecoid , lefty , chordin , and bmp2/4 [29] , [46] . These results are highly surprising since morpholinos are predicted to be ineffective at blocking translation when they target sequences after the first 25 bases following the initiator ATG [99] , [100] . Using two different and more conventional morpholinos targeting the 5′ leader or the translation start site of the P . lividus hnf6/onecut transcript , we were unable to reproduce either the striking hnf6/onecut morphant phenotypes originally reported by Otim and colleagues or the effects on nodal , goosecoid , lefty , chordin , and bmp2/4 reported by Su and colleagues . It is therefore very unlikely that onecut/hnf6 , which is expressed only transiently within the ventral ectoderm , plays the crucial role proposed by these authors in this gene regulatory network . Regarding deadringer , Su et al . found that deadringer morphants display a much reduced expression of ventral genes such as goosecoid , NK1 and hes as well as a strongly reduced expression of dorsal genes such as irx , nk2 . 2 and tbx2 . 3 . Again , these results are surprising since the published cDNA sequence of deadringer used by Su et al . to design their morpholino as well as the associated predictions of the translation start site of the protein are probably incorrect and correspond to a truncated protein sequence as suggested by our sequence analysis of the genomic S . purpuratus deadringer locus and the analysis of the deadringer cDNAs in Paracentrotus ( Figure S1 ) . In addition , using two different morpholinos against the P . lividus deadringer transcript , we were unable to reproduce the published drastic effects of deadringer morpholinos on the expression of ventral and dorsal marker genes . It is therefore also unlikely that deadringer plays the role that it had been previously attributed in the S . purpuratus GRN . Finally , it has been argued that specific aboral differentiation genes such as CyIIIa and spec1 are transcriptionally activated in the aboral ectoderm long before late blastula and that this implied the existence of an early asymmetry in the aboral ectoderm that affected transcriptional activity . Su et al . postulated that this asymmetry may be a redox gradient that would directly regulate the transcriptional activity of aboral genes such as CyIIIa and tbx2/3 . Our results oppose this view . In Paracentrotus , the ectodermal expression of tbx2/3 is essentially lost following inhibition of Nodal or BMP2/4 signaling . While it is true that a residual tbx2/3 expression is observed in the Nodal morphants at gastrula stage , this expression is restricted to the vegetal most regions and therefore likely reflects the response of this gene to signals that act along the animal-vegetal axis rather than response to a redox gradient along the D/V axis . Furthermore , in Paracentrotus , expression of CyIII genes is first ubiquitous and only becomes restricted to the dorsal ectoderm at mesenchyme blastula stage ( see Figure S5 ) , coinciding with the nuclear translocation of pSmad1/5/8 in dorsal cells . In other words , we never observed any marker gene that was expressed specifically in the dorsal ectoderm before the onset of BMP signaling i . e . at late blastula stage . Our observations therefore do not support the view that the asymmetrical CyIIIa or tbx2/3 expression is driven by an early red-ox gradient , at least not in Paracentrotus , but suggest that their expression is more likely driven by differential Nodal and BMP signaling along the dorsal-ventral axis . A comparison of the mechanisms of neural induction in different species reveals both similarities and divergences regarding the signaling pathways involved . In Xenopus , inhibition of both Nodal and BMP signaling appears to be essential for neural induction , although FGF signaling is likely implicated in the early steps of this process [101] , [102] . Similarly , in mammals , both Nodal and BMP signaling have been involved in neural differentiation , the strongest evidence being that most epibast cells of mouse embryos mutant for nodal or bmpr1 display widespread and precocious expression of anterior neural markers [97] , [103] . In the chick and in zebrafish , there is strong evidence that FGF signaling regulates neural induction partly through the regulation of expression of BMP ligands and of BMP antagonists [104] , [105] , [106] . In contrast in ascidians , which are basally branching but divergent chordates , FGF signals are the key players in neural induction by directly regulating the expression of neural markers such as otx [107]–[109] . Inhibition of BMP signaling does not appear to play a role in this process [110] while Nodal plays a distinct , inductive role in patterning of the neural plate [111] . Similarly , in hemichordates , which together with the echinoderms form a sister group of the chordates and have a diffuse neural system , BMP signaling does not appear to play a role in the choice between neural and epidermis [80] . Our experiments in the sea urchin embryo show that inhibition of Nodal and BMP signaling is central to neural induction in echinoderms and that in the absence of Nodal or BMP signaling , most cells of the ectoderm differentiate into a neurogenic ectoderm . Since BMP signaling also regulates neural differentiation in insects [112] and annelids [113] , it appears likely that inhibition of Nodal and BMP signaling may have been an ancestral mechanism to specify neural cells not only in deuterostomes but also perhaps in bilateria , and thus that the neural specification mechanisms used in ascidians and hemichordates have diverged during evolution . Although in the sea urchin inhibition of Nodal causes the ventral ectoderm to adopt ultimately a neurogenic ectodermal fate , it should be kept in mind that our experiments also suggest that Nodal may have an early and positive role in specification and/or patterning of the neurogenic territory of the ciliary band since we showed that Nodal promotes the expression of Delta in a subpopulation of ciliary band cells and drives the early expression of the neural gene foxG . Therefore , in the sea urchin as in chordates , in addition to its general inhibitory role on neural induction , Nodal may also play a positive role in specification and/or patterning of the neural territory [111] , [114] , [115] . In conclusion , this large scale , systematic GRN analysis has allowed us to identify a number of key gene regulatory interactions and to build a provisional gene regulatory network describing specification of the three main ectodermal territories of the sea urchin embryo . It has not only uncovered key and probably ancient regulatory sub circuits that drive morphogenesis of the ectoderm , but has also allowed us to propose a new model of how specific regions of the ectoderm are induced over a default state , and of how the ectoderm is patterned by successive rounds of induction by TGF beta ligands . This relatively simple model captures most of the results derived from the functional analyses of Nodal and BMP2/4 in the sea urchin embryo and provides testable predictions for futures studies . Finally , our study illustrates the power of the GRN based approaches which can provide a global perspective on a set of genes regulating a biological process , explaining how this process works and what happens when it fails . Adults sea urchins ( Paracentrotus lividus ) were collected in the bay of Villefranche-sur-Mer . Embryos were cultured as described previously [116] , [117] . When required , fertilization envelopes were removed by adding 2mM 3-amino-1 , 2 , 4 triazole 1 min before insemination to prevent hardening of this envelope followed by filtration through a 75µm nylon net . SB431542 ( 10 µM in sea water ) was diluted from stocks solutions in DMSO , and embryos incubated in 24 well plates protected from light . In controls experiments , DMSO was added at 0 . 1% final concentration . NiCl2 was used at 0 . 5 mM . SB431542 and nickel treatments were performed continuously starting 30 min after fertilization . Continuous treatments with recombinant mouse Nodal ( 1µg/ml ) and BMP4 proteins ( 0 . 5 µg/ml ) ( R&D ) started at the 16-cell-stage and used embryos lacking the fertilization envelope . We verified with a set of 10 genes that RNA overexpression and recombinant proteins produced equivalent effects for both Nodal and BMP . To determine if marker genes are direct or indirect targets of Nodal or BMP4 signaling , embryos at the swimming blastula/late blastula , early mesenchyme blastula stage or at gastrula stage from which the fertilization envelope had been removed were treated for 2h with recombinant proteins in the presence or absence of protein synthesis inhibitors . To block protein synthesis , puromycin or emetine was added at a final concentration of 360µM ( 200µg/ml ) or 5µM ( 10µg/ml ) respectively using stock solutions prepared in DMSO . In control experiments embryos were treated with 0 . 1% DMSO or with Puromycin at 200µg/ml or emetine at 10µg/ml . Development of the treated embryos was usually arrested 30 min after addition of the inhibitor , an indication of the effectiveness of the reagent and after 3–4h , all the treated embryos underwent a massive and brutal apoptosis , an effect characteristic of treatments with protein synthesis inhibitors . In the case of nodal , bmp2/4 , lefty , goosecoid , fgfr1 , chordin , nk2 . 2 , tbx2 . 3 , treatments were performed at the swimming blastula stage . In the case of nk1 , foxA , brachyury , foxG , dlx , hox7 , id , irxA , glypican5 , cyIIIa , admp2 , smad6 and msx , treatments were performed at the early mesenchyme blastula sage . In the case of deadringer , atbf1 , msx , wnt5 , irxA and dlx treatments were also performed at gastrula stage . Short treatments with Nodal or BMP4 failed to induce ectopic expression of any marker gene at gastrula stage suggesting that most of the genes expressed at this stage are indirect targets of Nodal and BMP2/4 or alternatively that at this stage , ectodermal territories are resistant to respecification by exogenous Nodal or BMP4 . Most of the genes analyzed in this study were discovered in the course of a random in situ hybridization screen using cDNA libraries from various stages ( T . Lepage unpublished ) . Additional marker genes were discovered in a second in situ screen aimed at analyzing the expression profiles of all the transcription factors and signaling molecules expressed during early sea urchin development [30] using a Paracentrotus lividus EST library ( http://goblet . molgen . mpg . de/cgi-bin/webapps/paracentrotus . cgi ) . When the isolated clones were incomplete , full-length cDNA sequences were obtained either by screening cDNA libraries with conventional methods and sequencing the corresponding clones . In certain cases , 5′RACE was performed using the Smart RACE kit ( Clontech ) to obtain the 5′ sequences . A list of all the Paracentrotus transcripts analyzed in this study with a summary of their temporal and spatial expression patterns is provided in Table 1 together with the corresponding accession numbers and original references describing these genes . Note that in the case of deadringer , the sequence of the Paracentrotus lividus clones diverged significantly from the published Strongylocentrotus purpuratus sequence . The published S . purpuratus deadringer transcript is predicted to encode a 490 amino acid protein . However , all the 13 independent deadringer cDNA clones that we sequenced encoded a protein 100 amino acids longer on the N-terminal side . Furthermore , translation of the S . purpuratus genomic sequence upstream of the predicted first ATG revealed the presence of a much longer open reading frame compared to the published deadringer protein sequence that encoded a protein highly similar to the deduced protein sequence from Paracentrotus ( see Figure S1 ) . This indicates that the previously published deadringer mRNA sequence was probably incorrect on the 5′ end and that the predicted deadringer protein sequence deduced from this mRNA was truncated . Since morpholinos fail to block translation when their target sequence is located after the first 25 bp following the initiator ATG [100] , the conclusions derived from previous functional studies of deadringer in S . purpuratus , which relied on a truncated sequence , are probably erroneous . For each gene of the network , a detailed analysis of the expression pattern was performed using digoxygenin labeled probes and in some cases , the temporal expression was analyzed by Northern blotting to verify maternal expression and to determine the exact onset of zygotic gene expression ( Figure S2 ) . In situ hybridization was performed following a protocol adapted from Harland [118] with antisense RNA probes and staged embryos . For marker genes expressed in ventral or dorsal territories at early stages , and for genes with complex expression profiles , double in situ hybridization was performed to confirm the orientations of the expression pattern . In this case , the two probes were hybridized and developed simultaneously . Probes derived from pBluescript vectors were synthesized with T7 RNA polymerase after linearization of the plasmids by NotI , while probes derived from pSport were synthesized with SP6 polymerase after linearization with SfiI . Control and experimental embryos were developed for the same time in the same experiments . Two color in situ hybridization was used following the procedure of Thisse et al . [119] . For overexpression studies the coding sequence of the genes analyzed was amplified by PCR with the Pfx DNA polymerase ( Invitrogen ) using oligonucleotides containing restriction sites and cloned into pCS2 [120] . Capped mRNAs were synthesized from NotI-linearized templates using mMessage mMachine kit ( Ambion ) . After synthesis , capped RNAs were purified on Sephadex G50 columns and quantitated by spectrophotometry . RNAs were mixed with Tetramethyl Rhodamine Dextran ( 10000 MW ) or Texas Red Dextran ( 70000 MW ) or Fluoresceinated Dextran ( 70000 MW ) at 5 mg/ml and injected in the concentration range 100–800µg/ml . The nodal , bmp2/4 , fgfA , univin , alk3/6QD , and chordin pCS2 constructs have been described in Duboc et al . ( 2004 ) , Röttinger et al . ( 2008 ) , Range et al . ( 2007 ) and Lapraz et al . ( 2009 ) . The pCS2 goosecoid construct is described in [40] . RNA derived from the following additional constructs were made ( the cloning sites are indicated in parenthesis ) : pCS2foxA ( ClaI-XbaI ) ; pCS2deadringer ( EcoRI-XhoI ) ; pCS2foxG ( ClaI-XhoI ) ; pCs2smad6 ( EcoRI-XbaI ) ; pCS2pax2/5/8 ( BamHI-XhoI ) ; pCS2tbx2/3 ( BamHI-XhoI ) ; pCS2msx ( BamHI-XhoI ) ; pCS2nk2 . 2 ( BamHI-XhoI ) . Morpholino antisense oligonucleotides were obtained from GeneTools LLC ( Eugene , OR ) . The nodal , BMP2/4 , Alk4/5/7 , Alk3/6 , univin , lefty and soxB1 morpholinos are described in [12]–[14] , [26] . Since morpholinos can have side effects or display toxicity or produce variable reductions in gene activity [121] , we designed and tested several morpholinos for each gene . A pair of morpholinos that did not display toxic effects was selected for further use ( a morpholino was considered toxic if it caused developmental arrest during cleavage or a massive cell death at the onset of gastrulation when injected at low doses ( 0 . 1–0 . 3 mM ) ) . In the cases of nodal , bmp2/4 , alk3/6 , Alk4/5/7 , univin and soxB1 , the efficiency of the morpholino to downregulate the expression of previously characterized targets genes was systematically assessed in control experiments [13] , [14] , [26] . The phenotypes observed for nodal , bmp2/4 , brachyury chordin , foxA , fgfA , goosecoid , irxA , lefty , tbx2/3 , dlx , msx , onecut/hnf6 , soxB1 , univin , wnt8 morpholinos were considered specific since they were confirmed with a separate , non-overlapping morpholino . In the case of alk3/6 , alk4/5/7 and nodal , a rescue experiment had previously been performed demonstrating the specificity of these reagents [13] , [14] , [26] . The phenotypes observed were always consistent with the zygotic expression pattern of the targeted genes and with previous well-established functional data [13] , [14] , [26] , [35] , [42] , [122] . We did not observe inconsistent phenotypes among several knockdowns except in one case , in which knocking down Tbx2/3 , an upstream regulatory gene of irxA , did not cause the same effect on the IrxA target gene onecut/hnf6 as knocking down irxA itself suggesting that the tbx2/3 morphant phenotype is a hypomorphic phenotype and not a null . In the case of the ventrally expressed genes nodal and bmp2/4 , we observed strong non autonomous effects consistent with the demonstrated translocation of BMP2/4 from the ventral to the dorsal ectoderm and with the role of BMP2/4 as relay downstream of Nodal [26] . In contrast , we never observed strong effects on the expression of ventral markers by morpholinos targeting genes expressed dorsally . In three cases , ( dlx , msx , foxG ) a morphological phenotype was consistently observed but molecular analysis failed to detect significant perturbations in the expression of the genes analyzed . Other morpholinos pairs ( deadringer , hox7 , nk2 . 2 , oasis , wnt5 ) gave very weak or not always reproducible phenotypes . Molecular analysis on embryos injected with these morpholinos failed to detect significant and reproducible changes in gene expression in any of the ventral , dorsal or ciliary band markers genes that we tested . In a few cases , ( atbf1 , klf2/4 ) all the morpholinos synthesized were highly toxic and were not studied further . The loss of function phenotypes of 29D , tubulinß3 , egip , CyIIIa , admp2 , fgfr1 , pax2/5/8 , unc4 , nk1 , id , rkhd and ptb and otx were not analyzed in this study and these genes were only used as markers in the following experiments . The sequences of all the morpholino oligomers used in this study are listed below . The most efficient morpholino of each pair is labeled with a star . alk4/5/7 Mo 1: TAAGTATAGCACGTTCCAATGCCAT alk3/6: Mo1: TAGTGTTACATCTGTCGCCATATTC brachyury Mo1: AGCATCGGCGCTCATAGCAGGCATA brachyury Mo2*: CTGGCAGAAGATGTACTTCGACGAT bmp2/4 Mo1*: GACCCCAGTTTGAGGTGGTAACCAT bmp2/4 Mo2: CATGATGGGTGGGATAACACAATGT chordin Mo1*: GGTATAAATCACGACACGGTACATG chordin Mo2: CGAAGATAAAAACTTCCAAGGTGTC deadringer Mo1: TGCTCGCGGTAACAAGTGATTCCAT deadringer Mo2: TTATATGGCAAAGGACTTCTACAGC dlx Mo1: CCCACGTCAAATGAATACATCAACA dlx Mo2: AAACACGTTTAGAATCCTCACGACT fgfA Mo1: ACTTTCATCCATTTTCGCTTTCATG fgfA Mo2*: ACACATTTTGGATACTTACAGCTCC foxA Mo1: CATGGGTTCCTCCTTGAAATCCACG foxA Mo2*: TGAAAGATTAAAGTAGCACAGTCAG foxG Mo1*: TCCGATGAATGTGCATGAAAAACTG foxG Mo2: CTTCTTGCTAAATACCAAGTTGGAG goosecoid Mo1*: TGTCTGGAAGGTAATAGTCCATCTC goosecoid Mo2: AGATCAGAGCTAACCACTTAGGACG hnf6/onecut: Mo1: AGCCGCTGGACCTCAAACGCGAAGA hnf6/onecut Mo2*: AAAATGATAATGTGGTCTCCGTCGC hox7 Mo1: TGACGAAATACGAACTCGAACTCAT hox7 Mo2: ACCACTTCATTAATAGCCAAAACCT irxA Mo1: ATTGTGGATAACTGCTCGTCGTCAT irxA Mo2: TTGTTGAAATCAACTTTGAGACGAT Lefty Mo1: GGAGCGCCATGAGATAATTCCATAT Lefty Mo2: GGAGATGGGCAAAATATGAAGATAC msx Mo1: CGACTTGATGGAAGAAAATTATTCC msx Mo2 : TTATCGCTTTAAGAATGACCAAGGA NK1 Mo1: AAGCATTGAGAATCCCTAAAACTGC NK1 Mo2: CATGTGCTCTGTTCAGACGGTCAAC nk2 . 2 Mo1: ATCAACATTCATACGATGTCTCTAT nk2 . 2 Mo2: ATAGTTAATTCCACACCACCCACTT nodal Mo1*: ACTTTGCGACTTTAGCTAATGATGC nodal Mo2: ATGAGAAGAGTTGCTCCGATGGTTG tbx2/3 Mo1: TCGACGAACCACCAAATCTTGAGCA tbx2/3 Mo2* : TCGGCAAAAGCCTCCGAGTCCAAAT Oasis Mo1: CTCTTCACCTAAAAGCCCATCCATG Oasis Mo2: CCAATTTGGGCCGTAGTCGAGGGAC soxB1 Mo 1*: GACAGTCTCTTTGAAATTAGACGAC soxB1 Mo2: GAAATAAAGCCAAAGTCTTTTGATG univin Mo1*: ACGTCCATATTTAGCTCGTGTTTGT univin Mo2: GTTAAACTCACCTTTCTAAACTCAC wnt8 Mo1: GAACAACTGCCGTAAAGATATCCAT wnt8 Mo2*: AACAGTCCAAATATGAAGTTCAAAC As a control for defects related to injection and egg quality , we used morpholinos directed against the hatching enzyme gene: 5′-GCAATATCAAGCCAGAATTCGCCAT-3′ or against the Nemo like kinase transcript -5′-TCGGAGGCAGACCAGCAGCGAGAAA-3′ . Embryos injected with either of these morpholinos at 1mM normally develop into pluteus larvae . Morpholinos oligonucleotides were dissolved in sterile water and injected at the one-cell stage together with Tetramethyl Rhodamine Dextran ( 10000 MW ) at 5 mg/ml . For each morpholino a dose-response curve was obtained and a concentration at which the oligomer did not elicit non-specific defect was chosen . Approximately 2–4 pl of oligonucleotide solution at 0 . 5 mM were used in most of the experiments described here . For morphological observations , about 150–200 eggs were injected in each experiment . To analyze gene expression in the morphants a minimum of 50–75 injected embryos were hybridized with a given probe . All the experiments were repeated at least twice and only representative phenotypes observed in more than 80% of embryos are presented .
Echinoderms ( sea urchins , starfish , etc . ) are marine invertebrates that share a close ancestry with vertebrates . Their embryos offer many advantages for the analysis of transcriptional circuits that control developmental programs . During early development of the common sea urchin Paracentrotus lividus , a signaling center located within the ventral ectoderm sends two key signals , Nodal and BMP2/4 , that control patterning of the embryo along the whole dorsal-ventral axis . How this signaling center works is not understood . We have conducted a large-scale functional analysis of the genes responsible for patterning of the ectoderm along the dorsal-ventral axis . We identified direct targets of Nodal and BMP2/4 and identified several key regulators that mediate the effects of these factors and drive essential and probably ancient regulatory circuits that together constitute a transcriptional program controlling morphogenesis of the embryo . In addition , we uncovered a striking parallel between the mouse embryo and the sea urchin embryo by showing that in both models a neurogenic ectoderm is the default state of ectoderm differentiation in the absence of Nodal and BMP signaling . Our results support the idea that inhibition of Nodal and BMP signaling was probably an ancient mechanism to specify neural cells in the ancestor of vertebrates .
You are an expert at summarizing long articles. Proceed to summarize the following text: We use reinforcement learning to train an agent for computational RNA design: given a target secondary structure , design a sequence that folds to that structure in silico . Our agent uses a novel graph convolutional architecture allowing a single model to be applied to arbitrary target structures of any length . After training it on randomly generated targets , we test it on the Eterna100 benchmark and find it outperforms all previous algorithms . Analysis of its solutions shows it has successfully learned some advanced strategies identified by players of the game Eterna , allowing it to solve some very difficult structures . On the other hand , it has failed to learn other strategies , possibly because they were not required for the targets in the training set . This suggests the possibility that future improvements to the training protocol may yield further gains in performance . Re-engineering and de novo design of RNA molecules to perform novel biological functions have been major focuses of modern bioengineering research [1–5] . The function of an RNA molecule is determined by the structure into which it folds , which is in turn determined by the sequence of nucleotides that comprise it . Designing RNA molecules to perform specific functions therefore requires solving the inverse folding problem for RNA: given a target structure , design a sequence that folds into that structure . Here , we are specifically interested in solving the computational RNA design problem: given a target structure , design a sequence that folds into the target as judged by an in silico structure predictor . Current state-of-the-art structure prediction software such as ViennaRNA [6] can predict the experimental minimum free energy structure with high accuracy , and has been a key step in designing RNA molecules for several real-life biological applications [7–10] . Therefore , solving the computational RNA design problem will likely provide valuable insights into solving real-life RNA design problems . Significant progress has been made in developing machine-based algorithms for computational RNA design . One of the initial algorithms , RNAInverse [11] , uses a simple adaptive walk in which random single or pair mutations are performed on a sequence , and a mutation is accepted if it improves the structural similarity between the current and the target structure . A subsequent algorithm RNA-SSD [12] first performs hierarchical decomposition of the structure into substructures to reduce the size of the search space before performing adaptive walk . INFO-RNA [13] first generates an initial guess of the sequence using dynamic programming to estimate the minimum energy sequence for a target structure , and then performs a stochastic search using simulated annealing . DSS-Opt [14] and NUPACK [15] both attempt to optimize more complex objective functions that explicitly punish incorrectly-pairing bases . DSS-Opt employs a gradient-based approach on the full sequence to optimize an objective function that includes both a free energy component and a “negative design term” to punish incorrectly paired bases . On the other hand , NUPACK first performs hierarchical decomposition on the target , and then for each substructure , computes a thermodynamic ensemble of structures given the current sequence . It then perturbs the sequence to optimize the “ensemble defect” , which is the average number of incorrectly paired bases across all structures in the ensemble , weighted by their thermodynamic populations . Finally , MODENA [16] generates an ensemble of initial guesses using a genetic algorithm , and then performs stochastic search using either crossover moves , in which pieces of two candidate solutions at the same position are swapped with each other , or single-point mutations . These solutions are then judged using an objective function with components for both energetic stability and target structure similarity . Eterna [17] takes a very different approach to the problem . It is an online open laboratory that presents the inverse folding problem as a game , and asks players to create sequences that fold to specific structures . Some of the top players develop exceptional skill at this task , and can successfully find solutions for structures that none of the algorithms described above can solve . The Eterna100 benchmark [18] is a collection of 100 target structures ( "puzzles" ) created by players of the Eterna game . They were chosen to cover a wide range of difficulty levels , and to exhibit a variety of challenging structural elements . They are ordered inversely with respect to the overall number of correctly proposed solutions by Eterna players , with the second half being significantly more difficult to solve than the first half . A test of the above algorithms on the Eterna100 benchmark resulted in several of the algorithms performing poorly , with RNAInverse and RNA-SSD predicting valid sequences for only 28/100 and 27/100 structures respectively . Even the best-performing algorithm , MODENA , could only succeed on 54/100 structures . A common weakness of several existing computational algorithms , including RNAInverse , RNA-SSD , INFO-RNA , NUPACK , and MODENA , is that they rely at least partially on purely stochastic search: random mutations are made to a sequence and beneficial mutations ( e . g . that improve structural similarity to the target or reduce the structure’s energy ) are accepted . This procedure successfully predicts sequences for short nucleotide chains and simple RNA structures due to the small space of possible mutations . However , this method fails for longer chains or more complex structures in which sampling the entire space of possible mutations is prohibitively expensive . One way to reduce the size of the search space is to first decompose the full structure into substructures and independently optimize each substructure , as was done in RNA-SSD . However , for target structures in which accurate determination of the sequence of one part of the structure requires global knowledge about the entire structure , hierarchical decomposition is not feasible . For example , the "Mutated Chicken Feet" structure from the Eterna100 contains three symmetric branches in which each branch must be assigned a sequence that is different from the other two , and thus cannot be solved by independently considering each branch . Alternatively , target structures that possess easily accessible off-pathway local minima to obstruct the search , or lack on-pathway local minima to positively guide the search , present additional challenges for these algorithms . One possible means to address the weaknesses of purely stochastic search is to incorporate prior information into the model . Most commonly this is done by performing free energy calculations to find an energetically favorable candidate sequence for a given target that is closer to a valid sequence , thereby reducing the number of search steps . Algorithms such as INFO-RNA use this strategy , in which it first performs base-by-base free energy calculations over the target structure to generate the minimum energy sequence for the target . This sequence is then used as a starting point for the subsequent stochastic search . This method results in significantly increased performance on the Eterna100 , with INFO-RNA succeeding on 50/100 structures as opposed to RNAInverse’s 28/100 . Nevertheless , this strategy proves infeasible in many cases because the sequence that minimizes the energy for a target structure is not guaranteed to fold into that structure , or even a structure close to it . For many of the more complex puzzles in the second half of the Eterna100 , the sequence which minimizes the energy of the target possesses a global energy minimum that is structurally very different from the target , making it very difficult to refine this solution into a correct one through stochastic search . Algorithms such as DSS-Opt and NUPACK attempt to mitigate this issue by using more complex objective functions that punish incorrectly paired bases . This allows for some level of control in avoiding sequences that fold into structures very different from the target structure , leading to respectable performance on the Eterna100 , with DSS-Opt and NUPACK succeeding on 47/100 and 48/100 structures respectively . Most notably , both DSS-Opt and NUPACK manage to succeed on some structures unsolvable by INFO-RNA , such as "Misfolded Aptamer 6" , demonstrating the advantages of utilizing a more complex objective function for certain structures . Nevertheless , the performance of all the algorithms substantially decreases as puzzle complexity increases , as all perform very poorly on the second half of the Eterna100 , solving at maximum only about 25% of the puzzles . In the last decade , machine learning has had remarkable success at solving a variety of challenging computational problems including computer vision [19] , speech recognition [20] , machine translation [21] , and others . Instead of designing an algorithm by hand , one constructs a very flexible mathematical model ( usually a neural network ) , then optimizes the parameters of the model until it produces the desired output for a set of training data . Reinforcement learning ( RL ) is a branch of machine learning that deals with problems where an agent performs a series of actions to reach a goal . In the past , RL has proven extremely effective at training agents to perform a variety of difficult tasks , from video game playing [22] to robotic arm control [23] . Most recently , AlphaGo Zero achieved superhuman performance in the game of Go by learning the game purely through self-play [24] . Given these remarkable results , we reasoned that RL might be able to train a competent agent for RNA design . Similar to how an agent for Go can learn the optimal move to make given one of many unique board positions , we hypothesized that an RNA design agent could also learn the optimal design choice given one of many unique target RNA structures . Reinforcement learning describes a problem in terms of an agent ( e . g . a player of the game Eterna ) interacting with an environment ( e . g . an RNA molecule ) . At each step , the agent observes the current state of the environment ( the current RNA sequence ) , and selects an action to perform ( a change to the sequence ) . The action is selected by following a policy represented by a neural network . Whenever the agent makes progress toward its goal , it receives a reward . The policy network is trained by having the agent perform the task over and over , adjusting the network parameters to maximize the expected future rewards . Remarkably , this simple process can sometimes produce algorithms that outperform the very best algorithms crafted by expert programmers . In this work , we apply it to the RNA inverse folding problem , training a policy network that outperforms all previous algorithms on the Eterna100 benchmark . We formulate the inverse folding problem as a reinforcement learning problem . The current state corresponds to a candidate sequence . The agent takes actions that modify the type of a single base , or in some cases two paired bases . We train a policy network to select actions that eventually lead to a sequence with the desired secondary structure . When the target structure is achieved ( that is , when the agent finds any sequence that is predicted to fold to the target structure ) , the agent receives a fixed positive reward . All other actions receive a reward of 0 . The input to the policy network is an N×4 tensor , where N is the number of bases , giving the current sequence in one-hot encoding . The network's output is an N×4 tensor containing action probabilities . Each element is the probability of changing one of the N bases to one of the four possible types . At each step , an action is randomly chosen based on the probabilities generated by the network . Note that 25% of all actions simply say that a base should have the same type it already has . To avoid wasting time on unproductive actions , the probabilities of these actions are forced to be 0 . If an action modifies a base that is supposed to be paired in the target structure , the type of its desired partner is checked and , if necessary , modified as well to ensure the two bases are capable of forming a pair . If their types are not one of GC , AU , or GU , the target partner is modified so they can form a GC or AU pair . Our goal is to train a single model that can be applied to RNA sequences of any length . This requires that the set of parameters defining the model must be independent of sequence length . We achieve this by building the model entirely out of convolutional layers . More precisely , each layer takes as input a tensor of shape N×Cin and produces one of shape N×Cout , where Cin and Cout are the numbers of channels per base . At the network's input ( the current sequence ) and output ( action probabilities ) , the number of channels is 4 . At all other points in the network , it is set to 80 . The network is built up out of the following types of layers . Single base convolution ( conv1 ) : The output is computed independently for every base . Each output channel is a linear combination of the input channels for that same base . Seven base convolution ( conv7 ) : The output for each base is a linear combination of the inputs for seven bases , followed by a ReLU activation . The seven bases include: For example , suppose that base 7 is supposed to be paired with base 18 in the target structure , but is actually paired with base 15 in the structure to which the current sequence folds . The output for base 7 would be calculated from the inputs for bases 5 , 6 , 7 , 8 , 9 , 18 , and 15 . It is possible for some of these bases not to exist , such as if the target base is at the end of the chain , or if it is not paired in the target structure . In that case , the corresponding inputs are set to 0 . The conv7 layer can be viewed as a type of graph convolution , although it is unusual in that the graph structure may change at every step , as it explores sequences that fold to different structures . Also note that the connections formed within conv7 layers are the only way in which the network receives information about the target structure . Instead of using a simple convolutional network , we combine multiple layers to form residual blocks . The output of each block is computed as y = x + conv1 ( conv7 ( x ) ) Networks composed of residual blocks have been shown to be easier to train and to produce better results than simple convolutional networks [25] . The full network is shown in Fig 1 . It consists of a single conv7 layer that increases the number of channels from 4 to 80 , followed by three residual blocks . A final conv1 layer with softmax activation computes the action probabilities . In addition , a dense ( fully connected ) layer computes an estimate of the state value function , which is used by the training algorithm [26] . It outputs an estimate of the total future reward starting from a given state . The number of parameters in the dense layer depends on the length of the sequence , which means it is necessarily specific to a particular sequence length . It is only used during training , however . Therefore , all training sequences must have the same length , but once training is complete , the policy network can be applied to sequences of any length . The numbers , types , and widths of the layers in the network were chosen by experimentation , although no exhaustive hyperparameter search was performed . Our goal was to find the smallest network such that further increases in size had little benefit . For example , using 80 output channels was found to give significantly better results than 64 , but 100 output channels worked no better than 80 . A more rigorous hyperparameter search might improve our results somewhat , but would probably not dramatically change the model's performance . We require a set of target structures to use for training . We created them by randomly generating 100 , 000 RNA sequences of length 32 , then computing the structure each one folds to . This yielded a total of 46 , 188 unique structures , none of which appears in the Eterna100 benchmark . The majority of them ( 34 , 264 ) occurred only a single time among the 100 , 000 sequences . Another 6158 structures occurred twice , and 5766 structures occurred three or more times . The number of times a structure occurred gives an indication of its difficulty: if many sequences fold to a structure , it will be much easier to find one than if only few sequences do . Only the structures were used in training , not the sequences . Any sequence that folded to a target structure was accepted as a valid solution . The network was trained using the Asynchronous Advantage Actor-Critic ( A3C ) algorithm [26] . A different target structure was randomly chosen for every episode . Training was run for a total of 1 . 5 million steps . The learning rate was initially set to 10−5 , then decreased by a factor of 0 . 8 after every 100 , 000 steps . The first half million steps used only the easier structures for training ( those which occurred three or more times ) . For the final 1 million steps , all structures were used except for 500 of the most difficult ones ( ones that occurred only a single time ) , which were set aside for use as a validation set . Every 100 , 000 steps , the current network was used to solve all of the validation structures and the total number of steps requires was recorded . If this was less than the previous best validation score , the network parameters were saved as the new best network . The model and training procedure were implemented using DeepChem 1 . 3 . 1 [27] and Tensorflow 1 . 3 [28] . ViennaRNA 2 . 3 . 5 [6] was used to compute the folded structures for sequences . The trained model was tested on the Eterna100 benchmark by following procedures that , as closely as possible , match those used in [18] . For each puzzle , a random sequence was initially chosen , then modified by performing actions chosen by the policy network until a solution was reached . 24 hours were allowed for each attempt , and up to five attempts were made for each puzzle . Each attempt ran on a single Intel Xeon E5-2680 v2 CPU . Structures with short stems are in general challenging for design due to their energetic instability [18] . Furthermore , as the number of stems increases , the design difficulty also increases due to the higher probability of mispairing between stems [18] . We observe that our model is competent at addressing these structural challenges . For example , our model can quickly solve both Shortie 4 and Shortie 6 from the Eterna100 , which consist of two and four length-2 stems respectively . Shortie 4 took 1390 steps and 2 . 2 seconds to solve , whereas Shortie 6 required 121 , 113 steps and a little more than 4 minutes . This is consistent with the fact that Shortie 6 , having a larger number of stems , is more difficult to solve . To address the issue of mispairing between stems , our model introduces asymmetries in the stem base pairing patterns , such that stems 1 and 2 are composed of alternating GC/CG pairs , whereas stems 3 and 4 contain non-alternating CG/CG pairs , as shown in Fig 3 . The application of different sequences to symmetric structural elements is also a common strategy employed by human players to solve difficult puzzles with high symmetry [18] . Furthermore , our model also chose to mutate the first base at the 5' end of the 4-loop of stem 4 to G , which corresponds to a human-developed strategy that stabilizes stems , named "boosting" by Eterna players . From this observation , we initially believed that our model had learned how to boost 4-loops during training . However , this is not the case , as the model also makes a deceptively boost-like G-mutation at the 3' end of the 4-loop of stem 3 , which does not actually stabilize the structure ( the correct move would have been to mutate the first base at the 5' end to G ) . Therefore , we believe that the boost made at stem 4 was not due to the model learning this strategy , but rather due to random chance because of the relatively small size of the puzzle and the large number of steps taken to solve it . To further investigate whether the model had learned how to boost , we chose 124 structures at random from the validation set that contained 4-loops and examined the distribution of bases at the boosting position . We observed that the boost position has an identity of G in only 19 ( 15% ) of the solutions , suggesting that the model has not learned that boosting is a favorable move . To address the alternative possibility that the model is intentionally not boosting the 4-loop and instead choosing an alternative strategy incompatible with boosting , for all structures in which the boost position was not G , we manually mutated this base to G and then compared the predicted free energy of this boosted sequence to that of the original sequence . We observe that the boost stabilizes the structure an overwhelming majority of the time ( 98 . 2% of the structures ) by an average of -0 . 67 ± 0 . 58 kcal/mol . Therefore , we conclude that the model is not making design choices incompatible with boosting , but rather has not learned the boosting strategy . This is likely because the structures in the training set are small enough that boosting if often not necessary for stabilizing the structure . Indeed , of the 19 predicted solutions with G in the boost position , removing the boost by mutating the G to A did not affect the predicted structure in all but 3 cases , confirming that for the large majority of structures , boosting is not necessary to the solution . In the future , expanding the training set to include longer sequences to make boosting a necessary stabilization move may allow the model to learn this strategy . In addition to the Shortie puzzles , our model most notably also succeeds in solving both Kyurem 7 and Kyurem 5 very quickly , two structures which none of the algorithms previously benchmarked could solve . These structures are noted for being particularly difficult due to the presence of multiple short stems connected by multiloop junctions , which significantly increases the probability of mispairing between stems . Kyurem 5 , the easier of the two puzzles , took our algorithm 821 steps and 7 . 7 seconds to solve , whereas Kyurem 7 took 4563 steps and 36 . 4 seconds . Like its solution strategy for Shortie 6 , we notice that our model makes asymmetric design choices for Kyurem 7 with respect to the stems surrounding the two multiloops . It assigns the three length-3 stems around the first multiloop a common motif of alternating GC/AU/CG pairs , whereas for the stems around the second multiloop , except for the closing AU pair at ( 47 , 67 ) , the model assigns only GC pairs . Previously , it was shown that stabilization of a given stem for Kyurem 7 requires not only making stabilizing mutations within that stem , but also making precise mutations to multiple nearby stems [18] . Thus , such asymmetric design of stems to prevent inter-stem mispairing is likely necessary for stabilizing the structure . We also observe that our proposed sequence is indeed very sensitive to small perturbations . Taking each base pair in the sequence and either swapping the bases or mutating the pair to GC , both common design moves , almost always resulted in the sequence misfolding into a different structure . Only perturbations of four base pairs: ( 15 , 20 ) , ( 36 , 44 ) , ( 37 , 43 ) , or ( 51 , 60 ) did not result in misfolding . Finally , in a striking contrast with previous computational algorithms , we note that our model can solve both Kyurem 7 and Kyurem 5 much faster than Shortie 6 , which is designed to be a less difficult puzzle . This raises the possibility that perhaps our model is not learning design strategies in a progressive manner , i . e . simple to advanced , but is somehow first learning advanced strategies , leaving a gap in its knowledge base that makes it more difficult to solve easier puzzles . The presence of bulges and internal loops are known to destabilize structures , as they result in a smaller overall amount of base pairing and encourage mispairing to form more stable stems [18] . Likewise , our model's performance noticeably decreases as the number of bulges or internal loops increase . For example , our model can solve the relatively easy "Just down to 1 bulge" extremely quickly in only 11 . 9 seconds , but fails for its more difficult counterpart "1 , 2 , 3and4bulges" . This is consistent with the performance of previous computational algorithms: "1 , 2 , 3and4bulges" was only solvable by MODENA , whereas "Just down to 1 bulge" was solvable by 4 of 6 algorithms . Likewise , our model succeeds in solving "Loop next to a multiloop" , which consists of two internal loops stabilized by a short stem , in about 25 minutes , but fails for structures such as "Crop circle 2" , which consists of five internal loops stabilized by short stems . This is consistent with the performance of previous computational algorithms , as "Loop next to a multiloop" was solvable by RNA-SSD , but "Crop circle 2" was unsolvable by any algorithm . We expect that structures such as "Crop circle 2" , due to their complexity and uniqueness , will pose challenges for our model , as such motifs have not been encountered during training . As discussed above , structures with high symmetry introduce opportunities for mispairing between symmetric elements , contributing to design difficulty . Our model shows varied success at solving puzzles with this characteristic . Although it can readily handle simple puzzles with symmetric elements such as Shortie 6 and Fractal 2 , it fails for more complicated puzzles such as Mutated Chicken Feet , which consists of three symmetric branches of consecutive short stems linked by bulges and multiloops , a combination of several factors that make design difficult . The unique combination of different structural elements comprising this puzzle poses a significant challenge to our current model , since it is distinct from anything encountered in the training set . Unusual structural patterns that arise infrequently in nature also present challenges for current design algorithms . For example , in the "Hard Y" puzzle , the presence of two consecutive small bulges in a structure , named the "zigzag" , significantly increases the difficulty of the puzzle and renders it unsolvable for any of the previously benchmarked computational algorithms . Remarkably , we observed that our model could propose a valid sequence to stabilize the zigzag and solve "Hard Y" in a little under 10 minutes . However , given the relatively long timescale taken to solve hard Y compared to puzzles of even significantly longer length such as the Kyurem puzzles , it is possible that our model did not actually learn the solution strategy for the zigzag during training , but instead arrived at the solution primarily by random search after intelligently solving the remainder of the puzzle . Many computational algorithms approach the RNA design problem by first initializing to a random sequence of bases and then performing some form of stochastic search , such as RNAInverse and RNA-SSD . This random initialization can overcomplicate the solution process for certain structures . For example , "This is actually small and easy 6" , which is a chain of 400 unpaired bases , was solvable only by NUPACK and DSS-Opt out of the six methods tested . A trivial solution that is easily identifiable by humans is to simply set every position in the sequence to the same base , but many computational algorithms fail to arrive at this solution due to the random initialization of the sequence , leading to many unintended base pairs . We expected our model , which also randomly initializes the sequence , to struggle with this structure . However , our model was able to solve this puzzle remarkably quickly in less than 5000 steps despite the structure's length . This indicates that the model is not making random moves , but has learned during training how to break unwanted base pairs and is making intelligent mutation choices to fix a bad initial sequence . In summary , we observe that our model is very competent at stabilizing short stems , even in the presence of additional destabilizing structural motifs such as multiloops or structural symmetry , by introducing precise asymmetric base pairing patterns for the stems . The two representative puzzles with these characteristics unsolvable by any previous computational algorithm , Kyurem 5 and Kyurem 7 , were both solved very quickly by our model . On the other hand , our model seems to struggle with structures containing large numbers of bulges or internal loops . Although it solves the relatively simple "Just down to 1 bulge" , our model fails for the more difficult "1 , 2 , 3 , and4bulges" and "Crop circle 2" . We hypothesize that the model's poor performance on these structures is because stabilization of them requires the application of strategies the model did not learn during training . For example , "1 , 2 , 3 , and4bulges" possesses an unusual structural motif of a 4-loop connected to a length-1 stem that to our knowledge must be boosted to stabilize the structure . Likewise , "Crop circle 2" requires the internal loops to be simultaneously boosted in one of few specific ways . Because our model did not learn the boosting strategy during training , it expectedly fails for structures where boosting is necessary for stabilization . As a future study , increasing the length of the structures in the training set may allow for sampling of more complicated structures for which boosting is a necessary stabilization move . This would allow the model to consistently sample positive rewards for boosting during training and learn the strategy . In this study , we train an agent for RNA design using reinforcement learning and a training set of randomly generated secondary structures . We observe that the agent can learn effective design strategies fully autonomously with no human input . Remarkably , our trained agent outperforms any previous computational algorithm , solving 60/100 of the Eterna100 . Due to the limited length and complexity of our current training set , the model is unable to learn certain stabilizing strategies such as boosting , hindering its performance on more difficult structures for which these strategies are necessary . Expanding the length of the training sequences may allow the model to sample more complex structural motifs and learn more advanced strategies to improve its performance . We plan to investigate this possibility in future work .
Designing RNA sequences that fold to desired structures is an important problem in bioengineering . We have applied recent advances in machine learning to address this problem . The computer learns without any human input , using only trial and error to figure out how to design RNA . It quickly discovers powerful strategies that let it solve many difficult design problems . When tested on a challenging benchmark , it outperforms all previous algorithms . We analyze its solutions and identify some of the strategies it has learned , as well as other important strategies it has failed to learn . This suggests possible approaches to further improving its performance . This work reflects a paradigm shift taking place in computer science , which has the potential to transform computational biology . Instead of relying on experts to design algorithms by hand , computers can use artificial intelligence to learn their own algorithms directly . The resulting methods often work better than the ones designed by humans .
You are an expert at summarizing long articles. Proceed to summarize the following text: In the past decade , research on neglected tropical diseases ( NTDs ) has intensified in response to the need to enhance community participation in health delivery , establish monitoring and surveillance systems , and integrate existing disease-specific treatment programs to control overlapping NTD burdens and detrimental effects . In this paper , we evaluated the geographical distribution of NTDs in coastal Tanzania . We also assessed the collective ( compositional and contextual ) factors that currently determine risks to multiple NTDs using a cross sectional survey of 1253 individuals in coastal Tanzania . The results show that the effect size in decreasing order of magnitude for non-binary predictors of NTD risks is as follows: NTD comorbidities > poverty > educational attainment > self-reported household quality of life > ethnicity . The multivariate analysis explained 95% of the variance in the relationship between NTD risks and the theoretically-relevant covariates . Compositional ( biosocial and sociocultural ) factors explained more variance at the neighbourhood level than at the regional level , whereas contextual factors , such as access to health services and household quality , in districts explained a large proportion of variance at the regional level but individually had modest statistical significance , demonstrating the complex interactions between compositional and contextual factors in generating NTD risks . NTD risks were inequitably distributed over geographic space , which has several important policy implications . First , it suggests that localities of high burden of NTDs are likely to diminish within statistical averages at higher ( regional or national ) levels . Second , it indicates that curative or preventive interventions will become more efficient provided they can be focused on the localities , particularly as populations in these localities are likely to be burdened by several NTDs simultaneously , further increasing the imperative of multi-disease interventions . The role of space in shaping health inequities ( e . g . , differential distribution and by extension differential exposure to and risk of NTDs ) is of continuing interest in environmental health geography . In studying the role of space in shaping health outcomes , individual-level ( compositional ) and place-level ( contextual ) factors have traditionally been identified [11–15] . Usually , the health outcomes experienced by individuals living proximal to one another are more similar to one another than to those of individuals living in distant neighbourhoods . In theory , three plausible explanations account for this observation . First of all , it may purely be that individuals in the same neighbourhood tend to be more similar to one another than to those in other neighbourhoods in terms of predisposing factors such as age , gender and ethnicity , that is , the composition effect [13] . Another viable explanation may be that individuals living in the same neighbourhood are exposed to similar local factors that have impacts on their health outcomes ( NTD risks ) , for example proximity to a river where blackfly proliferate or service provision , that is , the context effect [13] . It can also be argued that individuals who live in proximity are more likely to engage in the same types of behavior that may have influences on health outcomes–for example behavior of bathing in rivers among adolescents which are affected by peer pressure-the collective effect . Since we live in a complex world , in reality all three elements may be present to varying extents in relation to the distribution of neglected tropical diseases . It is , therefore , imperative to include relevant environmental , behavioural and predisposing factors and to recognise the inherent complexity of composition/context/ collective effects [16] . It is recognized that ‘context’ and ‘place’ vary in time and space . Theoretical and empirical approaches ( both qualitative and quantitative ) require relational notions of space and place that accepts mutually reinforcing and reciprocal relationships between people and place [11] . Further , scale must be included in the analysis of ‘contexts’ relevant for health . Under this circumstance , place is regarded as complex , socially constructed , unbounded , fluid , and dynamic [11] . It also is multi-scalar , enmeshed in networks , shows social power relations and has cultural meaning [11 , 17] . Recent studies also demonstrate that context ( e . g . neighbourhoods ) is important in determining health outcomes but that compositional factors such as gender , ethnicity , employment status and socio-economic status remain better predictors of inequalities in health [13 , 15 , 18] . In modelling , the compositional and contextual accounts of health outcomes are usually considered as ‘mutually exclusive , competing , and culturally and historically universal’ [15] . In fact , from a sustainability perspective , this dichotomous framing is rather problematic . Smyth [15] argues that this supposed difference between people and places , composition and context , is rather artificial . In this study , therefore , we move beyond this polar conceptualization and focus on the collective effect of compositional and contextual attributes . According to Cummins et al . [11] , a change from empirical research intended to differentiate between contextual and compositional effects to research that focuses on the processes and interactions occurring between places and people and over time is important for understanding health outcomes ( e . g . the distribution of NTDs ) , and is justified . It is in this milieu that this paper should be understood . In Fig 1 we show how compositional and contextual attributes jointly shape exposures to multiple NTD risks . In Fig 1 , we observe that exposures of individuals to multiple health risks associated with NTDs involve complex and dynamic interplay among biological , environmental , and sociopolitical components spanning multiple time intervals and spatial ( geographic ) scales . In this context , the question is more about what types , in what places , how they contribute and how they can be addressed . In this framework , we consider NTD risks and health outcomes as emergent properties of complex interactions between humans and their environment . There are several feedback relationships in Fig 1 indicating the multi-factorial nature of both the determinants and the manifestations of NTD health outcomes in coastal populations . For instance , there is a bidirectional linkage between compositional and contextual factors . Similar relationships exist between NTD risks and compositional factors on the one hand and NTD risks and contextual factors , on the other hand . Risks of individuals to NTDs emanate from two mutually-reinforcing factors namely exposure and vulnerability . Predisposing factors to vulnerability include both biosocial and sociocultural dynamics . The latter , together with contextual attributes , also contribute to exposures of individuals to multiple NTD risks . Altogether , this interactivity between variables and processes , which are shaped by multiple factors , warrants a systems approach to addressing exposures and risks to multiple NTDs . A systems approach comprehensively considers all known and measurable aspects of a problem , including feedbacks that cross the boundaries of sub-systems and cut across scales; it acknowledges the nonlinearities and the dynamic nature of underlying processes , uncertainty and surprises [19] . Its potential can be harnessed by policymakers/researchers as they focus more on the social determinants of health when designing NTD interventions ( and target interventions ) . Tanzania is a coastal country lying between longitude 29° and 49° East and latitude 1° and 12° south of the Equator [20] ( Fig 2 ) . The marine waters comprise 64 000 km2 as territorial waters and 223 000 km2 as offshore waters ( EEZ ) [21] . Tanzania’s coastline stretches for 800km . It has five coastal regions-Tanga , Pwani , Dar-es-Salaam , Lindi and Mtwara . The five coastal regions cover about 15 percent of the country’s total land area and are home to approximately 25 percent of the country’s population [22] . According to the 2012 Population and Housing census , the total population was 44 , 928 , 923 compared to 12 , 313 , 469 in 1967 [23] , reflecting an annual growth rate of 2 . 9 percent . The under 15 age group represented 44 . 1 percent of the population , with 35 . 5 percent being in the 15–35 age group , 52 . 2 percent being in the 15–64 age group , and 3 . 8 percent being older than 64 [23] . Overall Tanzania on average is sparsely populated with population density of 51 persons per square kilometer , lower significant variation exists across regions . The population density varies from 1 person per square kilometre in arid regions to 51 per square kilometre in the mainland's well-watered highlands to 134 per square kilometre in Zanzibar [24] . The population density for the Dar es Salaam region is 3 , 133 persons per km2 ( the most densely populated ) and that of Lindi is only 13 . 1 persons per km2 [23] . This suggests wide disparities in population density across regions . This study specifically focuses on Dar-es-Salaam , Pwani and Tanga . The 3 coastal regions selected for analysis were chosen for two main reasons . First , the three regions are of historical significance to the Indian Ocean World project . Second , these regions were selected because of the 5 regions , they are the most ethnically diverse ( that is , representative of the different geographical locations ) and thus , had better prospects of providing heterogeneous survey responses . Dar es Salaam is the capital of the Dar es Salaam Region , which is one of Tanzania's 26 administrative regions . The Dar es Salaam Region consists of three local government areas or administrative districts: Kinondoni to the north , Ilala in the center of the region , and Temeke to the south . Pwani ( coast ) is the 21st most densely populated region . It is bordered to the north by the Tanga Region , to the east by the Dar es Salaam Region and the Indian Ocean , to the south by the Lindi Region , and to the west by the Morogoro Region . Tanga region has a population of 2 , 045 , 205 [24] . It is bordered by Kenya and Kilimanjaro Region to the north; Manyara Region to the west; and Morogoro and Pwani regions to the south . Its eastern border is formed by the Indian Ocean . Seven districts namely Kinondoni , Temeke and Illala ( in Dar-es-Salaam region ) , Bagamoyo ( in Pwani region ) , and Tanga Town , Muheza and Pangani ( in Tanga region ) were considered in this study . According to the Tanzania National Bureau of Statistics [23] , Kinondoni municipality has a population of 1 , 775 , 049 and density of 3302 . 8 inhabitants per km2 . Illala municipality has a population of 1 , 220 , 611 and density of 3344 . 4 inhabitants/km2 and Temeke municipality has population of 1 , 368 , 881 and density of 1878 . 5 inhabitants/km2 . The population and density of Bagamoyo are 311 , 740 and 36 . 8 inhabitants/km2 respectively whereas that of Muheza is 204 , 461 and 136 . 5 inhabitants/km2 , respectively [23] . Tanga town has a population of 237 , 332 and density of 458 . 2 inhabitants per km2 and Pangani district has a population of 54 , 025 and density of 30 . 8 inhabitants per km2 . The numbers of participations from each of seven districts were as follows Kinondoni ( 360 ) , Temeke ( 101 ) , Illala ( 140 ) , Bagamoyo ( 301 ) , Tanga Town ( 129 ) , Muheza ( 101 ) and Pangani ( 121 ) . The study was approved by the Research Ethics Board of the University of Western Ontario , Canada . Research approval was also granted by the Commission on Science and Technology ( COSTECH ) in Tanzania . Written informed consent was obtained from participant prior to commencement of the study . In addressing the first aim ( a brief historical perspective on NTD prevalence and distribution across Tanzania ) we conducted a literature search on the NTDs that have been reported in Tanzania since the 19th century . Using the country- and disease-specific query , we searched the Global Infectious Diseases and Epidemiology Network ( GIDEON ) database ( http://www . gideononline . com/ ) and the global NTD ( GNTD ) database ( http://www . gntd . org/ ) . We further obtained secondary data from the Tanzania National Institute for Medical Research ( NIMR ) . For triangulation , we interviewed experts from the national office of the Tanzania NTD Control Program ( TZNTDCP ) as well as the Ministry of Health and Social Welfare’s ( MOHSW ) integrated NTD program . In addressing the second objective ( assessment of the risk of exposure to multiple NTDs along the coastline of Tanzania ) we examined most-at-risk populations in coastal Tanzania and NTD profiles at the community level during the last five and ten years . This information was complemented with data from a cross sectional survey . The third aim was achieved by conducting a cross sectional survey with 1253 individuals in three regions ( Dar es Salaam , Tanga , and Pwani ) along the coastline of Tanzania . The survey data were collected between March and September 2013 . The study population included male ( 606 ) and female ( 647 ) participants between the ages of 18 and 70+ years . The study used multistage sampling to obtain representative estimates of the population of residents of the three regions . Within each region , a list of villages based on the 2012 Population and Housing Census was divided further into households . The list of villages was also divided into clusters ensuring that each cluster would provide adequate numbers of eligible respondents to be included in the survey . This approach both corrects for sampling bias and weights the cases to match census percentages of males and females of various age groups and by ethnicity . The enumeration areas ( EAs ) and their total number of households were listed geographically by urban and rural areas . Where EAs did not include the minimum number of households , geographically adjacent EAs were amalgamated to yield sufficient households . This provided the frame for selecting the clusters to be included in the survey according to a stratified systematic sampling technique in which the probability for the selection of any cluster was proportional to its size . A sampling interval was calculated by dividing the total number households by the number of clusters . A random number between 1 and the sampling interval was computer generated . The EA in which the random number fell was identified as the first selected cluster . The sampling interval was applied to that number and then progressively until the 20 ( urban ) and 15 ( rural ) clusters were identified . These clusters made up the sample for the survey . Households were randomly selected from these clusters for interview . Inferential and multivariate techniques were applied to examine associations between NTD risks and theoretically relevant compositional and contextual factors variables using STATA 13SE software . The Ordinary Least Squares technique was employed for the analysis . Analyses were preceded by diagnostic tests to establish whether variables met the assumptions of the regression model . Bivariate analysis was initially performed to examine zero-order correlations between the NTD risks and theoretically-relevant independent variables all of which were significant and in the expected direction , thus supporting good construct validity . Further , multivariate models were estimated to explore the net effects of the predictor variables using the stepwise selection approach . The relative quality of candidate multivariate models ( both OLS and ordinal logit based on categorized NTD risks ) was tested using Akaike Information Criterion ( AIC ) . After comparison , the best model was chosen based on parsimony , our working hypotheses , and strength of evidence . For analytical purposes , the unstandardized regression coefficients were estimated although standardized regression coefficients were used as indices of effect sizes of the non-binary predictors . Positive coefficients for any of the predictors indicate higher NTD risk , while negative coefficients show lower NTD risk . The ordinary least squares ( OLS ) regression models in this study are built under the assumption of independence of subjects , but the cross-sectional survey has a hierarchical structure with respondents nested within survey clusters , which could potentially bias the standard errors . STATA 13 ( StataCorp , College Station , TX , USA ) SE , which has the capacity to address this problem , is used by imposing on our models a ‘cluster’ variable , that is , the identification numbers of respondents at the cluster level . This in turn adjusts the standard errors ( SE ) producing statistically robust parameter estimates . Table 1 shows the various NTDs reported in Tanzania since the 19th Century . So far , buruli ulcer , chagas disease and dracuncunliasis have never been reported in Tanzania . However , cholera , schistosomiasis ( bilharzia ) , onchocerciasis ( river blindness ) , trachoma ( granular conjunctivitis ) , hookworm , whipworm , dengue fever , human African trypanosomiasis , leischmaniasis , leprosy and roundworm have been reported at least once during the last 200 years in Tanzania . Cholera outbreaks and cases surpass all cases and outbreaks of other NTDs in Tanzania . In general , prevalence and incidence of all NTDs have declined in the past 5 years in Tanzania . However , there are regional variations in the NTD type , number of cases and frequency of occurrence of the various NTDs . Each individual in the survey sample was simultaneously exposed to at least two NTDs . No individual was simultaneously exposed to four or more NTDs as shown in Table 2 . About 75% of 1253 respondents were simultaneously exposed to three NTDs and 25% were exposed to 2 NTDs . Males and females were evenly distributed in terms of simultaneous exposures to 3 NTDs although females ( 54% ) were more exposed to 2 NTDs than their male counterparts . NTD comorbidities was associated with gender ( x2 ( 8 ) = 19 . 9265 , pr = 0 . 011 ) ) . Also , there were regional variations in NTD comorbidities ( x2 ( 16 ) = 37 . 4640 , pr = 0 . 002 ) ) and exposures to multiple NTDs ( x2 ( 2 ) = 1 . 2e+03 , pr = 0 . 000 ) ) . Although there are no statistically significant differences in NTD comorbidities according to poverty status there are differences in exposures to multiple NTDs by poverty status ( x2 ( 1 ) = 33 . 5928 , pr = 0 . 000 ) ) . In this study , the reported comorbidities were malaria ( 76% ) , hypertension ( 25% ) , tuberculosis ( 25% ) , HIV ( 10% ) , skin diseases ( 9% ) , pneumonia ( 6% ) , heart disease ( 5% ) , cholera ( 4% ) diabetes ( 3% ) , hepatitis ( 2% ) and cancer ( 1% ) . Based on the sample , malaria was the most common comorbidity . Based on the responses in the survey sample , inferential statistics ( chi-square ) did not find any statistically significant relationships between region of residence and each of the following diseases: hepatitis , skin diseases , pneumonia , HIV status , hypertension , cancer and heart disease . This means that these diseases were independent of region of residence . However , region was not independent of malaria ( x2 ( 2 ) = 1 . 3e+03 , Pr = 0 . 000 ) , tuberculosis ( x2 ( 2 ) = 5 . 9731 , Pr = 0 . 045 ) , cholera ( x2 ( 2 ) = 19 . 1780 , Pr = 0 . 000 ) and diabetes ( x2 ( 2 ) = 11 . 7872 , Pr = 0 . 003 ) . Exposures to multiple NTDs do not vary according to age even though NTD comorbidities vary by age of respondents . Both NTD comorbidities and exposures to multiple NTDs vary by educational attainment of respondents . In coastal areas of Tanzania , risks of cholera , hookworm and whipworm are higher in Dar es Salaam region than in Tanga and Pwani regions . Risks of schistosomiasis and trachoma are higher in Pwani and Tanga regions than in Dar es Salaam region . Risks of onchocerciasis are higher in Tanga region than in Pwani and Dar es Salaam regions . Based on the magnitude of Cramér's V , the strength of association of theoretically-relevant covariates and exposure to multiple NTDs in decreasing order of magnitude is as follows: region of residence > residential locality > ethnicity > educational attainment > residential time > poverty access to health services > NTD comorbidities > self-rated housing quality . Table 3 shows zero-order relationships between NTD risks and theoretically relevant covariates . All biosocial factors were significant predictors of NTD risk . Older individuals and females were associated with higher NTD risk scores . Age squared was statistically significant indicating possibly that the relationship between age and NTD risks was not linear . Each of the sociocultural factors was a significant predictor of NTD risks except employment ( Table 3 ) . Higher NTD comorbidities , higher exposures to multiple NTDs and higher poverty levels were each associated with higher NTD risks . Except residential time and self-reported household quality of life , all contextual factors including sources of drinking water in the wet and dry seasons , rural/urban status and region of residence were significant predictors of NTD risks . Table 4 is a nested model that shows the multivariate relationship between NTD risks on the one hand and compositional and contextual factors , on the other hand . The statistically significant zero-order relationship between NTD risks , age , sex and ethnicity remains in the biosocial model in the multivariate analysis . The biosocial model explains only 4% of the variance in the relationship between NTD risks and theoretically relevant covariates . Females and older respondents were associated with higher NTD risks . When the model is adjusted for sociocultural factors , the original zero-order relationship between poverty and NTD risks unexpectedly disappears . Self-reported household quality of life relative to others was a significant predictor of NTD risks in the sociocultural model unlike educational attainment and marital status . The sociocultural model explains about 19% of the variance in the relationship between NTD risks and theoretically relevant covariates . In the place and neighbourhood model when both compositional and contextual factors ( collective effect ) are taken into account some interesting results emerge . For instance , the relationship between poverty and NTD risks which disappeared in the sociocultural model re-appears . The statistical significance of the relationship between ethnicity and NTD risks , which reduced in the sociocultural model , also becomes stronger in the neighbourhood model . In the sociocultural model , educational attainment was not a significant predictor of NTD risks unlike in the neighbourhood model . Employment status became statistically insignificant when collective effect is controlled . Access to health services , residential locality and NTD comorbidities were significant predictors of NTD risks when collective effect was accounted for in Table 3 unlike self-reported household quality of life and sources of drinking water in wet and dry seasons . Based on the standardised regression coefficients of non-binary predictors in the place and neighbourhood model , the effect size in decreasing order of magnitude is as follows: NTD morbidities > educational attainment > self-reported household quality of life > ethnicity . The place and neighbourhood model in the multivariate analysis explains 72% of the variance in the relationship between NTD risks and theoretically relevant covariates . Poorer , uneducated , unemployed individuals had higher NTD risk scores compared to their relatively affluent , educated and employed counterparts . This implies hypothesis 1 , which suggests that poorer individuals will be associated with higher exposures to multiple NTDs and by extension experience higher NTD risks than their less poor counterparts cannot be rejected . Individuals living in poorer neighbourhoods with limited access to health , social , water and sanitation services had higher NTD risk scores than their counterparts living in less-deprived neighbourhoods implying that hypothesis 2 cannot also be rejected . However , on disaggregating the results by gender , we did not find any evidence to support the third hypothesis which posits that women , by dint of social disadvantage , are more exposed to multiple NTDs and higher NTD comorbidities and so experience higher NTD risks than their male counterparts . In the females’ model , poverty was not even a significant predictor of NTD risks . Only , ethnicity , access to health services , rural/urban status and NTD comorbidities were significant predictors of NTD risks for women . In the males model however , poverty , age , ethnicity , educational attainment , employment status , access to health services and NTD comorbidities were significant predictors of NTD risks . Poorer older males without access to health services in rural areas had higher NTD risks compared to less poor younger males with access to health services living in urban areas . The study also presented a brief historical perspective on the distribution of NTDs in Tanzania during the last two centuries . This study also assessed exposure to multiple NTD risks using a cross sectional of 1253 individuals in coastal Tanzania . Both compositional and contextual factors act in complex ways to give rise to NTD risks . In particular , low socioeconomic status , poor neighbourhoods , age , NTD comorbidities and exposures to multiple NTDs are robustly associated with an increased risk of NTDs in coastal Tanzania . The link between poverty and NTD risks is persistent and remains even when compositional and contextual factors are adjusted . Based on the standardised regression coefficients of non-binary predictors in the place and neighbourhood model , the effect size in decreasing order of magnitude ( importance ) is as follows: NTD morbidities > educational attainment > self-reported household quality of life > ethnicity . On the whole , based on the findings , contextual factors are more important practically than compositional factors in terms of relative contribution to NTDs risks . Contextual factors cumulatively explained 72% of the variance in NTD risks whereas compositional ( biosocial and sociocultural ) factors jointly explained only 23% of the variance . Given the plethora of sociopolitical , economic and cultural factors that culminate in inequities in NTD exposures , comorbidities and risks between poor and less deprived individuals and groups an integrated approach to addressing NTD risks is warranted .
Neglected Tropical Diseases ( NTDs ) are characterized by their high incidence in low-income countries , thus maintaining the disastrous poverty-disease-poverty cycle . Apart from poverty , however , little is known of the magnitude of importance of both compositional and contextual factors in creating disease risk at the local level , although this knowledge is critical to disease control and policy action . In this study , we show that the order of importance of both sets of factors is as follows: NTD comorbidities > poverty > educational attainment > self-reported household quality of life > ethnicity .
You are an expert at summarizing long articles. Proceed to summarize the following text: Despite great efforts over several decades , our best models of primary visual cortex ( V1 ) still predict spiking activity quite poorly when probed with natural stimuli , highlighting our limited understanding of the nonlinear computations in V1 . Recently , two approaches based on deep learning have emerged for modeling these nonlinear computations: transfer learning from artificial neural networks trained on object recognition and data-driven convolutional neural network models trained end-to-end on large populations of neurons . Here , we test the ability of both approaches to predict spiking activity in response to natural images in V1 of awake monkeys . We found that the transfer learning approach performed similarly well to the data-driven approach and both outperformed classical linear-nonlinear and wavelet-based feature representations that build on existing theories of V1 . Notably , transfer learning using a pre-trained feature space required substantially less experimental time to achieve the same performance . In conclusion , multi-layer convolutional neural networks ( CNNs ) set the new state of the art for predicting neural responses to natural images in primate V1 and deep features learned for object recognition are better explanations for V1 computation than all previous filter bank theories . This finding strengthens the necessity of V1 models that are multiple nonlinearities away from the image domain and it supports the idea of explaining early visual cortex based on high-level functional goals . An essential step towards understanding visual processing in the brain is building models that accurately predict neural responses to arbitrary stimuli [1] . Primary visual cortex ( V1 ) has been a strong focus of sensory neuroscience ever since Hubel and Wiesel’s seminal studies demonstrated that neurons in primary visual cortex ( V1 ) respond selectively to distinct image features like local orientation and contrast [2 , 3] . Our current standard model of V1 is based on linear-nonlinear models ( LN ) [4 , 5] and energy models [6] to explain simple and complex cells , respectively . While these models work reasonably well to model responses to simple stimuli such as gratings , they fail to account for neural responses to more complex patterns [7] and natural images [8 , 9] . Moreover , the computational advantage of orientation-selective LN neurons over simple center-surround filters found in the retina would be unclear [10] . There are a number of hypotheses about nonlinear computations in V1 , including normative models like overcomplete sparse coding [11 , 12] or canonical computations like divisive normalization [13 , 14] . The latter has been used to explain specific phenomena such as center-surround interactions with carefully designed stimuli [15–18] . However , to date , these ideas have not been turned into predictive models of spiking responses that generalize beyond simple stimuli—especially to natural images . To go beyond simple LN models for natural stimuli , LN-LN cascade models have been proposed , which either learn ( convolutional ) subunits [19–21] or use handcrafted wavelet representations [22] . These cascade models outperform simple LN models , but they currently do not capture the full range of nonlinearities observed in V1 , like gain control mechanisms and potentially other not-yet-understood nonlinear response properties . Because experimental time is limited , LN-LN models have to be designed very carefully to keep the number of parameters tractable , which currently limits their expressiveness , essentially , to energy models for direction-selective and complex cells . Thus , to make progress in a quantitative sense , recent advances in machine learning and computer vision using deep neural networks ( ‘deep learning’ ) have opened a new door by allowing us to learn much more complex nonlinear models of neural responses . There are two main approaches , which we refer to as goal-driven and data-driven . The idea behind the goal-driven approach is to train a deep neural network on a high-level task and use the resulting intermediate representations to model neural responses [23 , 24] . In the machine learning community , this concept is known as transfer learning and has been very successful in deep learning [25 , 26] . Deep convolutional neural networks ( CNNs ) have reached human-level performance on visual tasks like object classification by training on over one million images [27–30] . These CNNs have proven extremely useful as nonlinear feature spaces for tasks where less labeled data is available [25 , 31] . This transfer to a new task can be achieved by ( linearly ) reading out the network’s internal representations of the input . Yamins , DiCarlo and colleagues showed recently that using deep networks trained on large-scale object recognition as nonlinear feature spaces for neural system identification works remarkably well in higher areas of the ventral stream , such as V4 and IT [32 , 33] . Other groups have used similar approaches for early cortical areas using fMRI [34–36] . However , this approach has not yet been used to model spiking activity of early stages such as V1 . The deep data-driven approach , on the other hand , is based on fitting all model parameters directly to neural data [37–41] . The most critical advance of these models in neural system identification is that they can have many more parameters than the classical LN cascade models discussed above , because they exploit computational similarities between different neurons [38 , 40] . While previous approaches treated each neuron as an individual multivariate regression problem , modern CNN-based approaches learn one model for an entire population of neurons , thereby exploiting two key properties of local neural circuits: ( 1 ) they share the same presynaptic circuitry ( for V1: retina and LGN ) [38] and ( 2 ) many neurons perform essentially the same computation , but at different locations ( topographic organization , implemented by convolutional weight sharing ) [39–41] . While both the goal-driven and the data-driven approach have been shown to outperform LN models in some settings , neither approach has been evaluated on spiking activity in monkey V1 ( see [42 , 43] for concurrent work ) . In this paper , we fill this gap and evaluate both approaches in monkey V1 . We found that deep neural networks lead to substantial performance improvements over older models . In our natural image dataset , goal-driven and data-driven models performed similarly well . The goal-driven approach reached this performance with as little as 20% of the dataset and its performance saturated thereafter . In contrast , the data-driven approach required the full dataset for maximum performance , suggesting that it could benefit from a larger dataset and reach even better performance . Our key finding is that the best models required at least four nonlinear processing steps , suggesting that we need to revise our view of V1 as a Gabor filter bank and appreciate the nonlinear nature of its computations . We conclude that deep networks are not just one among many approaches that can be used , but are—despite their limitations—currently the single most accurate model of V1 computation . We start by investigating the goal-driven approach [23 , 24] . Here , the idea is to use a high-performing neural network trained on a specific goal—object recognition in this case—as a non-linear feature space and train only a simple linear-nonlinear readout . We chose VGG-19 [28] over other neural networks , because it has a simple architecture ( described below ) , a fine increase in receptive field size along its hierarchy and reasonably high classification accuracy . VGG-19 is a CNN trained on the large image classification task ImageNet ( ILSVRC2012 ) that takes an RGB image as input and infers the class of the dominant object in the image ( among 1000 possible classes ) . The architecture of VGG-19 consists of a hierarchy of linear-nonlinear transformations ( layers ) , where the input is spatially convolved with a set of filters and then passed through a rectifying nonlinearity ( Fig 3 ) . The output of this operation is again an image with multiple channels . However , these channels do not represent color—as the three channels in the input image—but learned features . They are therefore also called feature maps . Each feature map can be viewed as a filtered version of its input . The collection of such feature maps serves as input for the next layer . Additionally , the network has five pooling layers , where the feature maps are downsampled by a factor of two by taking the local maximum value of four neighboring pixels . There are 16 convolutional layers that can be grouped into five groups named conv1 to conv5 with 2 , 2 , 4 , 4 , 4 convolutional layers and 64 , 128 , 256 , 512 , 512 output feature maps , respectively , and a pooling layer after each group . We used VGG-19 as a feature space in the following way: We selected the output of a convolutional layer as input features for a Generalized Linear Model ( GLM ) that predicts the recorded spike counts ( Fig 3 ) . Specifically , we fed each image x in our stimulus set through VGG-19 to extract the resulting feature maps Φ ( x ) of a certain layer . These feature maps were then linearly weighted with a set of learned readout weights w . This procedure resulted in a single scalar value for each image that was then passed through a ( static ) output nonlinearity to produce a prediction for the firing rate: r ( x ) = exp [ w T Φ ( x ) + b ] ( 1 ) We assumed this prediction to be the mean rate of a Poisson process ( see Methods for details ) . In addition , we applied a number of regularization terms on the readout weights that we explain later . We first asked which convolutional layer of VGG-19 provides the best feature space for V1 . To answer this question , we fitted a readout for each layer and compared the performance . We measured performance by computing the fraction of explainable variance explained ( FEV ) . This metric , which ranges from zero to one , measures what fraction of the stimulus-driven response is explained by the model , ignoring the unexplainable trial-to-trial variability in the neurons’ responses ( for details see Methods ) . We found that the fifth ( out of sixteen ) layers’ features ( called ‘conv3_1’ , Fig 3 ) best predicted neuronal responses to novel images not seen during training ( Fig 4 , solid line ) . This model predicted on average 51 . 6% of the explainable variance . In contrast , performance for the very first layer was poor ( 31% FEV ) , but increased monotonically up to conv3_1 . Afterwards , the performance again decreased continually up the hierarchy ( Fig 4 ) . These results followed our intuition that early to intermediate processing stages in a hierarchical model should match primary visual cortex , given that V1 is the third processing stage in the visual hierarchy after the retina and the lateral geniculate nucleus ( LGN ) of the thalamus . An important issue to be aware of is that the receptive field sizes of VGG units grow along the hierarchy—just like those of visual neurons in the brain . Incidentally , the receptive fields of units in the best-performing layer conv3_1 subtended approximately 0 . 68 degrees of visual angle , roughly matching the expected receptive sizes of our V1 neurons given their eccentricities between 1 and 3 degrees . Because receptive fields in VGG are defined in terms of image pixels , their size in degrees of visual angle depends on the resolution at which we present images to VGG , which is a free parameter whose choice will affect the results . VGG-19 was trained on images of 224 × 224 px . Given the image resolution we used for the analyses presented above , an entire image would subtend ∼6 . 4 degrees of visual angle ( the crops shown to the monkey were 2 degrees; see Methods for details ) . Although this choice appears to be reasonable and consistent with earlier work [33] , it is to some extent arbitrary . If we had presented the images at lower resolution , the receptive fields sizes of all VGG units would have been larger . As a consequence , the receptive fields of units in earlier layers would match those of V1 and these layers may perform better . If this was indeed the case , there would be nothing special about layer conv3_1 with respect to V1 . To ensure that the choice of input resolution did not affect our results , we performed a control experiment , which substantiated our claim that conv3_1 provides the best features for V1 . We repeated the model comparison presented above with different input resolutions , rescaling the image crops by a factor of 0 . 67 and 1 . 5 . These resolutions correspond to 9 . 55 and 4 . 25 degrees of full visual field for VGG-19 , respectively . While changing the input resolution did shift the optimal layer towards that with matching receptive field sizes ( Fig 5 , first and third row ) , the resolution we had picked for our main experiment yielded the best overall performance ( Fig 5 , second row , third column ) . Thus , over a range of input resolutions and layers , conv3_1 performed best , although conv2_2 at lower resolution yielded only slightly lower performance . The number of predictors given by the convolutional feature space of a large pre-trained network is much larger than the number of pixels in the image . Most of these predictors will likely be irrelevant for most recorded neurons—for example , network units at spatial positions that are not aligned with the neuron’s receptive field or feature maps that compute nonlinearities unrelated to those of the cells . Naïvely including many unimportant predictors would prevent us from learning a good mapping , because they lead to overfitting . We therefore used a regularization scheme with the following three terms for the readout weights: ( 1 ) sparsity , to encourage the selection of a few units; ( 2 ) smoothness , for a regular spatial continuity of the predictors’ receptive fields; and ( 3 ) group sparsity , to encourage the model to pool from a small number of feature maps ( see Methods for details ) . We found that regularization was key to obtaining good performance ( Table 1 ) . The full model with all three terms had the best performance on the test set and vastly outperformed a model with no regularization . Eliminating one of the three terms while keeping the other two hurt performance only marginally . Among the three regularizers , sparsity appeared to be the most important one quantitatively , whereas smoothness and group sparsity could be dropped without hurting overall performance . To understand the effect of the different regularizers qualitatively , we visualized the readout weights of each feature map of our conv3_1-based model , ordered by their spatial energy for each cell , for each of the regularization schemes ( see Fig 6A for five sample neurons ) . Without the sparsity constraint , we obtained smooth but spread-out weights that were not well localized . Dropping the smoothness term—despite performing equally in a quantitative sense—produced sparse activations that were less localized and not smooth . Without any regularization , the weights appeared noisy and one could not get any insights about the locality of the neuron . On the other hand , the full model—in addition to having the best performance—also provides localized and smooth filters that provide information about the neurons’ receptive field and the set of useful feature maps for prediction . Finally , we also observed that only a small number of feature maps was used for each neuron: the weights decayed exponentially and only 20 feature maps out of 256 contained on average 82% of the readout energy ( Fig 6B ) . An alternative form of regularization or inductive bias would be to constrain the readout weights to be factorized in space and features [40] , which reduces the number of parameters substantially . However , the best model with this factorized readout achieved only 45 . 5% FEV ( Table 1 ) , presumably because the feature space has not been optimized for such a constrained readout . Multi-layer feedforward networks have been fitted successfully to neural data on natural image datasets in mouse V1 [38 , 40] . Thus , we inquired how our goal-driven model compares to a model belonging to the same functional class , but directly fitted to the neural data . Following the methods proposed by Klindt et . al [40] , we fitted CNNs with one to five convolutional layers ( Fig 7A; see Methods for details ) . The data-driven CNNs with three or more convolutional layers yielded the best performance , outperforming their competitors with fewer ( one or two ) layers ( Fig 7B ) . We therefore decided to use the CNN with three layers for model comparison , as it is the simplest model with highest predictive power on the validation set . We then asked how the predictive performance of both data-driven and goal driven models compares to previous models of V1 . As a baseline , we fitted a regularized version of the classical linear-nonlinear Poisson model ( LNP; [46] ) . The LNP is a very popular model used to estimate the receptive field of neurons and offers interpretability and convexity for its optimization . This model gave us a good idea of the nonlinearity of the cells’ responses . Additionally , we fit a model based on a handcrafted nonlinear feature space consisting of a set of Gabor wavelets [4 , 47–49] and energy terms of each quadrature pair [6] . We refer to this model as the ‘Gabor filter bank’ ( GFB ) . It builds upon existing knowledge about V1 function and is able to model simple and complex cells as well as linear combinations thereof . Moreover , this model is the current state of the art in the neural prediction challenge for monkey V1 responses to natural images [50] and therefore a strong baseline for a quantitative evaluation . We compared the models for a number of cells selected randomly ( Fig 8A ) . There was a diversity of cells , both in terms of how much variance could be explained in principle ( dark gray bars ) and how well the individual models performed ( colored bars ) . Overall , the deep learning models consistently outperformed the two simpler models of V1 . This trend was consistent across the entire dataset ( Fig 8B and 8D ) . The LNP model achieved 16 . 3% FEV , the GFB model 45 . 6% FEV . The performance of the CNN trained directly on the data was comparable to that of the VGG-based model ( Fig 8C and 8D ) ; they predicted 49 . 8% and 51 . 6% FEV , respectively , on average . The differences in performance between all four models were statistically significant ( Wilcoxon signed rank test , n = 166; family-wise error rate α = 0 . 05 using Holm-Bonferroni method to account for multiple comparisons ) . Note that the one-layer CNN ( mean 34 . 5% FEV , Fig 7 ) structurally resembles the convolutional subunit model proposed by Vintch and colleagues [21] . Thus , deeper CNNs also outperform learned LN-LN cascade models . We next asked whether the improvement in predictive performance afforded by our deep neural network models was related in any way to known tuning properties of V1 neurons such as the shape of their orientation tuning curve or their classification along the simple-complex axis . To investigate this question , we performed an in-silico experiment: we showed Gabor patches of the same size as our image stimulus with various orientations , spatial frequencies and phases ( Fig 9A ) to our CNN model of each cell . Based on the model output , we computed tuning curves for orientation ( Fig 9B ) and spatial phase ( Fig 9D ) by using the set of Gabors with the optimal spatial frequency for each neuron . Based on the phase tuning curves we compute a linearity index ( see Methods ) , which locates each cell on the axis from simple ( linearity index close to one ) to complex ( index close to zero ) . We then asked whether there are systematic differences in model performance as a function of this simple-complex characterization . As expected , we found that more complex cells are explained better by the Gabor filter bank model than an LNP model ( Fig 9C ) . The same was true for both the data-driven CNN and the VGG-based model . However , the simple-complex axis did not predict whether and how much the CNN models outperformed the Gabor filter bank model . Thus , whatever aspect of V1 computation was additionally explained by the CNN models , it was shared by both simple and complex cells . Next , we asked whether there is a relationship between orientation selectivity ( tuning width ) and the performance of any of our models . We found that for cells with sharper orientation tuning , the performance gain afforded by the Gabor filter bank model ( and both CNN-based models ) over an LNP was larger than for less sharply tuned cells ( Fig 9E ) . This result is not unexpected given that cells in layer 2/3 tend to have narrower tuning curves and also tend to be more complex [51 , 52] . However , as for the simple-complex axis , tuning width was not predictive of the performance gain afforded by a CNN-based model over the Gabor filter bank ( Fig 9E ) . Therefore , any additional nonlinearity in V1 computation captured by the CNN models is not specific to sharply or broadly tuned neurons . Our stimulus set contains both natural images as well as four sets of textures generated from those images . These textures differ in how accurately and over what spatial extent they reproduce the local image statistics ( see Fig 1 ) . On the one end of the spectrum , samples from the conv1 model reproduce relatively linear statistics over small regions of a few minutes of arc . On the other end of the spectrum , samples from the conv4 model almost perfectly reproduce the statistics of natural images over larger regions of 1–2 degrees of visual angle , covering the entire classical and at least part of the extra-classical receptive field of V1 neurons . We asked to what extent including these different image statistics helps or hurts building a predictive model . To answer this question , we additionally fit both the data-driven CNN model and the VGG-based model to subsets of the data containing only images from a single image type ( originals or one of four texture classes ) . We then evaluated each of these models on all image types ( Fig 10 ) . Perhaps surprisingly , we found that using any of the four texture statistics or the original images for training lead to approximately equal performance , independent of which images were used for testing the model ( Fig 10 ) . This result held for both the VGG-based ( Fig 10A ) and the data-driven CNN model ( Fig 10B ) . Thus , using the very localized conv1 textures worked just as well for predicting the responses to natural images as did training directly on natural images—or any other combination of training and test set . This result is somewhat surprising to us , as the conv1 textures match only very simple and local statistics on spatial scales smaller than individidual neurons’ receptive fields and perceptually are much closer to noise than to natural images . An interesting corollary of the analysis above is the difference in absolute performance between the VGG-based and the data-driven CNN model when using only a subset of images for training: while the performance of the VGG-based model remains equally high when using only a fifth of the data for training ( Fig 10A ) , the data-driven CNN takes a substantial hit ( Fig 10B , second and following rows ) . Thus , while the two models perform similarly when using our entire dataset , the VGG-based model works better when less training data is available . This result indicates that , for our current experimental paradigm , training the readout weights is not the bottleneck—despite the readout containing a large number of parameters in the VGG-based model ( Table 2 ) . Because we know that only a small number of non-zero weights are necessary , the L1 regularizer works very well in this case . In contrast , the data-driven model takes a substantial hit when using only a subset of the data , suggesting that learning the shared feature space is the bottleneck for this model . Thus collecting a larger dataset could help the data-driven model but is unlikely to improve performance of the VGG-based one . Our goal was to find which model among various alternatives is best for one of the most studied systems in modern systems neuroscience: primary visual cortex . We fit two models based on convolutional neural networks to V1 responses to natural stimuli in awake , fixating monkeys: a goal-driven model , which uses the representations learned by a CNN trained on object recognition ( VGG-19 ) , and a data-driven model , which learns both the convolutional and readout parameters using stimulus-response pairs with multiple neurons simultaneously . Both approaches yielded comparable performance and substantially outperformed the widely used LNP [46] and a rich Gabor filter bank ( GFB ) , which held the previous state of the art in prediction of V1 responses to natural images . This finding is of great importance because it suggests that deep neural networks can be used to model not only higher cortex , but also lower cortical areas . In fact , deep networks are not just one among many approaches that can be used , but the only class of models that has been shown to provide the multiple nonlinearities necessary to accurately describe V1 responses to natural stimuli . Our work contributes to a growing body of research where goal-driven deep learning models [23 , 24] have shown unprecedented predictive performance in higher areas of the visual stream [32 , 33] , and a hierarchical correspondence between deep networks and the ventral stream [35 , 53] . Studies based on fMRI have established a correspondence between early layers of CNNs trained on object recognition and V1 [35 , 54] . Here , with electrophysiological data and a deeper network ( VGG-19 ) , we found that V1 is better explained by feature spaces multiple nonlinearities away from the pixels . We found that it takes five layers ( a quarter of the way ) into the computational stack of the object categorization network to explain V1 best , which is in contrast to the many models that treat V1 as only one or two nonlinearities away from pixels ( i . e . GLMs , energy models ) . Earlier layers of our CNNs might explain subcortical areas better ( i . e . retina and LGN ) , as they are known to be modeled best with multiple , but fewer , nonlinearities already [41] . What are , then , the additional nonlinearities captured by our deep convolutional models beyond those in LNP or GFB ? Our first attempts to answer this question via an in-silico analysis revealed that whatever the CNNs capture beyond the Gabor filter bank model is not specific to the cells’ tuning properties , such as width of the orientation tuning curve and their characterization along the simple-complex spectrum . This result suggests that the missing nonlinearity may be relatively generic and applicable to most cells . There are a few clear candidates for such nonlinear computations , including divisive normalization [55] and overcomplete sparse coding [12] . Unfortunately , quantifying whether these theories provide an equally good account of the data is not straightforward: so far they have not been turned into predictive models for V1 neurons that are applicable to natural images . In the case of divisive normalization , the main challenge is learning the normalization pool . There is evidence for multiple normalization pools , both tuned and untuned and operating in the receptive field center and surround [56] . However , previous work investigating these normalization pools has employed simple stimuli such as gratings [18] and we are not aware of any work learning the entire normalization function from neural responses to natural stimuli . Similarly , sparse coding has so far been evaluated only qualitatively by showing that the learned basis functions resemble Gabor filters [12] . Solving a convolutional sparse coding problem [57] and using the resulting representation as a feature space would be a promising direction for future work , but we consider re-implementing and thoroughly evaluating this approach to be beyond the scope of the current paper . To move forward in understanding such nonlinearities may require developing more interpretable neural networks or methods that provide interpretability of networks , which are an active area of research in the machine learning community . Alternatively , we could build predictive models constrained with specific hard-coded nonlinearities ( such as normalization ) that express our knowledge about important computations . It is also possible that the mechanistic level of circuit components remains underconstrained by function and thus allows only for explanations up to some degree of degeneracy , requiring knowledge of the objective function the system optimizes ( e . g . sparse coding , predictive coding ) . Our results show that object categorization—despite being a relatively impoverished visual task—is a very useful learning objective not only for high-level areas in the ventral stream , but also for a more low-level and general-purpose area like V1 , despite the fact that V1 clearly serves a large number of tasks beyond object categorization . This finding resonates well with results from computer vision , where object categorization has also been found to be an extremely useful objective to learn features applicable to numerous other visual tasks [25] . Our current best models still leave almost half of the explainable variance unexplained , raising the question of how to make further progress . Our finding that the VGG-based model performed equally well with only 20% of the images in the training set suggests that its performance was not limited by the amount of data available to learn the readout weights , which make for the bulk of the parameters in this model ( Table 2 ) . Instead , the VGG-based model appears to be limited by a remaining mismatch between VGG features and V1 computation . This mismatch could potentially be reduced by using features from neural networks trained simultaneously on multiple ethologically relevant tasks beyond object categorization . The data-driven model reached its full performance only with the full training set , suggesting that learning the nonlinear feature space is the bottleneck . In this case , pooling over a larger number of neurons or recording longer from the same neurons should improve performance because most of the parameters are in the shared feature space ( Table 2 ) and this number is independent of the number of neurons being modeled . We conclude that previous attempts to describe the basic computations that different types of neurons in primary visual cortex perform ( e . g . “edge detection” ) do not account for the complexity of multi-layer nonlinear computations that are necessary for the performance boost achieved with CNNs . Although these models , which so far best describe these computations , are complex and lack a concise intuitive description , they can be obtained by a simple principle: optimize a network to solve an ecologically relevant task ( object categorization ) and use the hidden representations of such a network . For future work , combining data- and goal-driven models and incorporating the recurrent lateral and feedback connections of the neocortex promise to provide a framework for incrementally unravelling the nonlinear computations of V1 neurons . All behavioral and electrophysiological data were obtained from two healthy , male rhesus macaque ( Macaca mulatta ) monkeys aged 12 and 9 years and weighing 12 and 10 kg , respectively , during the time of study . All experimental procedures complied with guidelines of the NIH and were approved by the Baylor College of Medicine Institutional Animal Care and Use Committee ( permit number: AN-4367 ) . Animals were housed individually in a large room located adjacent to the training facility , along with around ten other monkeys permitting rich visual , olfactory and auditory interactions , on a 12h light/dark cycle . Regular veterinary care and monitoring , balanced nutrition and environmental enrichment were provided by the Center for Comparative Medicine of Baylor College of Medicine . Surgical procedures on monkeys were conducted under general anesthesia following standard aseptic techniques . To ameliorate pain after surgery , analgesics were given for 7 days . Animals were not sacrificed after the experiments . We performed non-chronic recordings using a 32-channel linear silicon probe ( NeuroNexus V1x32-Edge-10mm-60-177 ) . The surgical methods and recording protocol were described previously [58] . Briefly , form-specific titanium recording chambers and headposts were implanted under full anesthesia and aseptic conditions . The bone was originally left intact and only prior to recordings , small trephinations ( 2 mm ) were made over medial primary visual cortex at eccentricities ranging from 1 . 4 to 3 . 0 degrees of visual angle . Recordings were done within two weeks of each trephination . Probes were lowered using a Narishige Microdrive ( MO-97 ) and a guide tube to penetrate the dura . Care was taken to lower the probe slowly , not to penetrate the cortex with the guide tube and to minimize tissue compression ( for a detailed description of the procedure , see [58] ) . Electrophysiological data were collected continuously as broadband signal ( 0 . 5Hz–16kHz ) digitized at 24 bits as described previously [59] . Our spike sorting methods are based on [60] , code available at https://github . com/aecker/moksm , but with adaptations to the novel type of silicon probe as described previously [58] . Briefly , we split the linear array of 32 channels into 14 groups of 6 adjacent channels ( with a stride of two ) , which we treated as virtual electrodes for spike detection and sorting . Spikes were detected when channel signals crossed a threshold of five times the standard deviation of the noise . After spike alignment , we extracted the first three principal components of each channel , resulting in an 18-dimensional feature space used for spike sorting . We fitted a Kalman filter mixture model [61 , 62] to track waveform drift typical for non-chronic recordings . The shape of each cluster was modeled with a multivariate t-distribution ( df = 5 ) with a ridge-regularized covariance matrix . The number of clusters was determined based on a penalized average likelihood with a constant cost per additional cluster [60] . Subsequently , we used a custom graphical user interface to manually verify single-unit isolation by assessing the stability of the units ( based on drifts and health of the cells throughout the session ) , identifying a refractory period , and inspecting the scatter plots of the pairs of channel principal components . Visual stimuli were rendered by a dedicated graphics workstation and displayed on a 19” CRT monitor ( 40 × 30 cm ) with a refresh rate of 100 Hz at a resolution of 1600 × 1200 pixels and a viewing distance of 100 cm ( resulting in ∼70 px/deg ) . The monitors were gamma-corrected to have a linear luminance response profile . A camera-based , custom-built eye tracking system verified that monkeys maintained fixation within ∼ 0 . 42 degrees around the target . Offline analysis showed that monkeys typically fixated much more accurately . The monkeys were trained to fixate on a red target of ∼ 0 . 15 degrees in the middle of the screen . After they maintained fixation for 300 ms , a visual stimulus appeared . If the monkeys fixated throughout the entire stimulus period , they received a drop of juice at the end of the trial . At the beginning of each session , we first mapped receptive fields . We used a sparse random dot stimulus for receptive field mapping . A single dot of size 0 . 12 degrees of visual field was presented on a uniform gray background , changing location and color ( black or white ) randomly every 30 ms . Each trial lasted for two seconds . We obtained multi-unit receptive field profiles for every channel using reverse correlation . We then estimated the population receptive field location by fitting a 2D Gaussian to the spike-triggered average across channels at the time lag that maximizes the signal-to-noise-ratio . We subsequently placed our natural image stimulus at this location . We used a set of 1450 grayscale images as well as four texturized versions of each image . We used grayscale images to avoid the complexity of dealing with color and focus on spatial image statistics . The texturized stimuli allowed us to vary the degree of naturalness , ranging from relatively simple , local statistics to very realistic textures capturing image statistics over spatial scales covering both classical and at least parts of the extra-classical receptive field of neurons . The images were taken from ImageNet [44] , converted to grayscale and rescaled to 256 × 256 pixels . We generated textures with different degrees of naturalness by capturing different levels of higher-order correlations from a local to a global scale by using a parametric model for texture synthesis [45] . This texture model uses summary statistics of feature activations in different layers of the VGG-19 network [28] as parameters for the texture . The lowest-level model uses only the statistics of layer conv1_1 . We refer to it as the “conv1” model . The next one uses statistics of conv1_1 and conv2_1 ( referred to as conv2 ) , and so on for conv3 and conv4 . Due to the increasing level of nonlinearity of the VGG-19 features and their increasing receptive field sizes with depth , the textures synthesized from these models become increasingly more natural ( see Fig 1 and [45] for more examples ) To synthesize the textures , we start with a random white noise image and iteratively refine pixels via gradient descent such that the resulting image matches the feature statistics of the original image [45] . For displaying and further analyses , we cropped the central 140 pixels of each image , which corresponds to 2 degrees of visual angle . The entire data set contains 1450 × 5 = 7250 images ( original plus synthesized ) . During each trial , 29 images were displayed , each for 60 ms , with no blanks in between ( Fig 1B ) . We chose this fast succession of images to maximize the number of images we can get through in a single experiment , resulting in a large training set for model fitting . The short presentation times also mean that the responses we observe are mainly feedforward , since feedback processes take some time to be engaged . Each image was masked by a circular mask with a diameter of 2 degrees ( 140 px ) and a soft fade-out starting at a diameter of 1 degree: m ( r ) = { 1 if 0 < r < 0 . 5 0 . 5cos ( π ( 2 r - 1 ) ) + 0 . 5 if 0 . 5 ≤ r < 1 0 otherwise ( 2 ) Images were randomized such that consecutive images were not of the same type or synthesized from the same image . A full pass through the dataset took 250 successful trials , after which it was traversed again in a new random order . Images were repeated between one and four times , depending on how many trials the monkeys completed in each session . We recorded a total of 307 neurons in 23 recording sessions . We did not consider six of these sessions , for which we did not obtain enough trials to have at least two repetitions for each image . In the remaining 17 sessions , we quantified the fraction of total variance of each neuron attributable to the stimulus by computing the ratio of explainable and total variance ( grey bars in Fig 8 ) : Var [ y ] - σ noise 2 Var [ y ] ( 3 ) The explainable variance is the total variance minus the variance of the observation noise . We estimated the variance of the observation noise , σ noise 2 , by averaging ( across images ) the variance ( across repetitions ) of responses to the same stimulus: σ noise 2 = E j [ Var i [ y i | x j ] ] , ( 4 ) where xj is the jth image and yi the response to the ith repetition . We discarded neurons with a ratio of explainable-to-total variance ( see Eq 3 ) smaller than 0 . 15 , yielding 166 isolated neurons ( monkey A: 51 , monkey B: 115 ) recorded in 17 sessions with an average explainable variance of 0 . 285 . Monkey A had only sessions with two repetitions while Monkey B had four repetitions per image . All images were contrast-matched before displaying them on the screen . To do so , we rescaled the pixel intensities of all images such that the central , unmasked 1° ( 70 pixels ) of each image had the same mean and standard deviation . We set the mean to 128 ( same as the gray background ) and the standard deviation to the average standard deviation across images . Any pixels falling outside the range of [0 , 255] after this procedure were cropped to this range . Prior to model fitting , we additionally cropped the central 80 pixels ( 1 . 1° ) of the 140-pixel ( 2° ) images shown to the monkey . For most of the analyses presented in this paper , we sub-sampled these crops to half their size ( 40 × 40 ) and z-scored them . For the input resolution control ( Fig 5 ) , we resampled with bicubic interpolation the original 80 × 80 crops to 60 × 60 , 40 × 40 , and 27 × 27 for scales 1 . 5 , 1 , 0 . 67 , respectively . Our proposed model consists of two parts: feature extraction and a generalized linear model ( GLM; Fig 3 ) . The features are the output maps Φ ( x ) of convolutional layers of VGG-19 [28] to a stimulus image x , followed by a batch normalization layer . We perform this normalization to ensure that the activations of each feature map have zero mean and unit variance ( before ReLU ) , which is important because the readout weights are regularized by an L1 penalty and having input features with different variances would implicitly apply different penalties on their corresponding readout weights . We fit a separate GLM for each convolutional layer of VGG-19 . The GLM consists of linear fully connected weights wijk for each neuron that compute a dot product with the input feature maps Φijk ( x ) , a static output nonlinearity f ( also known as the inverse of the link function ) , and a Poisson noise model used for training . Here , i and j index space , while k indexes feature maps ( denoted as depth in Fig 3 ) . The spiking rate of a given neuron r will follow: r ( x ) = f ( ∑ Φ i j k ( x ) w i j k + b ) ( 5 ) Additionally , three regularization terms were applied to the weights: Considering the recorded image-response pair ( x , y ) for one neuron , the resulting loss function is given by: L = - ∑ ylogr ( x ) + r ( x ) + L sparse + L Laplace + L group ( 9 ) where the sum runs over samples ( image , response pairs ) . We fit the model by minimizing the loss using the Adam optimizer [63] on a training set consisting of 80% of the data , and reported performance on the remaining 20% . We cross-validated the hyperparameters λsparse , λLaplace , λgroup for each neuron independently by performing a grid search over four logarithmically spaced values for each hyperparameter . The validation was done on 20% of the training data . The optimal hyperparameter values obtained on the validation set where λLaplace = 0 . 1 , λsparse = 0 . 01 , λgroup = 0 . 001 . When fitting models , we used the same split of data for training , validation , and testing across all models . We followed the results of [40] and use their best-performing architecture that obtained state-of-the-art performance on a public dataset [38] . Like our VGG-based model , this model also consisted of convolutional feature extraction followed by a GLM , the difference being that here the convolutional feature space was learned from neural data instead of having been trained on object recognition . The feature extraction architecture consisted of convolutional layers with filters of size 13 × 13 px for the first layer and 3 × 3 px for the subsequent layers . Each layer had 32 feature maps ( Fig 7A ) and exponential linear units ( ELU [64] ) E L U ( x ) = { x if x > 0 e x p ( x ) - 1 if x ≤ 0 ( 10 ) as nonlinearities with batch normalization [65] to facilitate training in between the layers . As in the original publication [40] , we regularized the convolutional filters by imposing smoothness constraints on the first layer and group sparseness on the subsequent layers . A notable difference to our VGG-based GLM is that here the readout weights are factorized in space and feature maps: w i j k = u i j v k , ( 11 ) where uij is a spatial mask and vk a set of feature pooling weights . We fitted models with increasing number of convolutional layers ( one to five ) . We found that optimizing the final nonlinearity , f ( x ) , of each neuron was important for optimal performance of the data-driven CNN . To do so , we took the following approach: we split f ( x ) into two components: f ( x ) = h ( x ) g ( x ) ( 12 ) where g ( x ) is ELU shifted to the right and up by one unit ( to make it non-negative—firing rates are non-negative ) : g ( x ) = E L U ( x - 1 ) + 1 ( 13 ) and h is a non-negative , piecewise linear function: h ( x ) = e x p ( ∑ i = 1 n α i t i ) ( 14 ) Here , αi are parameters learned jointly with the remaining weights of the network and the ti are a set of ‘tent’ basis functions to create a piecewise linear function with interpolation points xi = −3 , −2 . 82 , … , 6 ( i . e . Δx = 0 . 18 ) : t i = min ( max ( 0 , x - x i - 1 Δ x ) , max ( x i + 1 - x Δ x ) ) ( 15 ) We regularize the output nonlinearity by penalizing the L2 norm of the first and second discrete finite differences of αi to encourage h to be close to 1 and smooth: L o u t = λ o u t ( ∑ i = 2 n + ( α i - α i - 1 ) 2 + ∑ i = 2 n - 1 ( 2 α i - α i - 1 - α i + 1 ) 2 ) ( 16 ) Note that we applied this optimization of the output nonlinearity only to the data-driven model , as doing the same for the VGG-based model did not improve performance . One potential reason for this difference is that the VGG-based model has a much larger number of feature maps ( 256 for layer conv3_1 ) that each neuron can pool from . We implemented a simple regularized LNP Model [46] . This model is fitted for each neuron separately and consists of two simple stages: The first one is a linear filter w with the same dimensions as the input images . The second is a pointwise exponential function as nonlinearity that converts the filter output into a non-negative spike rate . The LNP assumes spike count generation through a Poisson process , so we minimize a Poisson loss ( negative log-likelihood ) to obtain the kernels of each neuron ( see first term of Eq 17 below ) . Additionally , we imposed two regularization constraints that we cross-validated: smoothness ( Eq 7 ) and sparsity ( Eq 6 ) . With the same M image-response pairs ( x , y ) of the training set that we used for all other models , we optimized the following loss function: L L N P = ∑ i = 1 M [ w T x i - y ilog ( w T x i ) ] + L s p a r s e ( w ) + L l a p l a c e ( w ) ( 17 ) Varying versions of the Gabor filter bank model ( GFB ) have been used in classical work on system identification [22 , 47 , 48 , 66] . This model convolves the image with quadrature pairs of Gabor filters with varying scales , frequencies , aspect ratios , and orientations . Each quadrature pair consists of an ‘even’ ( cosine/symmetric ) and an ‘odd’ ( sine/antisymmetric ) Gabor filter and produces three feature maps: the results of the convolution with the two filters ( ‘even’ and ‘odd’ features ) and an ‘energy’ feature , which is the spectral power of each pair ( i . e . sum of the squares ) . Thus , this model allows for modeling simple and complex cells and linear combinations thereof . The Gabor filters obeyed the following equations with x and y representing spatial dimensions: g σ , f , γ , θ , φ ( x ′ , y ′ ) = exp [ - x ′ 2 + γ 2 y ′ 2 2 σ 2 ]cos ( 2 π f x ′ + φ ) , ( 18 ) where [ x ′ y ′ ] = [ cosθ - sin θ sin θ cosθ ] [ x y ] ( 19 ) The standard deviation ( σ ) represents the scale of the Gaussian envelope , the aspect ratio ( γ ) specifies the ellipticity of the envelope , the spatial frequency ( f ) quantifies the number of sinusoidal cycles divided by the width of the Gaussian aperture ( ∼4σ ) , The sinusoidal grating is determined by an orientation ( θ ) and phase ( φ ) . To form quadrature pairs we set φ to 0 and π/2 for even and odd filters , respectively . We set the kernel size of every Gabor filter to the minimum of the input image size and the closest integer to 4σ/γ for both spatial dimensions . To compute the even and odd feature maps , we convolved the input image with each Gabor filter using strided convolutions ( ⊗ ) with stride s: E σ , f , γ , θ= I ⊗ g σ , f , γ , θ , φ = 0 ( 20 ) O σ , f , γ , θ= I ⊗ g σ , f , γ , θ , φ = π / 2 ( 21 ) We then computed the energy features as follows: A σ , f , γ , θ = ( E σ , f , γ , θ ) 2 + ( O σ , f , γ , θ ) 2 ( 22 ) The full feature space of this model ΦGabor ( x ) is a concatenation of triplets of even , odd , and energy features of every Gabor filter . The filter bank consisted of Ns filter sizes , Nf spatial frequencies per size , Nθ orientations and Nγ aspect ratios . The spatial frequencies depended on the size of the envelope: fn = n/4σ with n = 1 , … , Nf , which means for n = 3 we used Gabors with 1 , 2 and 3 cycles . Aspect ratios ranged from 0 . 5 to 1 with equal spacing , except for Nγ = 1 where we used an aspect ratio of γ = 1 . As for the GLM with VGG features , we fit a linear readout on top of this feature space ( Eq 5 ) , followed by a shifted ELU output nonlinearity ( Eq 13 ) . To train the model , we minimized a Poisson loss and regularized the readout weights to be sparse ( i . e . first two terms in Eq 9 ) ; we did not enforce smoothness or group sparsity here , as they mainly improve interpretability but do not affect performance . We determined the hyperparameters of the Gabor filter bank by running a search over a number of parameter combinations and evaluating the performance of each model on the validation set . We converged to the following values: three different sizes ( Ns = 3: 6 px/0 . 17° , 11 px/0 . 31° and 21 px/0 . 60° ) , three different spatial frequencies ( Nf = 3: f = 1 , 2 , 3 ) per size , one aspect ratio ( Nγ = γ = 1 ) , eight orientations ( Nθ = 8 ) , convolutional stride of 6 for all filters , and L1 regularization parameter α = 0 . 05 . Notably , including multiple different aspect ratios did not improve performance , presumably because it increased the dimensionality of the feature space which harmed generalization . The parameters we fit for each of the models belong either to a shared set for all neurons ( the core ) , or are specific to each neuron ( the readout ) . Table 2 shows the number of parameters for each of the models and how many belong to either core or readout . For both the LNP and GFB models , we learn only a readout from a fixed feature space for each neuron plus a bias . For the LNP we learn one channel of pixel intensities ( 40 × 40 + 1 ) . For the GFB model , we have for each size Nf Nθ channels ( 3 × 8 = 24 ) , and each filter produced a feature output of size ⌊1 + ( 40 − size ) /stride⌋ . With Ns = 3 we got sizes 6 , 11 , and 21 so the output features have size 6 , 5 , and 4 , respectively . Since each Gabor filter quadrature pair produces odd , even , and energy feature spaces , the total dimensionality is 3 × 24 × ( 62 + 52 + 42 ) + 1 = 5545 . For the three-layer CNN , we have 32 channels in all layers ( 32 × 3 biases ) and filters with sizes 13 × 13 × 32 , 3 × 3 × 32 × 32 , and 3 × 3 × 32 × 32 , resulting in 23 , 963 core parameters . The output feature space for an image is 28 × 28 × 32 ( reduced from 40 × 40 due to the padding of the convolutions: no padding in first layer , zero padding in second and third ) . With a factorized readout and a bias , the readout per neuron is then 28 × 28 + 32 plus a bias . In addition , our point-wise output nonlinearity has 50 parameters . Thus , overall we have 867 readout parameters per neuron for this CNN model . For the VGG-based model , although we do not learn the feature space , we do learn batch normalization parameters at the output of the last convolutional layer . For the model that used conv3_1 ( 256 feature channels ) this means learning scale and bias parameters common to all neurons: 2 × 256 = 512 for the core . For a 40 × 40 input , the output of the feature space is 10 × 10 × 256 ( due to downsampling twice via max pooling ) . Here , we learn a dense readout and a bias , so the readout per neuron has 10 × 10 × 256 + 1 = 25 , 601 parameters . We measured the performance of all models with the fraction of explainable variance explained ( FEV ) . That is , the ratio between the variance accounted for by the model ( variance explained ) and the explainable variance ( numerator in Eq 3 ) . The explainable variance is lower than the total variance , because observation noise prevents even a perfect model from accounting for all variance . We compute FEV as F E V = 1 - 1 N ∑ ( y - y ^ ) 2 - σ noise 2 V a r [ y ] - σ noise 2 , ( 23 ) where y ^ represents the model predictions , y the observed spike counts , and the level of observation noise , σ noise 2 is defined in Eq 4 above . We implemented all models in TensorFlow [67] . We optimized them with Adam [63] using mini-batches of size 256 , and early stopping: we evaluated performance on the validation set every 100 training steps , and after ten iterations of no improvement , we decayed the learning rate by a factor of three and repeated this three times . The learning rate at the beginning of the optimization was cross-validated for the goal-driven models and set to 1e-4 for the others as this value always worked best .
Predicting the responses of sensory neurons to arbitrary natural stimuli is of major importance for understanding their function . Arguably the most studied cortical area is primary visual cortex ( V1 ) , where many models have been developed to explain its function . However , the most successful models built on neurophysiologists’ intuitions still fail to account for spiking responses to natural images . Here , we model spiking activity in primary visual cortex ( V1 ) of monkeys using deep convolutional neural networks ( CNNs ) , which have been successful in computer vision . We both trained CNNs directly to fit the data , and used CNNs trained to solve a high-level task ( object categorization ) . With these approaches , we are able to outperform previous models and improve the state of the art in predicting the responses of early visual neurons to natural images . Our results have two important implications . First , since V1 is the result of several nonlinear stages , it should be modeled as such . Second , functional models of entire visual pathways , of which V1 is an early stage , do not only account for higher areas of such pathways , but also provide useful representations for V1 predictions .
You are an expert at summarizing long articles. Proceed to summarize the following text: The objective of this paper is to report evaluated observations from survey records captured through a cross-sectional observational study regarding canine populations and dog owners in rabies enzootic KwaZulu-Natal province , South Africa . Our aim was to evaluate respondent knowledge of canine rabies and response to dog bite incidents towards improved rabies control . Six communities consisting of three land use types were randomly sampled from September 2009 to January 2011 , using a cluster design . A total of 1992 household records were analyzed using descriptive statistics and regression modeling to evaluate source of rabies knowledge , experiences with dog bites , and factors affecting treatment received within respective households that occurred within the 365 day period prior to the surveys . 86% of the population surveyed had heard of rabies . Non-dog owners were 1 . 6 times more likely to have heard of rabies than dog owners; however , fear of rabies was not a reason for not owning a dog . Government veterinary services were reported most frequently as respondent source of rabies knowledge . Nearly 13% of households had a member bitten by a dog within the year prior to the surveys with 82% of the victims visiting a clinic as a response to the bite . 35% of these clinic visitors received at least one rabies vaccination . Regression modeling determined that the only response variable that significantly reflected the likelihood of a patient receiving rabies vaccination or not was the term for the area surveyed . Overall the survey showed that most respondents have heard of dog associated rabies and seek medical assistance at a clinic in response to a dog bite regardless of offending dog identification . An in-depth study involving factors associated within area clinics may highlight the area dependency for patients receiving rabies post exposure prophylaxis shown by this model . Rabies kills tens of thousands of people in developing countries each year , and it is estimated that almost half of global rabies incidences occur in Africa [1]–[2] . However , one major factor compounding the problems of rabies is a high probability of disease underreporting . Studies in Tanzania , for example , indicated that there are ten cases for every one officially reported [1] . Once clinical signs of encephalitis become apparent , human rabies is virtually untreatable [3] . Although there has been at least one bona fide case of survival using intensive care treatment , much remains to be understood about factors determining the outcome of such treatment procedures while the required facilities and cost of procedure put such interventions outside the reach of those countries where dog and human rabies is most prevalent [4] . Reliance on proper wound management , and timely post exposure prophylaxis ( PEP ) [appropriate administration of vaccine and immunoglobulin] , is crucial to the prevention of human rabies in exposed persons . From the above perspective , rabies deaths in Africa are linked to ignorance and poverty . People from rural areas and young children , lacking knowledge of rabies and thus the requirement and urgency of PEP , are most frequently affected [5] . In just one example , from a relatively progressive African state , viz . South Africa , it was shown that half of the laboratory confirmed cases from 2008 did not seek any medical intervention after dog bite exposure [6] . Although rabies PEP is free of charge for bite victims in South Africa , the full post exposure treatment with vaccine and immunoglobulin G costs the South African health system more than USD $152 per individual [7] . KwaZulu-Natal ( KZN ) , one of nine provinces , is located on the east coast of South Africa ( Figure 1 ) with an area of 94 , 361 km2 and a human population last estimated at 10 , 819 , 130 with a growth rate of 1 . 2% for all races of people [8] . KZN contains just over 21% of the total human population for South Africa despite the province being only 7 . 7% of the country's land mass . Over 84% of the population is black African , mostly of Zulu cultural origin [8] . It is thought that canine rabies spread to KZN from adjacent Mozambique during the 1960's . Although the disease was then eradicated through the use of mass vaccination campaigns and dog control , rabies was reintroduced in the mid 1970's and has been enzootic to KZN ever since [9] . Historically , most of the human rabies cases in South Africa over past decades have been from KZN . Many dogs in South Africa are not immunized against rabies despite laws mandating vaccination , and KZN is no exception . High dog population turnover , lax enforcement of government regulations and interruptions in vaccination campaigns are all likely factors that contribute to low rabies immunization coverage . In this regard , rabies does not generally appear to enjoy appropriate public health priority in African countries . Poor reporting and poor surveillance , resulting in an apparent lack of political commitment to rabies control , seems to be common practice . In this study it was our objective to better quantify issues such as the above , for the particular region of KZN . Here we analyze the responses to dog bites in an area that has been dog rabies-enzootic for decades , and where control has been attempted for an equal period of time . A better understanding of the societies and practices involved , including knowledge and awareness , would be crucial in improving a disease situation that has been ongoing for the past 40 years . As part of a comprehensive rabies control program , we have queried a representative sample of several population segments of the KZN province about rabies knowledge , interest in enforcement of animal control laws and in community based surveillance . From September 2009 through January 2011 , household surveys were conducted in six different communities across KZN province , covering three land use types: rural , urban and peri-urban ( Figure 1 ) . Distribution of the 1992 households completing the surveys was 52% rural , 33% urban and 15% peri-urban . Rabies was enzootic in all areas , with the exception of the peri-urban community of Wembezi . Affluent urban and suburban areas where people keep dogs in confined spaces most likely have lower rabies risk due to fewer affective contacts between animals and easier access to veterinary services and were therefore excluded [10] . Poorer urban townships and rural villages most frequently represent the areas from where canine rabies is reported ( KZNDAERD unpublished data ) . The study areas were selected with the assistance of the KZN Department of Agriculture and Environmental Affairs and Rural Development ( KZNDAERD ) , Veterinary Services division . Simple random sampling and systematic surveys are difficult in developing countries due to logistical and sometimes adverse cultural reasons [11] . Random sampling using a cluster or ‘area’ design was used because homesteads in rural areas are not numbered and informal housing settlements within townships frequently are arranged haphazardly [12] . Based upon World Health Organization guidelines [13] the questionnaires were composed of two parts; a household survey for collecting demographics and community opinions , and an individual dog survey for descriptive statistics of the owned dog population . Though the primary objective of the surveys was to gain provincial dog demographics , respondents were asked exploratory questions regarding their knowledge of rabies , histories of dog bites and response to those incidents in consideration of possible future studies . Respondents were also queried about their interest in animal-law enforcement , animal ownership regulations and community based surveillance . The surveys were translated into isiZulu and then back translated to English before being piloted in a township with similar human demographics and a history of canine rabies . The survey tool was refined prior to use in this study and the final questionnaires were well received by both the surveyors and the target population with no further improvements or modifications required . KZNDAERD Animal Health Technicians and students , Department of Health workers , Environmental Health workers and SPCA employees were trained to perform the surveys . Surveyors were instructed to introduce themselves to household respondents and explain the purpose of the questionnaire prior to asking their permission to carry out the survey . Surveyors wore name tags which had ‘Rabies Surveillance Team’ printed in large block letters with a bright red border and the Departmental insignia as an identification aid . Prior permission to conduct the surveys had been sought from municipal counselors . All interviews were conducted between the hours of 9 am and 3 pm . The data from each area was entered into a Microsoft Excel spreadsheet and then imported into SAS version 9 . 3 ( SAS Institute , Inc . , Cary , North Carolina , USA ) . Descriptive statistics were generated , and cross tabulations calculating Pearson's Chi Square ( χ2 ) were performed in tests of association . A logistic regression model was built using SAS to predict outcomes of human dog bite victims receiving rabies PEP [14] . The study was approved by the University of Pretoria , Veterinary Faculty Research Committee at Onderstepoort campus . An application for the non-experimental use of animals was approved by the Animal Use and Care Committee from the University of Pretoria . Interviewed subjects were provided informed consent orally in their native language as was stated in the research proposal approved by the Research Committee . The purpose of the survey interview was explained to each participant by the interviewer , who could either accept or decline to participate in the survey . If the respondent declined to be interviewed it was marked at the top of the survey form , whereas those who agreed to be interviewed had a third party witness to this verbal agreement , and the interview was continued . A total of 1992 households consisting of 13 , 756 people ( range 1–34 , median = 6 ) completed the surveys within the three targeted community types . Surveys were answered by a person defined as head of the household in 68% ( 1361/1992 ) of the cases across the province ( range 63–76% ) . The sex of the respondent was not recorded . Of the remaining cases , 11 comprised interviews with children under the age of fourteen years of age in the presence of an older relative who consented to the child answering questions . Another 183 children over the age of fourteen years were interviewed at homes where adults were not present . In 435 surveys , an adult other than the head of the household responded to interview questions . In 2 cases the category of the respondent was missing . Eighty-six percent of the population ( 1716/1992 ) surveyed across the province had heard of the disease called rabies ( Figure 2 ) . No attempt was made to evaluate the individual's depth of rabies knowledge . Some respondents stated that they did not truly know the source of rabies or how to prevent it . However , it was clear that some respondents knew that vaccination of dogs was important to the safety of people in the community based upon their remarks . There was no significant relationship found between area surveyed and respondent knowledge of rabies ( χ2 = 10 . 864 , df = 5 , p = 0 . 541 ) . When surveyed areas were grouped by land use , 81% of the peri-urban society had some knowledge of rabies , whereas 87% of rural and 88% of urban citizens had knowledge of rabies . Surprisingly , non-dog owners were 1 . 6 times more likely to have heard of rabies compared with dog owners . No respondents stated that fear of rabies infection was a reason for not owning a dog . Persons who responded that they did have some knowledge of rabies were further queried as to the source of their knowledge . Government Veterinary Services Animal Health Technicians have the role of visiting schools and educating children about rabies . Among other resources , they utilize a government prepared video entitled “If I Only Knew” and various informative pamphlets discussing rabies . We found that schools and school children accounted for 19% of the population's knowledge source across the province . However , there was not a significant relationship between the presence of school children and knowledge of rabies in individual households ( χ2 = 0 . 027 , df = 1 , p = 0 . 868 ) . Less than two percent of the surveyed population indicated that they have acquired rabies knowledge from the local health clinic . Since one objective of our research was to understand where citizens were able to receive valuable information about rabies , any viable source was noted and utilized as an element of feedback for the development of future programs employed by the government departments responsible for human and animal health . Public print and broadcast media were cited highest among non-dog owners as their source of rabies knowledge , whereas government sponsored campaigns were most frequently cited from dog owners ( Figure 3 ) . 12 . 7% ( 95% CI 11 . 3–14 . 2 ) of the 1992 households in the areas surveyed reported that at least one member of the household had been bitten by a dog in the past year . Age of bite victim was not recorded . The lowest incident rate was in Esikhawini ( urban ) and the highest incidence occurred in Wembezi ( peri-urban ) . Across KZN , significantly more people who did not own dogs had been bitten by a dog than those who did own a dog ( χ2 = 9 . 477 , df = 1 , p = 0 . 002 ) . Among victims who were dog owners the number of dogs owned did not make a difference in dog bite incidence . Although 33% ( 667/1992 ) of households reported feeding of dogs that they did not own ( on their property ) , there was not a significant relationship between being bitten by a dog and feeding other dogs on the property ( χ2 = 3 . 424 , df = 1 , p = 0 . 064 ) . As a follow up question , households with recent dog bite victims were asked if they knew the aberrant dog involved in the incident ( Figure 4 ) . Identification of the offending dog was missing in 10% ( 26/253 ) of recorded cases . In 71% of records where the dog was identified , the neighbor's dog had bitten the victim . Only 12% of victims had been bitten by their own dog and 17% of victims had been bitten by a dog with which they were unfamiliar . Unknown dogs were referred to as strange dogs rather than stray dogs , as the animal could be owned but unrestricted . Historically there have been concerns about people in rural areas visiting traditional healers rather than attending a clinic after a dog bite event . Sudarshan et al [15] showed that 60% of dog bite victims in India who succumbed to rabies had sought some kind of indigenous treatment following the incident , receiving either magico-religious practices or some kind of herbal therapy . After a recent emerging rabies epidemic in the Limpopo province of South Africa ( 2005–2006 ) , it was established that 20% of the fatal human rabies case patients saw a traditional healer prior to attending hospital [16] . In this survey in KZN , less than 2% ( 4/253 ) of dog bite victims , all of whom were from rural areas , reported resultant visits to a traditional healer . With regard to the washing of bite wounds as a first response to incident , only 8% of victims mentioned washing the wound . We found that 56% ( 1115/1992 ) of domiciles visited in our surveys had either a pit latrine or no toilet facilities , indicative of a lack of running water at the household level . In some rural areas a public tap was available some distance from the house . In other rural areas , people made use of rivers or streams for daily water . In the six areas surveyed , over 80% ( 207/253 ) of victims visited a clinic as a response to dog bite incident except in the rural area of St . Chad's ( Figure 5 ) . This area alleged the highest rate of rabies knowledge ( 88% ) ( Figure 2 ) , but had the least number of visits to the clinic ( 54% ) as a response to bite incidence . St . Chad's has both a community health center and close access to neighboring community clinics and hospitals . Detailed questions that would uncover the decisions made by bite victims , actions taken and reasons for those actions were not asked . Those households with victims who had visited a clinic in response to a dog bite were asked what injections they had received ( Table 1 , Figure 6 ) . Twenty-four percent ( 50/207 ) of clinic visitors reported receiving no injections . Thirty-four percent ( 70/207 ) of respondents did not know the extent of treatment received , as it was either not explained to them , they could not remember or they did not attend the clinic with the victim . Thirty-five percent ( 73/207 ) of persons visiting the clinic received rabies vaccine , 5% received tetanus only and 1 . 4% received both rabies and a tetanus vaccine . Those victims who said they received rabies vaccine were not asked if they returned to the clinic to complete the World Health Organization recommended ( Essen schedule in the case of South Africa ) four injection series or if they received immunoglobulin in the case of category III bites [17] . Severity and location of bite wounds was not queried of respondents . An effort was made to determine if there was an association between those clinics where patients had received rabies vaccine and the area surveyed , whether the victim owned dogs , respondent knowledge of rabies and identification of offending dog . Fifty-two responses ( 20% ) were deleted from the model due to missing values for either the response or explanatory variables . In the final logistic regression model only the area surveyed significantly contributed to human rabies vaccination outcome ( Table 2 ) . The model appeared to fit the data ( Somer's D = 0 . 395 ) . The urban township of Esikhawini was the area with the most dog bite victims receiving rabies vaccine , while victims in urban Umlazi Township received the least ( Table 3 ) . Respondents were queried if they were interested in learning more about illnesses that could be shared between people and animals . Ninety-four percent of the population surveyed across the province ( 1865/1992 ) said they would be interested in gaining information about zoonotic disease potential in their community . The urban area of Umlazi , where only 88% of the respondents answered agreeably , stood out as the only area where there are a significant number of respondents who were disinterested in zoonoses ( χ2 = 30 . 581 , df = 10 , p = 0 . 001 ) . Respondents were asked if they would , as witnesses , be interested in reporting ill dogs , strange behavior or dog bite incidents occurring in their communities . The majority of respondents across all communities were interested in reporting such sightings; however , the peri-urban and urban communities had significantly less interest than the rural areas in community based reporting ( χ2 = 22 . 120 , df = 5 , p = 0 . 000 ) ( Figure 7 ) . Respondents answering in the affirmative were further queried as to whom in the community they would want to report these incidents . Nearly 40% identified government veterinary services when considering reporting sick dogs and possible rabies cases ( Figure 8 ) . Other parties mentioned were the local clinic , a teacher , the dog's owner , or SPCA . However , community members regularly stated that despite their desire to report , they had no contact information for either veterinary services or the SPCA . Other than dogs , 2 potential rabies maintenance hosts present in KZN are mongooses and jackals . Bat eared foxes , which maintain rabies in the western provinces of South Africa [18] , are rarely seen in KZN . Questioning respondents about sightings in their community served as a screening tool for the possibility of further studies concerning wildlife and the spread of rabies in KZN . Overall , less than 22% of the respondents across the province encountered either of these wildlife species in their communities ( Figure 9 ) . The rural tribal authority area outside of Pongola has dense flora and is located on the Swaziland border which could explain why there were so many more jackal sightings in this rural area versus any other . Despite South Africa possessing laws requiring vaccination and licensure of dogs , these regulations are rarely enforced . Respondents were asked if they desire law enforcement regarding removal of stray or unsupervised dogs ( Figure 10 ) . There was a significant difference in area type and desire for animal control law enforcement , with the least concern reported from the rural areas ( p = 0 . 0001 ) . Surveys respondents were asked if they desired laws that would limit the number of dogs that one household could own ( Figure 11 ) . The number of dogs owned by the dog owning households surveyed was similar between the rural and peri-urban households with an average of 2 . 47 dogs per dog owning household ( range 2 . 16–2 . 64 ) . Urban dog owning households had fewer dogs with the average being 1 . 66 ( range 1 . 64–1 . 68 ) . Some households in rural areas were recorded as owning up to 19 dogs . There was a significant difference between rural areas and the urban/peri-urban areas in desire for limitations on the number of dogs one household could own ( p = 0 . 0001 ) . The results from this survey indicate that 86% of persons in high risk canine rabies areas of KwaZulu-Natal have at least heard of the disease even if they are unaware of the details surrounding transmission and consequences of exposure . The long history of enzootic canine rabies in the province and the continuous efforts put forth by the KZNDAERD- Government Veterinary Services appear to contribute the most ( 33% ) to this public awareness . Although 100% would be ideal , an 86% knowledge rate is better than was reported from other studies . In dog rabies enzootic Zimbabwe , for example , 74% of pet owners interviewed in Harare animal clinics were aware that rabies was transmitted to people by dogs [19] . Zimbabwean respondents also reported gaining information about other zoonotic diseases from their veterinarian . Respondents in KZN do not have local veterinary clinics as a resource from which to gain this knowledge . In the current study , the peri-urban working community of Wembezi had the lowest rate of rabies knowledge at 81% . This is interesting in that it is also the only community identified as being free of canine rabies for greater than 10 years per surveillance records from KZNDAERD . Thirty-nine percent of households in Wembezi owns at least one dog and has an estimated dog population of 2 , 916 ( Hergert unpublished data ) . Calculated dog density figures for Wembezi are similar to the urban areas surveyed in this study , where a slightly higher level of rabies knowledge was recorded . Non-dog owners were 1 . 6 times more likely to have heard of rabies versus dog owners . People who do not own dogs are gaining information about rabies from media sources , which had a value of almost 30% from respondents in all areas . This is similar to findings in the developed world – e . g . in Texas , USA 43% of non-pet owners reported learning about zoonotic diseases from the media or newspaper [20] . Media sources for rabies information may actually be viewed by both the dog owning and non-dog owning public; however , dog owners may be more likely to respond that their source of rabies knowledge was from government vaccination campaigns , as they would be attending these events . Therefore , a certain amount of bias may weigh towards government campaigns as a source for dog owners due to their familiarity . Understanding why more non-dog owners report knowing about rabies versus dog owners is unclear from this survey and would require further study . South African Government Veterinary Services has the task of informing people about rabies through vaccination campaigns and schools . When these two reported sources of knowledge are combined it is evident that Veterinary Services is responsible for 52% of the information gained by both the dog and non-dog owning public . However , there was not a significant relationship between household knowledge of rabies and knowledge source originating from schools . Veterinary Services of KZN might take into consideration when planning educational campaigns in their communities , that schools are not heavily targeted . Eighty-two percent of interviewed households contained school aged children; therefore , schools appear to be a viable outlet for the dissemination of rabies information . Human health clinics were reported as a knowledge source in less than 2% of responses . This result may support the findings from Francophone countries of Africa where medical authorities and health practitioners are reported to be under educated in the perils of rabies [5] . In Texas , USA , the family doctor was reported as the source of zoonotic disease information in only 6% of both pet and non-pet owning households [20] . Doctors were also indicated well below veterinarians and the media as a source of disease information in Zimbabwe [19] . Detailed examination into what transpires in KwaZulu-Natal clinics for dog bite case patients should be explored in the face of a One Health environment . Twelve percent of the households in the areas surveyed had someone bitten by a dog in the last year . Other animal bite victims in African countries have been identified through retrospective studies starting at the clinic or hospital level using a trace back system in order to locate and interview the victims in depth [21]–[22] . This type of retrospective study should be conducted in KwaZulu-Natal in order to gain further descriptive information of the dog bite incidence . The neighbor's dog was identified as the offending canine in 71% of bite cases in this survey . Only 12% of the people had been bitten by their own dog . However , 17% of victims had been bitten by a dog with which they were unfamiliar . These dogs were identified as strange rather than strays or feral dogs , as they could not be differentiated from owned free roaming dogs . Eighty-three percent of dogs in this study were identified as being fully or partially unrestricted , being allowed to wander at will . Over 96% of the human rabies cases in India from 1992 to 2002 resulted from a dog bite , with 75 . 2% resulting from stray dogs and 11 . 1% from pets [15] . In response to the dog bites more than 80% of people went to the clinic in all areas except for rural St . Chad's where only 54% of bite victims reported clinic visits . St . Chad's residents reported a high awareness of rabies ( 88% ) , which could be explained by the rabies epidemic experienced in the area a few months prior to the survey . This area has a community clinic , as well as other nearby clinics and hospitals reachable by taxi . Without in depth queries , the reason why more persons did not visit a clinic remains unknown . Despite concerns about delayed treatment after dog bites , less than 2% of victims in this study visited a traditional healer and all of those cases were from rural areas . Herbal therapy and magico-religious practices were sought by rabies bite victims in India in 60% of fatal cases [15] . Respondents in KZN may be more informed about rabies than persons in other developing countries . One survey respondent said that the reason he did not go to the clinic after being bitten by his neighbor's dog was because the neighbor could prove to him that his dog had been previously vaccinated against rabies . Therefore , the victim felt he was safe to treat the wound at home . The investigation , follow through and cognition shown by this respondent is not something that should be expected from most bite victims . Dog bite victims in this study tended to visit the clinic regardless of their familiarity with the dog that bit them . Of those victims that did attend a clinic 22–75% received at least one rabies vaccine . The lower end of this spectrum is similar to what was seen in India where only 21% of rabies victims had received at least one rabies vaccine [15] . The area with the lowest rabies vaccination treatments was urban Umlazi Township and the highest was urban Esikhawini Township . In the regression model predicting what factors had an important impact on the victim receiving a rabies vaccine , only the area surveyed was found to be significant ( p = 0 . 0001 ) . Health facilities in South Africa where rabies vaccine and immunoglobulin ( RIG ) are available are listed with telephone contact numbers in the national rabies guideline [23] . However , a nationwide telephonic survey , which included 50% of the facilities identified for KwaZulu-Natal , was conducted in order to confirm the availability of these products . Only 68% of all the sites surveyed across the country were contactable by telephone . Forty-one percent had both vaccine and RIG , 32% had only vaccine , 5% had only RIG and 21% had neither vaccine nor RIG available [24] . Considering the results of this telephonic survey it is quite conceivable that administration of rabies vaccine is area dependent across the country . The juxtaposition of Esikhawini Township to the Port of Richard's Bay could explain why this area , which had the lowest recorded number of dog bite cases , had the highest amount of rabies vaccine administered . The Richard's Bay area may have more rabies vaccine dispensed that are related to aspects about the constituents the medical community serves , or because the medical staff could be indiscriminately dispensing supplies regardless of exposure risk . In Francophone African countries accurate rabies data is scarce [5] . This may be true in other African countries as has been previously eluded from Tanzania [1] . This survey showed a large respondent willingness to participate in community based surveillance at the village level . Community based surveillance activities should be considered in countries which lack central political will or local municipal finances . However , it has been stated that passive systems in developing countries are ineffective; therefore , an economic community based active surveillance system is recommended [25] . Unfortunately , community based systems have been shown to fail , particularly when there is a discrepancy in the interpretation of needs between the community and the donor organization [26] . Therefore , the methodology to be employed would have to be developed from a grassroots level rather than at a higher administrative level , which would take a commitment not previously demonstrated from this rank of society . Persons living in communities at high risk for canine rabies are interested in animal control laws and regulations . However , there is an indication of concern in the rural areas that these laws would also limit the number of livestock owned . This result may be due to rural areas owning more livestock . An indirect association between the limitations on number of dogs allowed with restrictions on livestock ownership may be behind these results . In rural Texas , USA , a survey regarding cattle ownership conducted from Texas A&M University indicated that ranchers were reluctant to comply with trace back ear-tagging measures , as the procedure would identify to officials how many cattle were owned by each producer at any point in time ( Dominguez unpublished data ) . Responsiveness and dedication to upholding animal control laws in this cultural environment by obliged parties will have to be instilled in a generation of officers committed to uplifting the community . Crucially , since this simple intervention may be particularly effective in preventing infection , only 21 of the 253 people in our survey bitten by a dog washed their bite wound as a response to treatment . It has been established that washing the bite wound for 15 minutes with soap and water can help reduce the incidence of disease by eliminating or inactivating the virus [5] . As only 44% of households were reported as having indoor plumbing ( indicated by flush toilets ) lack of available fresh water may explain the low percentage of wound washing as a response to post-bite treatment . This study shows that greater than 86% of the population has at least heard of the disease called rabies , but the response to dog bites indicates that both the general public and health sectors of the population do not understand the possible consequences related to dog bites in rabies enzootic environments . Availability of vaccine is an important factor in determining if bite victims receive rabies vaccine during clinic visits in KwaZulu-Natal and other parts of Africa; however , factors within the clinic setting such as staff knowledge need to be considered as well . Consideration of the offending dog in the bite incident has not been shown to play a role in victim response to dog bites . Therefore , the wasting of PEP could be as a big a problem as people at risk not receiving necessary vaccine . Our results also indicate that schools and rabies education of schoolchildren can be much improved . Not only are children most at risk of rabies exposure , but schools may present appropriate structures for dissemination of this kind of information and should be utilized to a greater extent . Questions in this survey regarding response to dog bites could have been more detailed . An example would be to include the age of the bite victim as a variable . Regardless , these results lend credence to the statement that an in-depth study regarding the treatment people are receiving and the public knowledge of rabies needs to be conducted .
Canine rabies has been enzootic to KwaZulu-Natal province , South Africa since the mid-1970's . Vaccination requirements for domestic species and animal control laws enforced in industrialized countries frequently eliminate the need for rabies post exposure prophylaxis ( PEP ) when an animal bite occurs . Rabies deaths in Africa are frequently linked to poverty and ignorance resulting in a lack of urgency for PEP in an environment where less than 70% of the domestic dog population is vaccinated against the disease . The results presented here are part of a larger canine ecology study conducted in KwaZulu-Natal from September 2009 through January 2011 . The six surveyed areas consisted of three land use types: three rural villages , two urban townships and one peri-urban township . The findings show that although a large portion of the population has awareness of rabies , there is a lack of understanding in the response to dog bites . Regression modeling of data suggests that there is an effect of area upon the result of a bite victim receiving PEP as part of treatment . Detailed retrospective study of dog bite incidence and an introspective study of clinics and treatment centers within the province may help explain the results found in this study .
You are an expert at summarizing long articles. Proceed to summarize the following text: Tetherin/BST2 was identified in 2008 as the cellular factor responsible for restricting HIV-1 replication at a very late stage in the lifecycle . Tetherin acts to retain virion particles on the plasma membrane after budding has been completed . Infected cells that express large amounts of tetherin display large strings of HIV virions that remain attached to the plasma membrane . Vpu is an HIV-1 accessory protein that specifically counteracts the restriction to virus release contributed by tetherin . Tetherin is an unusual Type II transmembrane protein that contains a GPI anchor at its C-terminus and is found in lipid rafts . The leading model for the mechanism of action of tetherin is that it functions as a direct physical tether bridging virions and the plasma membrane . However , evidence that tetherin functions as a physical tether has thus far been indirect . Here we demonstrate by biochemical and immunoelectron microscopic methods that endogenous tetherin is present on the viral particle and forms a bridge between virion particles and the plasma membrane . Endogenous tetherin was found on HIV particles that were released by partial proteolytic digestion . Immunoelectron microscopy performed on HIV-infected T cells demonstrated that tetherin forms an apparent physical link between virions and connects patches of virions to the plasma membrane . Linear filamentous strands that were highly enriched in tetherin bridged the space between some virions . We conclude that tetherin is the physical tether linking HIV-1 virions and the plasma membrane . The presence of filaments with which multiple molecules of tetherin interact in connecting virion particles is strongly suggested by the morphologic evidence . HIV interacts with a series of host proteins that facilitate its replication in cell . One of the clearest examples of this dependence on host machinery is the interaction between the p6 region of the HIV- 1 Gag protein and components of the cellular ESCRT machinery that are required for viral budding [1] , [2] . Conversely , some host cell factors act to limit viral replication , and are collectively known as host restriction factors . Host cell restriction factors have been identified that target specific steps in the human immunodeficiency virus type 1 ( HIV-1 ) lifecycle , including APOBEC3G [3] , [4] , [5] , Trim5α [6] , [7] , and recently tetherin [8] , [9] . These innate cellular defenses are constitutively expressed by host cells and can be upregulated in response to viral infection through the expression of type 1 interferons ( IFNs ) . Viruses in turn have evolved to express adaptor molecules that counteract important host cell restrictions , as illustrated by the Vif protein of HIV , which enhances the proteasomal degradation of APOBEC3G , and the Vpu protein , which relieves the host restriction imposed by tetherin . HIV-1 Vpu is a 16-kDa type 1 integral membrane protein [10] , [11] . Vpu operates as a multifunctional adaptor protein causing surface down-regulation and proteasomal degradation of CD4 in infected T lymphocytes [12] , [13] , [14] and enhancing viral particle release [15] , [16] . These two activities are separable , mapping to distinct structural domains and occurring in different subcellular compartments [17] , [18] . The particle release activity of Vpu was noted long ago to be cell-type dependent [8] , [19] , [20] , [21] . The presence of a host restriction factor acting at the level of particle release was suggested several years ago by experiments in which heterokaryons between restrictive and permissive cell lines exhibited a dominant restriction to particle release that was relieved by Vpu [16] . Recently the host cell restriction factor inhibiting particle release in the absence of Vpu was identified as bone marrow stromal cell antigen 2 ( BST-2 ) , also known as HM1 . 24 , CD317 , or tetherin [8] . Tetherin is a 28- to 36-kDa , type II integral membrane glycoprotein . The atypical topology of tetherin is comprised of a short N-terminal cytoplasmic tail , a single transmembrane spanning region , and a glycosyl-phosphatidlyinositol ( GPI ) anchor at its C-terminus . Tetherin's subcellular distribution is punctate on the plasma membrane and is also found on intracellular endosomal membranes , particularly the trans-Golgi network [22] , [23] . Tetherin expression is constitutive in restrictive human cell lines , including HeLa , H9 , Jurkat , Molt4 , primary T lymphocytes , and primary macrophages , and is absent in cells that are permissive for particle release , such as 293T , HOS , and HT1080 [8] . Tetherin expression is IFN-inducible , and IFN treatment of permissive human cell lines results in a restrictive , Vpu-dependent particle release phenotype . Tetherin overexpression results in a dramatic accumulation of virion particles attached to the plasma membrane of cells infected with vpu-deficient viruses . Virions are often found clustered at focal areas on the plasma membrane , and appear to be attached in long linear arrays extending from these plasma membrane foci [8] , [15] . As the name implies , tetherin is presumed to provide a physical tether between the plasma membrane and retained virions . However , the mechanism by which tetherin attaches nascent , mature HIV-1 virions to the plasma membrane remains to be fully elucidated . Tetherin and virions appear to colocalize by confocal microscopy at the particle budding site in some reports [8] , [9] , [24] , but not in others [25] . Immunoelectron microscopy of HA-tetherin failed to demonstrate concentration of the protein at particle budding sites [25] , and no definitive immunoelectron microscopic analysis of endogenous tetherin at the particle budding site has yet been reported . Furthermore , tetherin was recently found to be lacking on virions released from HeLa cells by shearing , suggesting that perhaps tetherin is not acting directly as a physical tether at all [26] . In this study , we provide evidence that tetherin is the physical linkage responsible for attachment of nascent HIV-1 virions to the plasma membrane . Partial protease stripping experiments utilizing both over-expressed and endogenous tetherin sources demonstrated incorporation into HIV-1 virions . In the absence of Vpu , tetherin was enriched on filamentous structures connecting virions to the plasma membrane , and was present between chains of tethered viral particles present in focal accumulations in infected cells . These results confirm tetherin's role in the physical attachment of virions to the plasma membrane and to each other in the absence of Vpu . The human T cell line A3 . 01 was employed in the original description of the EM phenotype of T cells infected with vpu-deficient HIV-1 [15] . Because this original report showed clearly both the accumulation of tethered virions on the plasma membrane and the accumulation of virions in intracytoplasmic vacuoles consistent with the action of tetherin , we concentrated on this cell type for the experiments in this report . A3 . 01 cells were incubated for 24 hours in the presence of 3000 U/ml recombinant IFN-α to induce tetherin expression . A3 . 01 cell lysates expressed near undetectable levels of tetherin , while the IFN-α treated population exhibited substantial induction of tetherin expression as indicated by Western blot analysis ( Fig . 1A ) . Tetherin was apparent as a series of bands at the 25–40 kD range , with a higher molecular mass component of apparent dimers . A background band at approximately 38 Kd is noted on this blot in both lanes . Miyagi and coworkers described a similar pattern for tetherin by Western blotting , but did not detect tetherin in A3 . 01 cells [26] , perhaps attributable to differences in the polyclonal rabbit antisera produced and employed in our laboratory . We then wanted to further define the specificity of our antisera to detect endogenous tetherin under non-denaturing , non-reducing conditions . The permissive 293T cell line has been shown to express low levels of tetherin [8] . To induce tetherin expression , 293T cells were cultured in the presence of 5000 U/ml of recombinant IFN-α for 24 h and compared with control cells . Cell surface staining of tetherin and subsequent analysis by flow cytometry displayed a substantial induction of tetherin staining of the IFN-α treated population as compared to the untreated cell sample ( Fig . 1B ) . A3 . 01 cells displayed two peaks when unstimulated , a tetherin-low and tetherin-intermediate population ( Fig . 1C , grey peaks ) . Following IFN stimulation , a distinct shift to a more uniform population of tetherin-high cells was noted ( solid line , no fill ) . IFN-α stimulated A3 . 01 cells demonstrated a restrictive phenotype that was consistent with the action of tetherin; this restriction was overcome by Vpu ( Fig . S2 ) . These data demonstrate the specificity of the rabbit anti-tetherin antiserum employed in this study , and indicate that IFN-α induction of tetherin in A3 . 01 cells induces a restriction to particle release that correlates with high cell surface levels of tetherin . The enrichment of tetherin on HIV-1 virions has not been established , and has not been detected by some investigators [26] . We thought it unlikely that tetherin would function as a physical link between particles and yet not be present in purified particle preparations . To begin to address the hypothesis that tetherin behaves as a physical linkage between nascent HIV-1 particles and the plasma membrane , we employed an assay to recover tethered virions from cell surfaces by proteolytic digestion [8] . We developed a 293T cell line stably expressing an N-terminal , HA-tagged tetherin ( HA-tetherin ) . HA-tetherin cells were transfected with pNL4 . 3/Udel and cultured for an additional 48 hours . HA-Tetherin bands were apparent in 293T-tetherin cells at molecular masses ranging from 25–36 kD ( Fig . 2A , + lane ) . Tethered virions were subtilisin “stripped” from the cell surface , and the supernatants concentrated through a 20% sucrose cushion . Concentrated material was then separated on linear 20–60% sucrose gradients by equilibrium density centrifugation . Stripped virions from this tetherin over-expression system were found to incorporate tetherin as evidenced by detection at a typical retroviral particle density enriched in Gag proteins ( Fig . 2B ) . HA-tetherin on this blot formed a single dominant band at 13 Kd , representing a uniform cleavage product with a protected HA tag . This band was substantially smaller than the full-length HA-tetherin observed in untreated cell lysates ( Fig . 2A ) . This is in fact the size that would be expected following cleavage at the predicted subtilisin cleavage site following residue 67 ( motif RNVTH , residues 64–68 of tetherin ) . Recognizing that over-expression of a membrane protein could result in its incorporation into virions in a non-physiologic manner , we next sought to detect endogenous tetherin on released virions from infected T cells . A3 . 01 cells were infected with VSV-G-pseudotyped NL4 . 3/Udel , cultured for 48 h , incubated with IFN-α for an additional 24 hours , and then subjected to gentle proteolytic digestion using TPCK-treated trypsin . Protease stripped supernatants were again concentrated and separated on a linear sucrose gradient . Endogenous tetherin was enriched in the peak viral fraction and co-sedimented precisely with NL4 . 3/Udel virions ( Fig . 2C ) . Interestingly , two major species of tetherin were detected on virions by Western blotting . The presence of both forms was consistent with Western blot analysis performed on IFN-α stimulated A3 . 01 cell lysates ( Fig . 1A ) , and suggests that the gentle trypsin treatment released virions without cleaving all of the full-length , virion-associated tetherin . These data demonstrate that tetherin is incorporated onto virions , and would be consistent with the proposed role of tetherin in physically linking particles to the plasma membrane and to each other . We next examined wildtype particles for the presence of tetherin . IFN-stimulated A3 . 01 cells were infected with wildtype NL4 . 3 , and particle gradients performed as for NL4 . 3/Udel . No tetherin was detected in the peak fractions from NL4 . 3 virions , indicating that they failed to incorporate endogenous tetherin due to the influence of Vpu ( Fig . 2C ) . NL4 . 3-infected cells stripped with TPCK-treated trypsin released low amounts of virions and also failed to concentrate tetherin ( data not shown ) . The incorporation of endogenous tetherin on NL4 . 3/Udel virions released by gentle protease digestion , and not wildtype NL4 . 3 virions , is consistent with a physical tethering of HIV virions that is overcome by Vpu . The results above are supportive of the idea that tetherin is present on particles and functions as a physical tether . However , we thought that more direct evidence would require demonstration of tetherin at the budding site and within virion-virion connections in the patches of tethered virions frequently seen when restrictive cells are infected with vpu-deficient HIV-1 . We therefore performed immunoelectron microscopic analysis of tetherin in A3 . 01 cells under a variety of conditions , including unstimulated cells , cells that had been subjected to IFN-α stimulation , and following infection with NL4 . 3/Udel . Several techniques were pursued in order to achieve specific staining . We first performed immunostaining with polyclonal anti-tetherin antisera prior to embedding in cells that had been lightly fixed with paraformaldehyde . Cells were then extensively washed and exposed to secondary antibody conjugated to 6 nm gold particles , followed by fixation , embedding , sectioning , and examination by transmission electron microscopy . IFN stimulated A3 . 01 cells maintained typical T-lymphocytic morphology ( Fig . 3A ) . Tetherin staining in uninfected cells was not diffuse , but was detected at focal membrane projections and small pits along the plasma membrane ( Fig . 1B and 1C ) . Detection of tetherin by immunogold labeling in this manner in unstimulated cells was minimal ( less than 0 . 1% of cells examined , Fig . S1A and S2A ) , while specific labeling was observed on 3 . 5% of IFN-stimulated cells ( Fig . S2A ) . Infection of IFN-stimulated A3 . 01 cells with NL4 . 3/Udel resulted in vast focal accumulations of mature virions attached to the plasma membrane ( Fig . 3D ) . Strikingly , within the tethered patches of virions , filamentous structures connecting mature virions exhibited positive tetherin immunogold labeling ( Fig . 3E and F , arrows ) . In each case , the immunogold beads appeared to be arrayed upon a filamentous , electron-dense substrate . The apparent filaments were sometimes noted to link multiple virions together ( Fig . 3F ) . We note that the length of the linearly-arrayed tetherin in these micrographs appears inconsistent with a simple protein bridge , but could represent tetherin on an extended membranous projection ( see Discussion ) . In order to generate robust tetherin immunogold labeling , A3 . 01 cells in the experiments above were only slightly fixed , causing aberrant virion morphology . In an attempt to improve virion morphology while addressing the same question , IFN-stimulated , NL4 . 3/Udel infected A3 . 01 cells were treated with indinavir to inhibit particle maturation , then stained prior to complete fixation and embedding as before . We reasoned that the stable and electron dense immature Gag shell would enhance this analysis . Indinavir treatment had no appreciable phenotypic affect related to the accumulation of virions along the plasma membrane in the absence of Vpu ( Fig . 4A ) . Tetherin was concentrated at HIV-1 plasma membrane budding sites in infected A3 . 01 cells ( Fig . 4B–G ) . Filamentous structures connecting particles immediately adjacent to the plasma membrane exhibited robust tetherin immunogold labeling were again observed ( Fig . 4B and C ) . Where filamentous structures were not observed , tetherin was localized in discrete clusters at sites of active particle generation along the membrane of infected A3 . 01 cells ( Fig . 4D–G ) . Again we noted that tetherin staining was not diffusely present along the plasma membrane . Also evident in these electron micrographs was a propensity of tetherin staining at the virion “base” , or areas of low particle electron density directed toward the pole of the virion adjacent to the plasma membrane ( Fig . 4G and H ) . Tetherin staining was not restricted entirely to the particle budding site on the plasma membrane , however , and was readily observed on the virion envelope within clusters of virions that were not adjacent to the membrane in the observed section ( Fig . 4I ) . We demonstrate above that tetherin is present in concentrated fashion at focal sites of particle budding . We next asked how the long strings of virions observed in many studies of vpu-deficient virions might form . Fig . 5 presents a series of micrographs in which immunogold staining was observed between virions within apparent chains or strings . Most common were the clustered immunogold beads between immature particles as shown in Fig . 5A and 5B . A second pattern was similar to the filamentous structures already described ( Fig . 5C and 5D ) . The stacked , parallel appearance in some sections was suggestive to us of a helical filament cut in cross section ( Fig . 5C ) , while in others a more linear extended connection was apparent ( Fig . 5D ) . Tetherin staining appeared to be present within chains extending several microns away from the plasma membrane , and was not observed more than 50 nm away from particles ( illustrated by Fig . 5F ) . To demonstrate that the presence of tetherin between virions was not due to aggregates or non-specific sticking of the anti-rabbit conjugated 6nm gold beads , we substituted 10nm protein A coated gold beads . Chains of NL4 . 3/Udel virions demonstrated 10nm gold beads connecting the virions to each other and to the plasma membrane , confirming the previous findings ( Fig . 5I ) . In this experiment , extended filaments were not readily apparent , but rather the protein A gold was found directly between adjacent particles . To further confirm the specificity of the observed labeling , identical experiments were performed in infected cells in the absence of labeling with the primary antibody . Strings of immunogold particles were not observed in over 50 sections examined from cells infected with NL4 . 3/Udel either with ( Fig . S1D–F ) or with indinavir ( Fig . S1G ) . Individual gold particles were sometimes observed on the cell surface in these experiments , but at a much reduced frequency ( detailed in Fig . S2A ) . A second technique to establish the relationship between tetherin and virion budding sites and particles was desirable . We continued this examination with A3 . 01 cells that had been infected with NL4 . 3/Udel and IFN-stimulated , but performed cryosectioning followed by immunolabeling with the identical polyclonal anti-tetherin antisera and secondary goat anti-rabbit antibody conjugated to gold . Staining was observed predominantly on the plasma membrane ( PM , Fig . 6A ) but also in apparent intracellular vesicles ( V , Fig . 6A ) . A low level of apparent background staining was seen outside of cells ( Fig . 6A ) . Importantly , the majority of virion particles observed were labeled with gold particles , and overall staining outside of 50 nm from particles was rare ( Fig . 6B , 6C ) . We counted the number of gold beads associated with 100 consecutive virus particles labeled in the presence or absence of primary antibody . Background in the absence of primary antibody was exceedingly low ( Fig . S1H and S1I ) , and the mean number of gold beads per virion stained with the anti-tetherin antibody was 9 . 3±5 ( Fig . S2B ) , while the mean number of gold beads was 0 . 2±0 . 4 in control experiments omitting the primary antibody . Tetherin staining was specifically located at the base of viral particles bridging the virion membrane and plasma membrane , similar to what had been observed with the pre-embedding labeling technique ( Fig . 6D ) . We slightly altered the fixation technique and repeated the cryosectioning and immunostaining experiment , ( Fig . 6E and 6F ) . With this alteration in methods , very little background staining was observed , and 91% of gold beads were found to be located within 50nm of visible particles . The number of gold beads per virion in this experiment was 3 . 2±2 . 2 , while using pre-immune sera the mean number of gold beads per virion ( within 50nm ) was 0 . 2±0 . 2 ( Fig . S3 , panels A–C ) . Taken together , these data are supportive of the role of tetherin as a physical link between the plasma membrane and virions , and suggest a potential role for a filamentous connection or substrate on which tetherin concentrates that contributes to the tethering phenomenon . Tetherin is a restriction factor that restricts the release of retroviruses and filoviruses [27] , [28] , [29] by inducing their accumulation at the plasma membrane of infected cells . Vpu overcomes this restriction for HIV , while HIV-2 and SIVs have evolved distinct mechanisms to counteract the block . In the case of HIV-2 Rod10 isolate , the envelope glycoprotein counteracts tetherin , while Nef fulfills this function for a number of SIV strains [30] , [31] . The mechanism by which Vpu lifts the restriction to particle release imposed by tetherin remains under study . Downregulation of cell surface tetherin was observed initially by the Guatelli laboratory [9] , providing a conceptually simple model that remains under study . Downregulation of surface tetherin has not been universally observed despite the clear relief of restriction by Vpu [26] . Evidence for Vpu-mediated degradation of tetherin by proteasomal [25] , [32] or lysosomal [24] pathways has been presented . The potency of the block to particle release induced by tetherin makes it an exciting area of investigation , both for its ability to enlighten understanding of intrinsic host restriction mechanisms and for its therapeutic implications . The work presented here addressed a simple question: is tetherin the physical connection that links retained virions to the plasma membrane and to each other ? Surprisingly , this had not yet been established , and some data to the contrary has been presented . During final revision of this manuscript , however , a paper strongly supporting tetherin as the physical link between virions and the plasma membrane was published [33] . In this paper , Perez-Caballero and coworkers demonstrate that tetherin is directly incorporated onto the membrane of HIV-1 particles . Our data agree with this finding , and support a model in which tetherin is present in the correct location to act as a physical tether . Tetherin is present at the budding site , it links particles together in long chains or strings , and it is present on released virion particles . These results certainly imply that removal of tetherin from the site of budding should remove the restriction , supporting the contention that cell surface downregulation of tetherin should correlate with relief of restriction . Results here were performed with endogenous tetherin in an infected T cell line , chosen because it is a prototypical cell line exhibiting a Vpu-responsive ( tetherin-mediated ) restriction to particle release . It will be important to repeat these experiments in infected primary T cells and macrophages , but we anticipate that the presence of tetherin at the site of particle retention will be similar . In some of our experiments , an extended filamentous electron-dense structure formed a bridge between retained virions . These filamentous structures demonstrated strong immunolabeling with tetherin . The length and intensity of labeling of these filaments makes it clear that multiple tetherin molecules are present , and that they cannot be tetherin dimers alone . The distance covered by these linear structures is too great to represent a protein bridge formed by tetherin-tetherin interactions . It is possible that membrane extensions bearing a high concentration of tetherin formed these apparent filaments . We observed these structures more consistently using the pre-embedding labeling technique than in cryosections , but failed to observe them at all in the absence of primary antibody or when pre-immune sera was employed . Thus we think they are specific structures worthy of additional investigation . Tetherin is intimately associated with the actin cytoskeleton [34] , and focal areas of intense cortical actin were apparent underlying focal collections of tetherin in some sections ( not shown ) . It is possible that actin or actin-associated proteins also contribute to the linear and perhaps helical filamentous connections we observed that were studded with tetherin . In summary , tetherin was specifically associated with particle budding sites and was found between virions in chains . These data support a physical role for tetherin in retaining virion particles in restrictive cells . Animals for production of antisera were housed and handled at Cocalico Biologicals , Inc . , Reamstown , PA . All animals were handled in strict accordance with good animal practice in accordance with NIH's Office of Laboratory Animal Welfare as reviewed by the Institutional Animal Care and Use Committee ( IACUC ) at Cocalico Biologicals ( Animal Welfare Assurance number A3669-01 ) . 293T cells ( obtained from the American Type Culture Collection ) were maintained in Dulbecco's Modified Eagle Medium ( DMEM ) supplemented with 10% fetal bovine serum ( FBS ) and penicillin/streptomycin ( PS ) . A3 . 01 cells ( a gift from Klaus Strebel , NIH ) were propagated in RPMI-1640 supplemented with 10% FBS , 2mM L-glutamine , and PS . A HA-tetherin cell line stably expressing an N-terminal , HA-tagged tetherin was generated by retroviral transduction of 293T cells . Briefly , subconfluent 293T cells were infected with HA-tetherin encoding retroviral stocks overnight in 10 cm2 dishes in the presence of 8 µg/ml polybrene . The following day the cell monolayers were washed with PBS and incubated in fresh growth media . After 48 hours , infected 293T cells were diluted and propagated in growth media supplemented with 0 . 5 µg/ml of puromycin until single colonies were present . Single puromycin-resistant colonies were isolated and assayed for the stable expression of physiological levels of HA-tetherin by Western blotting . The infectious HIV-1 molecular clone pNL4-3 , and the vpu-deficient pNL4-3/Udel , have been described [15] . pHCMV-G is an expression plasmid encoding the vesicular stomatitis virus glycoprotein G ( VSV-G ) [35] . pCMV-HA . Tetherin is a plasmid encoding an N-terminal , HA-tagged tetherin obtained by PCR cloning of tetherin cDNA into the Sal1-Xho1 sites of pCMV-HA ( Clontech ) . The HA-Tetherin sequence was amplified by PCR cloning and inserted into the Age1-Pac1 sites of the retroviral vector pQCXIP ( Clontech ) to generate pQCXIP-HA . tetherin . HIV-1 viral stocks were generated by Fugene HD ( Roche Diagnostics ) co-transfection of 293T cells with the molecular clone , pNL4-3/Udel , and the vesicular stomatitis virus envelope glycoprotein expression plasmid pHCMV-G . Virus was harvested from transfected cell supernatants 48 hours post-transfection , filter-sterilized , and assayed for infectivity using TZM-bl indicator cells . Infectious titers were measured as β-galactosidase+ colony-forming units . A retroviral stock encoding HA-tetherin was prepared by co-transfecting 293T cells with pCL-Ampho [36] , pQCXIP-HA-Tetherin , and pHCMV-G . Transfected cell supernatants were harvested 48 hours post-transfection , filter-sterilized , aliquoted , and stored at −80°C for future use . Anti-tetherin antiserum was elicited in rabbits at Cocalico Biologicals , Inc . ( Reamstown , PA , USA ) using a recombinant GST-tagged tetherin fusion protein . The tetherin fragment was composed of the entire ectodomain spanning amino acids 43–179 inserted into vector pGEX6p-1 ( GE LifeSciences ) . The tetherin fusion construct was concentrated from clarified bacterial sonicates using glutathione sepharose 4B beads . The GST-tag was excised with PreScission protease ( GE Life Sciences ) and the supernatants were then further purified using cation-exchange chromatography ( HiTrap Q HP; GE Life Sciences ) . Rabbits were immunized with 250 µg of recombinant tetherin protein per dose until an endpoint antibody titer of 1 . 0×108 was achieved . The rabbit anti-tetherin antiserum was assayed for reactivity and specificity in Western Blot and flow cytometry prior to final serum harvest . 5×106 293T cells were propagated overnight in 10cm2 dishes; A3 . 01 cells were maintained in suspension culture . The cells were incubated for 24 h in the presence of 5000 U/ml IFN-α . On the following day the cell monolayer ( 293T ) was washed twice with pre-warmed PBS and cells detached using 3 ml of Versene ( 0 . 2 g/l EDTA-4Na in PBS; Invitrogen ) per dish . Cells were then pelleted and washed repeatedly with ice-cold PBS . Cells were resuspended in 2%BSA-PBS and allowed to incubate on ice for 10 min prior to the addition of primary antibody ( rabbit α-tetherin ) for 1 h on ice . The cells were washed twice with 2%BSA-PBS followed by the addition of Alexa Fluor 633-conjugated anti-rabbit IgG secondary antibody ( Molecular Probes; Invitrogen ) diluted to 1 µg/ml in 2%BSA-PBS . The mixture was allowed to incubate in the dark for 30 min on ice . The stained cells were analyzed using a FACS Canto ( BD Biosciences ) flow cytometer . Subsequent data analyses were performed using Flow Jo 7 . 2 . 4 ( Tree Star ) . In order to determine the presence of over-expressed tetherin on HIV-1 virions , 5×106 HA-tetherin cells were plated per 10cm2 dish . The following day the cells were transfected with Fugene HD using 3 µg pNL4-3/Udel per dish . At 48 hours post-transfection , cells were washed twice with phosphate-buffered saline ( PBS; pH 7 . 4 ) . Cell monolayers were then incubated for 30 min at 37°C in 3 . 5 ml of Tris-Cl ( pH 8 . 0 ) , 150 mM NaCl , 5 mM CaCl2 , and 5 µg/ml subtilisin ( Sigma Aldrich ) . The digest was stopped by the addition of 3 . 5 ml of FBS-containing growth media plus protease inhibitors . Cellular supernatants were clarified by low-speed centrifugation and then purified by ultracentrifugation through a 20% sucrose cushion ( 100 , 000 g for 3 hours , 4°C ) . Pellets were then resuspended in 1 ml of PBS and overlaid onto linear 20–60% sucrose gradients . Ultracentrifugation of gradients was performed overnight at 100 , 000× g at 4°C . Equal 800 µl fractions were collected and density determined using a refractometer . Samples were diluted in PBS and concentrated using a microultracentrifuge ( 2 hours at 100 , 000 g; 4°C ) . Pellets were subsequently resuspended in 1× SDS-PAGE load buffer containing 100 mM DTT . Analysis of fractionated material was performed by Western blotting using anti-p24 hybridoma 183-H12-5C ( obtained from Bruce Chesebro and Hardy Chen through the NIH AIDS Research Reference and Reagent Program ) supernatants ( 1∶1000 ) and rabbit anti-tetherin polyclonal antisera ( 1∶2000 ) . Endogenous tetherin incorporation into HIV-1 virions was assessed by infecting A3 . 01 cells with VSV-G pseudotyped NL4 . 3/Udel . 1 . 0×107 A3 . 01 cells were infected with VSV-G pseudotyped NL4 . 3/Udel at an MOI of 0 . 75 . The cells were cultured for 3 days prior to the addition of 3000 U/ml IFN-α . On day 4 post-infection , the cells were pelleted at 350× g for 5 minutes at room temperature and washed twice with 10 ml PBS . The cells were resuspended in 5 ml serum-free growth media supplemented with 3 µg/ml TPCK-treated trypsin and incubated for 3 hours at 37°C . The protease activity of the supernatant was ablated by the addition of 5 ml of FBS-containing growth media plus protease inhibitors . The subsequent analysis of cellular digests was performed as described above . A3 . 01 cells were infected with VSV-G pseudotyped NL4 . 3/Udel and stimulated with IFN-α as described above . A subset of cell cultures were treated for 24 h prior to harvest with 5 µM indinavir to inhibit proteolytic particle maturation and assist in maintenance of virion morphology . At 48 hours , cells samples were fixed , sectioned , and stained for examination by transmission electron microscopy as previously described [37] . Pre-embedding immunogold labeling of samples for tetherin was performed by gentle fixation in 4% paraformaldehyde for 20 min at room temperature ( RT ) . The cells were then extensively washed with 1% BSA-PBS and blocked with 1% gelatin-PBS for 30 min at RT . Primary antibody ( rabbit anti-tetherin antisera ) was diluted to 1∶300 in 1% gelatin-PBS and incubated with cells for 1 . 5 hours at RT with gentle agitation . The cells were extensively washed prior to the addition of secondary goat anti-rabbit IgG antibody conjugated to 6 nm ( EM Sciences ) gold beads for 1 hour at RT with gentle agitation . The cells were then washed extensively , fixed with 2 . 5% glutaraldehyde for 1 hr at RT , and postfixed with 1% osmium tetroxide at 4°C for 1hour . Dehydration with ethanol was performed and the cells were embedded in Eponate-12 resin . Regular 70 nm ultra-thin sections were produced , double stained with uranyl acetate and lead citrate , and observed under a Hitachi H7500 transmission electron microscope at 75 KV . Infected and control A3 . 01 cells were also analyzed by immuno-EM techniques on ultra-thin cryosections . Cryosections were generated by fixing cells in freshly made 4% paraformaldehyde in PBS for 20 minutes at RT . Tissue blocks were embedded in 10% gelatin . In a variation of this procedure , an additional fixation step with 0 . 7% gluteraldehyde for 40 minutes was performed . The cells were then washed extensively with PBS prior to infusion with 2 . 3 M sucrose , or alternatively 30% polypropylene glycol ( PPG ) as a cryoprotectant . The cell pellets were frozen using liquid nitrogen under controlled conditions ( cryogen ) and sectioned using a cryo-ultra microtome generating 70 nm cryosections . The sections were thawed on nickle grids with formvar supporting membranes prior to antibody labeling . The samples were blocked using 1% BSA-1% gelatin in PBS for 1 h at RT . Glycine was then used to block any free aldehyde groups present . The samples were incubated in 5 µg/ml of primary rabbit anti-tetherin antibody in 1% BSA/1% gelatin overnight at 4°C . The sections were washed extensively prior to the addition of secondary goat anti-rabbit IgG conjugated with 6 nm gold beads for 1 hour at RT . The sections were then air dried in a film of methylcellulose with 1% uranyl acetate prior to observation under a Hitachi H7500 transmission electron microscope . Control experiments included unlabeled cells , cells labeled in the absence of IFN stimulation , uninfected cells either treated or untreated with IFN , and samples from each experimental group in which the primary antibody was omitted during the immunolabeling procedure . A minimum of 25 cells were examined for each experimental arm and control when recording labeling positivity . A total of 70 cells and their associated viruses were examined to derive the mean number of immunogold particles present per virion ( counting as positive those gold particles within 50nm of the virion ) in the cryosection-immunolabeling experiments .
Tetherin or BST2 is a cellular protein that was recently found to limit the ability of HIV to escape from cells . HIV counteracts this cellular restriction to its lifecycle by expressing the small viral accessory protein Vpu . Upon viral infection , cells expressing high levels of tetherin accumulate large clusters or strings of virions that remain attached to the plasma membrane by an unknown mechanism . The simplest explanation for this clustering is that tetherin itself physically attaches particles to the plasma membrane and to each other . In this article , we demonstrate that this is indeed the case . We found that particles released from cells by gentle protease treatment contain either cleaved or full-length tetherin . Using electron microscopy and immunogold staining , we show that tetherin is present as a physical link between viruses and the plasma membrane and sometimes between virus particles in large clusters or strings . Together this provides evidence that tetherin serves a direct , physical role in retaining particles on the surface of cells .
You are an expert at summarizing long articles. Proceed to summarize the following text: Loss of immune control over opportunistic infections can occur at different stages of HIV-1 ( HIV ) disease , among which mucosal candidiasis caused by the fungal pathogen Candida albicans ( C . albicans ) is one of the early and common manifestations in HIV-infected human subjects . The underlying immunological basis is not well defined . We have previously shown that compared to cytomegalovirus ( CMV ) -specific CD4 cells , C . albicans-specific CD4 T cells are highly permissive to HIV in vitro . Here , based on an antiretroviral treatment ( ART ) naïve HIV infection cohort ( RV21 ) , we investigated longitudinally the impact of HIV on C . albicans- and CMV-specific CD4 T-cell immunity in vivo . We found a sequential dysfunction and preferential depletion for C . albicans-specific CD4 T cell response during progressive HIV infection . Compared to Th1 ( IFN-γ , MIP-1β ) functional subsets , the Th17 functional subsets ( IL-17 , IL-22 ) of C . albicans-specific CD4 T cells were more permissive to HIV in vitro and impaired earlier in HIV-infected subjects . Infection history analysis showed that C . albicans-specific CD4 T cells were more susceptible to HIV in vivo , harboring modestly but significantly higher levels of HIV DNA , than CMV-specific CD4 T cells . Longitudinal analysis of HIV-infected individuals with ongoing CD4 depletion demonstrated that C . albicans-specific CD4 T-cell response was preferentially and progressively depleted . Taken together , these data suggest a potential mechanism for earlier loss of immune control over mucosal candidiasis in HIV-infected patients and provide new insights into pathogen-specific immune failure in AIDS pathogenesis . Untreated HIV infection causes progressive depletion of human CD4 T cells , leading to impaired cellular immunity , enhanced susceptibility to opportunistic infections ( OIs ) and development of acquired immunodeficiency syndrome ( AIDS ) [1–3] . Although the loss of immune control over OIs is known to be generally associated with overall reduction in CD4 T cells , HIV cohort studies have found that OI reactivation can occur at different stages of HIV disease and is not strictly associated with total CD4 loss [4–6] . For instance , while the opportunistic pathogen Mycobacterium tuberculosis ( MTB ) can cause active disease relatively early during HIV infection [7] , cytomegalovirus ( CMV ) infection rarely causes evident diseases at early stage [8 , 9] . These observations have suggested that host immunity specific for opportunistic pathogens may be impaired or lost at different stages of HIV disease [10–12] . In support , an important study by Geldmacher et al . demonstrated that compared to CMV , MTB-specific CD4 T cells are preferentially infected and depleted in HIV-infected human subjects [10 , 13] . Mucosal candidiasis , predominantly caused by the commensal fungal organism Candida albicans ( C . albicans ) , is one of the most common and earliest manifestations in HIV-infected subjects [14 , 15] . In immune competent humans , C . albicans can be readily detected without overt signs of clinical disease [16] . However , under immune compromised conditions such as in AIDS patients , C . albicans can quickly cause active infections in multiple tissues , including oral mucosa [17] . Evidence has shown that about 50–90% of HIV-infected individuals could manifest an episode of oral candidiasis during their progression to AIDS [18 , 19] . Even with the introduction of potent antiretroviral treatment ( ART ) , oropharyngeal and esophageal candidiasis are still the two clinically relevant presentations in HIV-infected patients [20] . The underlying immunological basis for early and profound onsets of pathogenic C . albicans infections in HIV-infected individuals is not fully defined . C . albicans exposure induces strong cellular immunity , as evidenced by the skin-test reactivity and in vitro lymphocyte proliferative response [21 , 22] . Majority of evidence obtained so far from animal models and human studies has suggested CD4-mediated cellular immunity as the predominant host defense mechanism against C . albicans infection [23–30] , although involvement of specific functional facets of CD4 T-cell immunity , for instance , Th1 vs . Th17 response , has been obscure . It was initially suggested that Th1 response was the key mediator of immunity [31] . More recently , increasing evidence has indicated that Th17 , but not Th1 , response is critical for immune protection against mucosal candidiasis [25 , 32 , 33] . Importantly , in the setting of HIV infection , limited information is currently available regarding the longitudinal impact of HIV on different functional facets of anti-C . albicans CD4 T-cell immunity in HIV-infected individuals . To explore the effect of HIV on different antigen-specific CD4 T cells , we have previously described an in vitro system , where HIV susceptibility and the associated phenotypes of antigen-specific CD4 cells can be examined [12 , 34] . We have found that human C . albicans-specific CD4 T cells are highly permissive to HIV infection in vitro compared to CMV-specific CD4 T cells [12] . It remains to be determined as to how HIV affects these two groups of pathogen-specific CD4 T-cell immunity in vivo in HIV-infected subjects . RV21 is an antiretroviral treatment ( ART ) naïve , longitudinal HIV-infection cohort established by the U . S . Military HIV Research ( MHRP ) and the HIV-infected subjects enrolled in this cohort were followed up for 2 to 6 years . In the current study , we studied HIV-infected subjects in the RV21 cohort who manifested ongoing CD4 depletion . Using PBMC samples from these individuals , we comparatively examined the longitudinal impact of HIV on functional profiles and magnitudes of C . albicans- and CMV-specific CD4 T cell responses in vivo during HIV disease progression . Our data showed that there was a sequential dysfunction for C . albicans-specific CD4 T cell response with an earlier and more profound impairment of Th17-associated functions ( IL-17 , IL-22 ) in HIV infection . Further analyses identified that compared to CMV-specific CD4 T cells , C . albicans-specific CD4 T cells were more susceptible to HIV in vivo and preferentially depleted in these HIV-infected subjects . Antigen-specific T cell responses elicited by different pathogens can be qualitatively distinct . In our previous studies [12 , 34] , we have reported an in vitro system for examining the susceptibility of antigen-specific human CD4 T cells to HIV infection and the associated phenotypic and functional characteristics ( Fig A in S1 Appendix ) . We here utilized this system and first determined the functional profiles of C . albicans-specific CD4 T cells as compared to CMV-specific CD4 T cells in healthy human subjects . PBMC samples from healthy donors were labeled with CFSE , a fluorescent dye to track T cell division , and then stimulated with C . albicans or CMV antigen for 6 days , during which memory CD4 T cells underwent Ag-specific proliferation in response to stimulation . Cells were re-stimulated on day 6 for de novo cytokine synthesis . Functional profiles ( IL-17 , IL-22 , IL-2 , IFN-γ and MIP-1β ) of C . albicans- or CMV-specific CD4 T cells in PBMCs were examined in CFSE-low CD4 T cells by multi-color flow cytometry ( Fig A in S1 Appendix ) . Verification of the system has been described in previous reports [12 , 34] . We found that C . albicans-specific CD4 T cells displayed a distinct functional profile from CMV-specific CD4 T cells in healthy donors ( Fig 1A ) . Compared to CMV-specific CD4 T cells , which predominantly expressed Th1-associated cytokine IFN-γ ( 75 . 6% ) and MIP-1β ( 67% ) , C . albicans-specific CD4 T cells expressed high levels of IL-17 ( 20 . 4% ) , IL-22 ( 15% ) and IL-2 ( 63 . 7% ) , in addition to expression of IFN-γ and MIP-1β , suggesting a Th17/Th1-like phenotype for C . albicans-specific CD4 T cells in human subjects ( Fig 1A ) . Analysis of PBMCs from multiple donors ( n = 6 ) showed that expression of IL-17 ( p<0 . 0001 ) , IL-22 ( p<0 . 001 ) , IFN-γ ( p<0 . 001 ) and MIP-1β ( p<0 . 01 ) was statistically different between C . albicans- and CMV-specific CD4 T cells ( Fig 1B ) . Poly-functional analysis showed that C . albicans-specific CD4 T cells demonstrate a more poly-functional profile and can co-express multiple cytokines compared to CMV-specific CD4 T cells ( Fig B in S1 Appendix ) . We also measured gene expression of Th17 and Th1 lineage-specific transcription factors , including RORC ( Th17 ) , T-bet and EOMES ( Th1 ) , in C . albicans- and CMV-specific CD4 T cells from the same donor PBMCs ( Fig 1C ) . CFSE-low , CD4 T cells were sorted from PBMC and subjected to real-time PCR quantification . We found that while gene expression of Th1 transcription factors T-bet and EOMES was comparable between C . albicans- and CMV-specific CD4 T cells , the Th17 transcription factor RORC was expressed at significantly higher levels in C . albicans-specific CD4 T cells , further suggesting the mixed Th17/Th1-like phenotype of C . albicans-specific CD4 T cells in human subjects ( Fig 1C ) . Since no significant difference in T-bet and EOMES expression was observed at mRNA level between C . albicans- and CMV-specific CD4 T cells , we measured protein expression of these two transcription factors using flow cytometry and found that compared to CFSE-Hi non-specific CD4 T cells , both C . albicans- and CMV-specific CD4 T cells expressed higher levels of T-bet and EOMES , although the expression levels in CMV-specific CD4 T cells appeared to be slightly higher than those in C . albicans-specific CD4 T cells ( Fig C in S1 Appendix ) . The results suggest that both Ag-specific CD4 T cell populations in this system manifest increased expression of Th1 transcription factors than non-specific CD4 T cells , which is in line with previous reports showing that T-bet and EOMES were readily detectable in CMV-specific CD4 T cells albeit at lower level than in their CD8 counterparts [35–37] . Based on this system , we examined HIV susceptibility of C . albicans- and CMV-specific CD4 T cells from healthy donor PBMCs and found that C . albicans-specific CD4 T cells were substantially more permissive to HIV than CMV-specific CD4 T cells in vitro ( Fig 2A ) , a finding that was consistent with our previous report [12] . To explore whether the significant difference in HIV susceptibility between C . albicans- and CMV-specific CD4 T cells is due to higher permissiveness of C . albicans-specific CD4 T cells or enhanced protection of CMV-specific CD4 T cells , we compared their HIV susceptibility with that of non-specific total CD4 T cells that were globally stimulated with anti-CD3/CD28 ( Fig D in S1 Appendix ) , and found that HIV infectivity in globally stimulated CD4 T cells fell into the range between C . albicans- and CMV-specific CD4 T cells ( Fig D in S1 Appendix ) , implying that the difference in HIV susceptibility between C . albicans- and CMV-specific CD4 T cells might attribute to the combination of both . This will be further investigated subsequently . Functional characteristics of CD4 T cells have been shown to associate with their susceptibility to HIV [10 , 13] . In order to define the relationship between HIV infectivity and functional characteristics for C . albicans-specific CD4 T cells , we performed the HIV susceptibility assay as described in Fig A in S1 Appendix . Healthy donor PBMCs were CFSE-labeled and stimulated with C . albicans antigen , followed by exposure to HIV . Three days after HIV infection , cells were re-stimulated with PMA/ionomycin and subjected to comprehensive flow cytometric analysis ( Fig A in S1 Appendix ) . Cytokine expression in activated T cells is transient and the CFSE-low , Ag-specific CD4 T cells in this system undergo days of proliferation . In order to simultaneously measure functional characteristics ( cytokine production ) and HIV infectivity ( intracellular p24 ) , cells were re-stimulated with the global PMA/ionomycin stimulus on day 6 for cytokine re-synthesis in T cells . As shown in Fig 2B , by gating on CFSE-low CD4 T-cell population , we determined HIV infectivity in each functional subset of C . albicans-specific CD4 T cells by measuring co-expression of intracellular HIV p24 , as an indication of productive HIV infection , with individual cytokines ( Fig 2B ) . Number in each plot showed intracellular p24+ rate in cytokine-producing , C . albicans-specific CD4 subset . We found that the IL-22 , IL-17 or IL-2 functional subsets of C . albicans-specific CD4 T cells were more susceptible to HIV as compared to those subsets expressing MIP-1β and IFN-γ ( Fig 2B ) . We measured expression of CCR5 , an important co-receptor for HIV entry , on these different functional CD4 subsets and no significant difference was observed ( Fig E in S1 Appendix ) . Instead , we found that the higher HIV infectivity in IL-22+ , IL-17+ and IL-2+ subsets appeared to be associated with their lower levels of MIP-1β co-expression ( Fig F in S1 Appendix ) . This observation was consistent with an earlier report showing that in vitro differentiated IL-17+ CD4 T cells are more susceptible to HIV than IFN-γ+ CD4 T cells due to reduced expression of MIP-1β [38] . We noted that HIV infectivity in IFN-γ+ and MIP-1β+ subsets was also fairly high ( 63% and 30% , respectively ) ( Fig 2B ) . Next , we gated on the IFN-γ+ or MIP-1β+ , CFSE-low CD4 subsets ( Fig 2C ) and found that significant fractions of IFN-γ+ ( or MIP-1β+ ) subset co-express with IL-2 and IL-17 or IL-22 ( Fig 2C and Fig G in S1 Appendix ) . When we further performed intracellular p24 ( red dots ) and cytokine ( blue background ) overlaying analysis , as shown in Fig 2C , we identified that HIV predominantly infected IFN-γ+ or MIP-1β+ CD4 subsets that co-expressed IL-2 and IL-17 or IL-22; the single IFN-γ- or MIP-1β-producing CD4 subsets ( bottom left quadrant ) demonstrated very low HIV infectivity ( Fig 2C ) . In order to better differentiate HIV infectivity in all different functional subsets ( combination of cytokine+ ) , we performed comprehensive Boolean gating and spice analysis . As shown in Fig 2D , we identified a consistent trend that HIV infectivity ( p24+% ) was substantially higher in populations that express of IL-17 , IL-2 and IL-22 , but lower in populations that only express MIP-1β and/or IFN-γ . CD25 , the high-affinity chain of IL-2 receptor , was shown to be important for HIV infection of CD4 T cells in vitro [13 , 39] . We examined expression of CD25 on C . albicans- and CMV-specific CD4 T cells and evaluated its relationship with HIV infectivity and cytokine expression in our system ( Fig 2E–2G ) . Interestingly , the data showed that C . albicans-specific CD4 T cells expressed substantially higher level of CD25 than CMV-specific CD4 T cells ( Fig 2E ) . Importantly , productive HIV infection was predominantly observed in CD25+ subset both C . albicans- and CMV-specific CD4 T cells ( Fig 2E , top panels; Fig 2F ) . We also examined the impact of exogenous IL-2 on CD25 expression and HIV infectivity ( Fig 2E , bottom panels ) . Despite recombinant IL-2 ( rIL-2 ) induced significant increase in CD25 expression and CD4 T-cell proliferation , HIV infectivity in exogenous IL-2-treated cells were not enhanced ( Fig 2E , bottom; Fig 2F ) , indicating that HIV infectivity is associated with endogenous IL-2-CD25 signaling . In addition , we investigated relationship between CD25 and cytokine expression in C . albicans-specific CD4 T cells . As shown in Fig 2G , CD25 predominantly co-expressed with IL-17 , IL-22 and IL-2 , but not IFN-γ- or MIP-1β , which is consistent with the observation about HIV infectivity in different CD4 T-cell subsets . Taken together , these data suggest that compared to CMV , C . albicans-specific CD4 T cells manifest distinct phenotypic and functional characteristics that favor productive HIV infection in these cells . As described earlier , CD4-mediated cellular immunity is a predominant host defense mechanism for immune control of pathogenic C . albicans infection [23–27] . While Th1 response was initially thought to be the key mediator of immunity , more recent studies have supported critical role of IL-17- and IL-22-producing Th17 , but not IFN-γ-producing Th1 , response in protection against candidiasis [31 , 32] [33] . After showing that IL-17 , IL-22 and IL-2 functional subsets of C . albicans-specific CD4 T cells were more permissive to HIV in vitro , we next aimed to determine the in vivo impact of HIV on C . albicans-specific CD4 T-cell immunity and the associated functional subsets in HIV-infected individuals . To do so , we selected HIV-infected subjects in RV21 cohort who manifested ongoing CD4 depletion , which permitted us to longitudinally examine the in vivo effect of HIV on pathogen-specific CD4 cells . We identified 20 HIV-infected subjects with positive responses to both C . albicans and CMV antigens and the PBMC samples from these subjects were accordingly investigated ( Table 1 ) . To better explore the impact of HIV on pathogen-specific CD4 T-cell immunity , multiple assays were performed . Due to limited cell number for each HIV-infected subject , we appropriately allocated cell samples from these 20 subjects for different assays as detailed below . We first used a similar method as described in Fig 1 and measured functional profiles of proliferating C . albicans-specific CD4 T cells in the HIV-infected subjects as compared to healthy donors ( Fig 3 ) . PBMCs of HIV+ subjects measured here were collected at early HIV infection when profound CD4 depletion had not occurred and C . albicans-specific response remained detectable . As shown in Fig 3A , only the CFSE-low , C . albicans-specific CD4 T cell populations were gated for analysis . Interestingly , we found that compared to C . albicans-specific CD4 T cells in control PBMC of healthy donors , which manifested strong proliferative response and normal production of all cytokines tested ( IL-17 , IL-22 , IL-2 , IFN-γ and MIP-1β ) , C . albicans-specific CD4 T cells from HIV-infected subjects , despite being able to proliferate at comparable levels , demonstrated a preferential impairment in Th17 functions with substantial decrease in IL-17 , IL-22 and IL-2 production , while their Th1 response ( expression of IFN-γ or MIP-1β ) was not significantly affected ( Fig 3A ) . We examined PBMC samples from multiple subjects ( n = 7 ) and observed statistically significant differences for expression of IL-17 ( p<0 . 01 ) , IL-22 ( p<0 . 01 ) and IL-2 ( p<0 . 01 ) , but not IFN-γ ( N . S . ) or MIP-1β ( N . S . ) between healthy donors and HIV-infected subjects ( Fig 3A ) . Importantly , we also measured functional profile of CMV-specific CD4 T cells in these HIV-infected subjects ( Fig 3B ) and found that CMV-specific CD4 T cells at early HIV infection manifest a comparable functional profile with that in uninfected healthy donors; no significant reduction in expression of Th1 cytokines IFN-γ ( N . S . ) and MIP-1β ( N . S . ) was observed , although the IL-2 expression in CMV-specific CD4 T cells appeared to be impaired in HIV-infected subjects as compared to healthy subjects ( p<0 . 01 ) ( Fig 3B ) . Taken together , these data from HIV-infected subjects were consistent with the in vitro observations in Fig 2 and imply that there was a sequential dysfunction for C . albicans-specific CD4 T-cell response with earlier impairment of Th17 functions during HIV infection . Given the critical role of Th17 , but not Th1 , response in protection for mucosal candidiasis , this finding is potentially significant and suggests that early following HIV infection , the anti-C . albicans specific immunity might become rapidly ineffective , despite their proliferative and Th1-type responses are still readily detectable . We have shown in Fig 2A that C . albicans-specific CD4 T cells are more susceptible to HIV infection than CMV-specific CD4 T cells in vitro [12] . In order to determine if this occurs in vivo , we examined the levels of cell-associated HIV DNA using quantitative PCR in C . albicans- and CMV-specific CD4 T cells from HIV-infected subjects [13 , 40] . To do so , we used PBMC samples of HIV-infected subjects collected at early HIV infection when profound CD4 depletion had not occurred and both C . albicans- and CMV-specific CD4 T cell responses were detectable . PBMCs were CFSE-labeled and stimulated with C . albicans- or CMV-antigen for 5 days , during which HIV replication inhibitor AZT was added to cell culture to prevent potential de novo HIV replication . No HIV virus was detected in the culture supernatants after antigen stimulation and T-cell division , supporting the effectiveness of AZT in blocking possible de novo viral replication ( Fig H in S1 Appendix ) . After stimulation , C . albicans- and CMV-specific CD4 T-cell populations from the same PBMCs were sorted based on CFSE-low ( Fig 4A ) and then subjected to quantification of HIV DNA . Plasmids encoding HIV Gag or GAPDH were used to generate standard curves for quantifying real copy numbers of HIV DNA in the sorted Ag-specific CD4 T cells ( Fig I in S1 Appendix ) . Quantification of HIV DNA in the sorted cells was normalized to GAPDH and expressed as copy number/106 CD4 T cells . As shown in Fig 4B , C . albicans-specific CD4 T cells harbored modestly but significantly higher levels of HIV DNA than CMV-specific CD4 T cells ( p = 0 . 004 ) . We measured PBMCs from 5 HIV-infected individuals and observed a consistent trend , although the difference for some subjects was modest ( Fig 4B ) . Since predominant sites of HIV infection and replication are lymphoid or mucosal tissues , not peripheral blood , we speculate that differences for HIV DNA content between C . albicans- and CMV-specific CD4 T cells , when isolated from these effector sites , might be more profound . Quantification of cell-associated HIV DNA to evaluate in vivo HIV susceptibility has been previously reported for MTB- and HIV-specific CD4 T cells [13 , 40] . To gain better insights into relative HIV susceptibility of different Ag-specific CD4 T cells in vivo in infected individuals in the absence of viral suppression , we further measured additional antigens for comparison with C . albicans . Since the HIV-infected subjects examined in this study resided in the US and demonstrated very low MTB response , we were unable to directly compare C . albicans with MTB . Instead , we measured CD4 T cells specific for varicella zoster virus ( VZV ) , another herpes virus similar to CMV , as well as CD4 T cells specific for HIV Env protein; HIV DNA in CFSE-Hi non-specific CD4 T cells from the same individuals was also compared ( Fig 4C ) . Since cell number for each subject was limited and some subjects only responded to certain antigens , not all antigens were compared for each subject . Interestingly , we found that among the subjects investigated , C . albicans-specific CD4 T cells appeared to also harbor higher levels of HIV DNA as compared to VZV-specific ( subject 1–3 ) and HIV Env-specific ( subject 6–9 ) CD4 T cells ( Fig 4C ) . Of note , HIV DNA copies in C . albicans-specific CD4 T cells varied fairly substantially among different subjects , an observation that was also reported for MTB-specific CD4 T cells [13] . Taken together , these results suggest that C . albicans-specific CD4 T cells are highly susceptible to HIV in vivo when compared to multiple other antigens . As discussed above , compared to peripheral blood , mucosal tissues represent a preferential site for HIV infection and manifest most remarkable CD4 depletion at all stages of HIV disease [41 , 42] . Integrin α4β7 is an important mucosal homing receptor , directing migration of CD4 T cells from blood to gut , and CCR6 is a marker associated with Th17 cells that contributes to their migration to mucosal tissues [43] . We examined expression of these mucosal homing receptors on C . albicans- and CMV-specific CD4 T cells from the HIV-infected subjects . Our data showed that significant fraction of C . albicans-specific CD4 T cells expressed high levels of α4β7 and CCR6 , while CMV-specific CD4 T cells rarely expressed these two receptors ( C . albicans vs . CMV: 70 . 6% to 7 . 7% for α4β7; 26 . 4% vs . 3 . 1% for CCR6 ) ( Fig 5A ) . We also examined gene expression of CCL-20 and CCL-25 , two important mucosal homing chemokines [43 , 44] , in C . albicans- and CMV-specific CD4 T cells . As described earlier ( Fig A in S1 Appendix ) , Ag-specific CD4 T cells were sorted from PBMCs based on CFSE-low and expression of these two genes was quantified by real-time PCR . We found that consistent with expression of mucosal homing receptors , C . albicans-specific CD4 T cells also expressed significantly higher levels of CCL-20 and CCL-25 than CMV-specific CD4 T cells ( Fig 5B ) . These data altogether indicate that in accordance with their Th17-like phenotype , C . albicans-specific CD4 T cells demonstrate a strong mucosal homing potential and may be more likely to migrate to mucosal tissues in HIV-infected subjects . α4β7 integrin can directly interact with HIV surface protein gp120 [45] . Although the role of α4β7 in HIV pathogenesis is not fully clear , it has been suggested that strong α4β7 reactivity may provide an increased fitness for mucosal HIV transmission [45 , 46] . We next investigated potential impact of α4β7 on cytokine expression and HIV infectivity in C . albicans-specific CD4 T cells . We found that IL-17 , IL-2 , IFN-γ and MIP-1β were expressed at comparable levels between α4β7+ and α4β7- subsets ( Fig 5C ) . Analysis of α4β7 expression and HIV infectivity showed no significant difference in HIV infection between α4β7+ and α4β7- subsets as well ( Fig 5D ) . In addition , we used ACT-1 , the anti-human α4β7 antibody known to efficiently block binding of HIV gp120 to α4β7 [47] , to block the interaction between HIV and α4β7 during HIV infection . The data showed that pre-inculcation of PBMCs with ACT-1 led to reduced staining of C . albicans-specific CD4 T cells for α4β7 ( Fig 5E ) ; however , blocking α4β7 could not reduce HIV infection of C . albicans-specific CD4 T cells ( Fig 5E ) . Taken together , our results suggest that unlike CD4 and CCR5 , α4β7 may not be required for in vitro HIV replication in our system , which is consistent with some previous reports [48] . In order to investigate longitudinal impact of HIV on C . albicans- and CMV-specific CD4 T-cell immunity in vivo , we first measured the proliferative responses of C . albicans- and CMV-specific CD4 T cells in PBMCs that were collected at early ( mean CD4 count: 797 ) and chronic ( mean CD4 count: 251 ) stages of HIV infection with time intervals of 2–6 years from the same HIV-infected individuals ( Table 1 ) . Ag-specific CD4 T-cell proliferative response in PBMCs was measured using the similar method as described in Fig 1 . Since PBMCs were stimulated with whole C . albicans or CMV antigens , predominantly CD4 , but not CD8 , T-cell proliferative response was induced ( Fig 6A ) . Since in vitro antigen stimulation can lead to significant down-regulation of CD4 receptor , we used CD3+CD8- phenotype to identify CD4 T-cell population following antigen stimulation ( Fig 6A ) . Interestingly , among the subjects examined , we found that while the CMV-specific CD4 T-cell proliferative response was persistent and comparable between early and chronic stages ( early vs . chronic: 14 . 2% to 13%; subject 1 ) , the C . albicans-specific CD4 T-cell proliferative response from the same subjects , which was readily detectable at high magnitudes at early HIV infection , was preferentially lost at late HIV infection ( early vs . chronic: 42% to 1 . 1%; subject 1 ) ( Fig 6A ) . Significant difference for magnitudes of C . albicans-specific CD4 T-cell proliferative responses between early and chronic stages was observed ( n = 4 ) ( p = 0 . 005 ) ( Fig 6B ) . In order to compare the de novo frequencies of C . albicans- and CMV-specific CD4 T cells in PBMCs of HIV-infected subjects , we performed short-term antigen stimulation ( overnight ) , where PBMCs were stimulated with peptide pools derived from C . albicans ( MP65 ) or CMV ( pp65 ) , followed by intracellular cytokine staining . Expression of cytokines ( IL-17 and IL-2 for C . albicans; IFN-γ and MIP-1β for CMV ) was used to determine the frequencies of Ag-specific CD4 T cells in PBMCs . As shown in Fig 6C , while the C . albicans-specific CD4 T cells were readily detectable at fairly high levels early after HIV infection , they were lost or greatly reduced at late stage of HIV infection ( p = 0 . 01 ) ; in contrast , the CMV-specific CD4 T cells were well maintained at comparable levels in both early and late stage of HIV infection from the same HIV-infected individuals ( Fig 6C and 6D ) . The results are very consistent with the proliferation data and altogether provide strong evidence for preferential and progressive depletion of C . albicans-specific CD4 T-cell response in progressive HIV-infected subjects . A better understanding of how pathogen-specific CD4 T cells are infected and/or depleted during HIV infection can provide important clinical insights into host susceptibility to opportunistic infections in AIDS patients . In this study , we used PBMC samples from an ART naïve , longitudinal HIV-infection cohort and reported a sequential dysfunction and preferential depletion of C . albicans-specific CD4 T-cell response , as compared to CMV-specific CD4 T-cell response , in HIV-infected individuals . Our results showed that C . albicans-specific CD4 T cells harbored higher levels of HIV DNA , which supports our in vitro findings and provides in vivo evidence for higher susceptibility of C . albicans-specific CD4 T cells to HIV . Such difference in HIV susceptibility may significantly contribute to their differential depletion rates in vivo . Also importantly , we identified an earlier impairment of Th17-associated functions ( IL-22 , IL-17 and IL-2 ) of C . albicans-specific CD4 T cells at early HIV infection when their proliferative and Th1 responses remain detectable , suggesting that anti-C . albicans cellular immunity may rapidly become inefficient early following HIV infection . C . albicans commensalism in healthy individuals stimulates robust cellular immune responses . Many studies have shown that C . albicans-specific CD4 T-cell response serves as the predominant host defense mechanism for protection [20 , 26 , 49] . However , specific functional facets of anti-C . albicans CD4 T-cell responses responsible for immune control have been obscure . Earlier studies reported that Th1 response was the key mediator of immunity , based on the observation that deficiency of IL-12 p40 subunit in mice was associated with susceptibility to C . albicans; however , studies also showed that mice deficient in IFN-γ were still resistant to candidiasis [31] . It was later recognized that IL-12 shares the p40 subunit with IL-23 , which promotes the differentiation of Th17 subset of CD4 T cells [32] , suggesting a role for Th17 , but not Th1 , response in protection against candidiasis [25] . In support , a recent study by Santos et al . showed that Th17 CD4 cells confer the long-term adaptive immunity to oral C . albicans infections in a murine model [33] . Th17 cells produce two major cytokines , IL-17 and IL-22 , which function to mobilize neutrophils and to enhance mucosal epithelial integrity respectively , and are shown to play important roles in host defense against mucosal candidiasis [50–53] . In our study , we identified that in the setting of HIV infection , C . albicans-specific CD4 T-cell responses manifest a sequential dysfunction with Th17-like functions ( IL-17 , IL-22 and IL-2 ) being impaired earlier following HIV infection as compared to Th1 functions ( IFN-γ and MIP-1β ) ( Fig 3 ) . In support , in vitro HIV susceptibility analysis showed that the IL-17 , IL-22 and IL-2 functional subsets of C . albicans-specific CD4 T cells are more permissive to HIV than the IFN-γ and MIP-1β subsets ( Fig 2 ) . These data suggest that during HIV infection anti-C . albicans CD4 T-cell immunity , even in the presence of detectable proliferative and Th1 responses , might quickly become less efficient due to preferential impairment of Th17 functions . However , it is interesting to note that unlike mucosal candidiasis , disseminated candidiasis is remarkably uncommon in HIV-infected subjects . The mechanisms are not entirely clear but likely due to the relative normal functions of neutrophils in HIV-infected individuals [54] . Neutrophil activation requires IL-17 signaling and recent evidence has suggested that innate lymphoid cells ( ILC ) represent an important source of IL-17 to support neutrophil activation in the absence of Th17 CD4 T cells [55] , which may help explain why disseminated candidiasis remains uncommon even in HIV-infected patients with severe CD4 T cell depletion . Mechanisms for in vivo depletion of CD4 T cells in HIV-infected subjects might be highly complex . However , direct HIV infection and ongoing viral replication is thought to be a major driving factor for CD4 depletion at both acute and chronic stages of the disease [13 , 56 , 57] . The in vitro system established in our group provides a method to examine differential HIV susceptibility of antigen-specific CD4 T cells and the associated functional profile ( Fig A in S1 Appendix ) . Based on this system , we have demonstrated that C . albicans-specific CD4 T cells are substantially more susceptible to HIV than CMV-specific CD4 T cells in vitro ( Fig 2A ) [12] . In the current study , we further investigated HIV susceptibility of these two Ag-specific CD4 T-cell populations in vivo . Like previously reported [13 , 40] , we used quantitative PCR to quantify cell-associated HIV load in sorted Ag-specific CD4 cells as an indication of their natural infection history . Ag-specific CD4 T cells were sorted from PBMCs based on CFSE-low , which provided an advantage in that we could obtain Ag-specific cells at relatively higher numbers for subsequent PCR . We showed that peripheral C . albicans-specific CD4 T cells harbored modestly but significantly higher levels of HIV DNA than CMV-specific CD4 T cells in HIV-infected individuals ( Fig 4B ) . We noted that the differences for HIV load between C . albicans- and CMV-specific CD4 T cells in some subjects were modest . Considering that lymphoid and mucosal tissues such as GI tract are major sites of HIV infection , we speculate that differences might be more profound for Ag-specific CD4 T cells isolated from lymphoid or mucosal effector sites . Previous studies have investigated in vivo HIV susceptibility of CD4 T cells specific for other important antigens such as MTB and HIV , in addition to CMV [13 , 40] . We here included additional antigens for comparison wit C . albicans ( Fig 4C ) . Since the HIV-infected subjects in RV21 demonstrated very low response to MTB , we were unable to directly compare between MTB and C . albicans in these subjects . Instead , we compared C . albicans antigen with varicella zoster virus ( VZV ) and HIV Env as non-CMV antigen controls . Based on the subjects investigated , we found that C . albicans-specific CD4 T cells also harbored higher levels of HIV DNA than these two groups of antigen-specific CD4 T cells ( Fig 4C ) , suggesting that the higher HIV susceptibility for C . albicans-specific CD4 T cells may not be simply due to enhanced protection of CMV-specific CD4 T cells . An important previous study [40] reported that HIV-specific CD4 T cells are highly susceptible to HIV in vivo , where CD4 T cells specific for all HIV antigens ( gag , env , nef , etc ) were measured together . We here also showed that HIV Env-specific cells are susceptible to HIV , albeit at lower levels than C . albicans-specific CD4 T cells , which might attribute to variations between different cohorts . In addition , it is possible that HIV susceptibility of CD4 T cell subsets specific to different HIV antigens ( e . g . Env vs . Gag or Nef ) may also significantly vary , which is under investigation in our group . Nevertheless , our results altogether suggest that C . albicans-specific CD4 T cells are highly susceptible to HIV both in vitro and in vivo , which may contribute to their rapid depletion during HIV infection . Phenotypic and functional characteristics of CD4 T cells are shown to be closely associated with their susceptibility to HIV infection , such as expression of IL-2/CD25 [10 , 13] , MIP-1β [13 , 58 , 59] and IL-17 [60 , 61] . Based on the in vitro system ( Fig A in S1 Appendix ) , we showed that compared to MIP-1β or IFN-γ , the IL-17 , IL-22 or IL-2-producing subsets of C . albicans-specific CD4 T cells are more permissive to HIV ( Fig 2 ) . We noted that although total IFN-γ+ CD4 T cells are also fairly susceptible to HIV , single IFN-γ+ CD4 T cells in the absence of other cytokine expression ( IL-17 , IL-2 or IL-22 ) are substantially more resistant to HIV ( Fig 2C ) , which is consistent with and possibly provides an explanation for earlier reports that IFN-γ+IL-2+ double-producing CD4 T cells are preferentially lost , while the IFN-γ single-producing CD4 T cells are frequently detected in HIV non-controllers [62] . While definitive molecular mechanisms for high HIV susceptibility of C . albicans-specific CD4 T cells remain not fully clear , data from the current study and our previous reports have suggested that multiple layers of mechanisms may contribute to this observation: 1 ) less protection of C . albicans-specific CD4 T cells from HIV at entry level due to limited production of beta-chemokines ( Fig F in S1 Appendix and [12] ) ; 2 ) permissive post-entry environment that favors productive HIV replication , including low expression of antiviral restriction factors [12] and high expression of pro-inflammatory cytokines such as IL-17 , IL-22 , and IL-2 ( Fig 2B and 2C ) ; 3 ) higher expression of important mucosal homing receptors ( α4β7 and CCR6 ) that enhances exposure of C . albicans-specific CD4 T cells to HIV at mucosal sites ( Fig 5 ) . These multiple layers of mechanisms may act together to render C . albicans-specific CD4 T cells susceptible to HIV infection and depletion . α4β7 is a key gut mucosal homing receptor that can directly interact with HIV gp120 [45] . It has been suggested that strong α4β7 reactivity may provide an increased fitness for mucosal HIV transmission [45 , 46] . Our results showed that expression of α4β7 does not appear to directly correlate with HIV infectivity in CD4 T cells ( Fig 5D ) , which is further supported by the α4β7 blocking experiment ( Fig 5E ) . In agreement with this result , further analysis also showed no difference in cytokine expression ( IL-17 , IL-2 , IFN-γ and MIP-1β ) between α4β7+ and α4β7- subsets of C . albicans-specific CD4 T cells ( Fig 5C ) . These results imply that unlike CD4 and CCR5 , α4β7 may not be critically important for in vitro HIV replication in our system , where both cells and virus were present at high concentrations and HIV may be able to efficiently bind to CD4 and CCR5 even in the absence of α4β7 . This is consistent with some previous reports [48] and results from other groups ( personal communication ) . However , impact of α4β7 on HIV susceptibility of Ag-specific CD4 T cells in vivo deserves further investigation . Mechanisms for how expression of cytokines is associated with HIV susceptibility for Ag-specific CD4 T cells are not fully known . We examined CCR5 expression on different functional CD4 subsets and found no significant difference ( Fig E in S1 Appendix ) ; instead , we found that IL-2+ , IL-22+ or IL-17+ CD4 T cells express lower levels of MIP-1β compared to IFN-γ+ CD4 T cells ( Fig 3A ) , which might explain the differential HIV infectivity between these cytokine-producing CD4 subsets . This is consistent with an earlier study showing that in vitro differentiated IL-17-producing CD4 T cells express comparable levels of CCR5 with IFN-γ-producing CD4 T cells , but are more susceptible to HIV due to lack of beta-chemokine production [38] . Another potentially important correlating factor for HIV susceptibility in different cytokine+ Ag-specific CD4 T cell subsets is CD25 . In this study , we showed that productive HIV infection was predominantly observed in CD25+ Ag-specific CD4 T cells and that CD25 co-expressed with IL-2 , IL-22 or IL-17 , but not IFN-γ+ or MIP-1β ( Fig 2E–2G ) . Lastly , post-entry mechanisms might also be involved in regulating HIV susceptibility in different cytokine-producing CD4 T cells . For instance , polarized Th1-like , IFN-γ-producing CD4 T cells , such as CMV-specific CD4 T cells , which can acquire direct antiviral functions [58] , were shown to be able to activate a broad array of innate antiviral factors and manifest strong post-entry inhibition of HIV replication [12] . Molecular mechanisms for how different cytokine signaling may affect HIV infectivity in target CD4 T cells remain largely unknown and further investigation is warranted . In summary , in the present study , based on an ART naïve HIV infection cohort , we comparatively investigated the longitudinal impact of HIV on C . albicans- and CMV-specific CD4 T-cell immunity in HIV non-controllers . We identified a sequential dysfunction and preferential depletion of C . albicans-specific CD4 T cell response during progressive HIV infection . These findings may provide an immunological basis for early loss of immune control over mucosal candidiasis in HIV-infected individuals and also suggest a potential mechanism for pathogen-specific immune failure in AIDS . The study involves use of PBMC samples from healthy human donors as well as from HIV-infected subjects enrolled in RV21 cohort , an ART naïve longitudinal HIV infection cohort established by US MHRP . Healthy donor PBMCs were obtained from University of Texas Medical Branch ( UTMB ) blood bank and the RV21 PBMC samples were obtained from MHRP . Characteristics of HIV-infected subjects were summarized in Table 1 . All samples were analyzed anonymously and investigators of this study have no access to any subject identification information . The study was determined as non-human subject research and approved by both UTMB and MHRP IRBs . Written informed consents were obtained from study participants . Antigens used for long-term ( 6 days ) or short-term ( overnight ) stimulation of PBMCs include: C . albicans extracts ( Greer Laboratories ) , C . albicans MP65 peptides ( JPT ) , varicella zoster virus lysates ( Advanced Biotechnologies ) , CMV lysates ( Advanced Biotechnologies ) , CMV pp65 peptides and HIV Env peptides ( NIH AIDS reagent program ) . HIV-1 US1 ( GS004 ) , an R5 subtype B isolate , was obtained through the NIH AIDS reagent program and used for in vitro HIV infection . CFSE labeling and antigen stimulation of PBMCs were performed as previously described [12 , 34] . Briefly , PBMCs were washed twice with staining buffer ( RPMI-1640 medium containing 1% FBS ) ( Life Technologies , USA ) , followed by labeling with 1 . 0 μM CFSE ( Life Technologies , USA ) at a cell concentration of 2×107 cells/ml for 8 minutes at room temperature ( RT ) in dark . Equal volume of pre-warmed FBS was then added to cells for incubation at RT for 4 minutes to quench CFSE . CFSE-labeled PBMCs were subjected to antigen stimulation and various subsequent assays . For antigen stimulation , cells were first pulsed with whole antigens ( C . albicans: 1:200; CMV: 5 μg/ml ) at high cell concentrations ( 1×107 cells/ml ) for 3–4 hours in tubes , and then diluted to 2×106 cells/ml for normal cell culture in culture plate for several days to stimulate antigen-specific T cell activation and proliferation . For experiments involving cell sorting and HIV DNA quantification for PBMCs of HIV-infected subjects ( RV21 ) , cells were also stimulated with VZV antigen ( final concentration: 5μg/ml ) and HIV Env peptides ( final concentration: 1μg/ml ) . Different assays were subsequently performed as detailed below . For normal PBMCs , stimulated cells were cultured for ~6 days and the proliferating CD4 T cells , identified by CFSE dilution ( CFSE-low ) , in stimulated PBMCs were subjected to multiple analyses: 1 ) cytokine expression by flow cytometer; 2 ) Ag-specific CD4 cell sorting using FACS Aria for subsequent gene-expression analysis . In addition , the Ag-stimulated normal PBMCs were also subjected to in vitro HIV infection ( day 3 after initial Ag stimulation ) for the examining the HIV susceptibility of Ag-specific CD4 T cells . For PBMCs from HIV-infected subjects , CFSE-labeled and antigen-stimulated cells were cultured for ~6 days and subjected to: 1 ) analysis for cytokine expression and proliferative response ( CFSE-low ) ; 2 ) Ag-specific CD4 cell sorting for the in vivo HIV infectivity analysis . Methods for each assay were detailed below . Three days after CFSE labeling and initial antigen stimulation , PBMCs of healthy donors were infected with pre-titrated HIV R5 ( US1; 50ng/ml p24 ) . In some experiments , prior to HIV infection , antigen-stimulated cells were pre-incubated with anti-human α4β7 antibody ( ACT-1 ) ( 5 μg/ml ) to block the interaction between HIV ( envelope protein ) and α4β7 present on antigen-specific CD4 T cells . Twenty-four hours after HIV exposure , free HIV virions were washed away from the cell culture . The infection was maintained for additional 2 days and cells were re-stimulated with PMA ( 500 ng/ml ) and ionomycin ( 1μg/m ) for de novo cytokine synthesis ( 3 days after HIV exposure ) . Productive HIV infection of antigen-specific CD4 T cells in PBMCs and the associated functional ( cytokine ) or phenotypic parameters were examined by multi-color flow cytometry based on intracellular HIV p24 expression in CFSE-low proliferating CD4 T cells as previously described [12 , 34] . In addition to 6-day stimulation , PBMCs from RV21 HIV-infected subjects ( early and chronic time points ) were also stimulated with C . albicans and CMV antigens for overnight in the presence of anti-CD28/CD49d antibody cocktail and protein transport inhibitors ( BD Bioscience ) . For short-term stimulation , C . albicans MP65 and CMV pp65 peptides were used . Intracellular cytokine staining and flow cytometric analysis were performed , as detailed below , to measure de nova frequencies of antigen-specific CD4 T cells in PBMCs of HIV-infected subjects . Sorting antigen-specific CD4 T cells from PBMCs was performed either for quantification of cell-associated HIV DNA ( HIV-infected subjects in RV21 ) or for gene-expression analysis ( healthy PBMC ) . CFSE-labeled , Ag-stimulated PBMCs were first stained with Fixable LIVE/DEAD Violet Dead Stain Kit ( Life Technologies ) , followed by staining for surface markers including CD3-APC-H7 , CD4-PE-Cy5 and CD8-PE ( BD Bioscience ) . Cells ( from HIV+ subjects ) were then fixed and subjected to sorting of antigen-specific CD4 T cells , based on CFSE-low CD3+ CD8- , as well as the CFSE-Hi non-specific CD4 T cells using FACS Aria ( BD ) . Sorted cells were subsequently subjected to quantification of HIV DNA ( below ) . Cells from healthy donors were live sorted for antigen-specific CD4 T cells , followed by RNA extraction and gene-expression analysis ( below ) . Total RNA was extracted from live-sorted , antigen-specific CD4 T cells using Quick-RNA MicroPrep kit ( Zymo ) according to the manufacturer's protocol . Gene expression was quantified using iTaq Universal SYBR Green Supermix ( Bio-rad ) and the CFX Connect Real-Time PCR Detection System ( Bio-rad ) after reverse transcription from RNA into cDNA using iScript Reverse Transcription Supermix for RT-qPCR ( Bio-rad ) . Primers were designed to amplify the target genes ( human T-bet , EOMES and RORC ) . Primer sequences for gene expression analysis were shown in Table 2 . The relative quantity of gene expression was calculated using the 2-ΔΔCt method . Genomic DNA was extracted from the fixed , sorted antigen-specific CD4 T cells in PBMCs of HIV+ subjects . After washing once in PBS , sorted cells were lysed in lysis buffer ( 10mM Tris , 5mM EDTA , 1% SDS pH8 . 0 ) for 1 hour at room temperature and then digested with 32 U/ml of Protease K ( New England Biolabs ) for 2 hours at 56°C . After Protease K inactivation at 95°C for 30 min , genomic DNA was purified and solved in Tris-Cl ( 10 mM , PH 8 . 0 ) . HIV DNA was quantified using iTaq Universal SYBR Green Supermix ( Bio-rad ) and the CFX Connect Real-Time PCR Detection System ( Bio-rad ) according to the manufacturer's protocol . Primers used to amplify HIV Gag and the control GAPDH genes were shown in Table 3 . pNL4-3 and recombinant plasmid encoding GAPDH gene were used to generate standard curves ( Fig I in S1 Appendix ) . The absolute quantity of HIV DNA copies was calculated based on standard curves . Statistical analysis was performed using Prism 6 . 0 ( GraphPad ) . Statistical comparison between groups was performed using paired or non-paired t test . Two-tailed p values were denoted , and p values < 0 . 05 were considered significant .
HIV infection is closely associated with enhanced host susceptibility to various opportunistic infections ( OIs ) , among which mucosal candidiasis caused by the fungal pathogen Candida albicans ( C . albicans ) is an early and common manifestation . Even in the era of effective ART , mucosal candidiasis is still a clinically relevant presentation in HIV-infected patients . The underlying mechanisms are not well defined . CD4-mediated immunity is the major host defense mechanism against C . albicans . We here investigated a group of ART naïve , HIV-infected human subjects and examined longitudinally the impact of HIV on C . albicans-specific CD4 T-cell immunity as compared to CD4 T-cell immunity specific for CMV , another opportunistic pathogen that usually does not cause active disease in early HIV infection . We found that C . albicans-specific CD4 T cells were more susceptible to HIV in vivo and were preferentially depleted in progressive HIV-infected individuals as compared to CMV-specific CD4 T cells . Of importance , we also found that in these HIV-infected subjects C . albicans-specific CD4 T cell response manifested a sequential dysfunction with earlier impairment of Th17 , but not Th1 , functions . Our study suggests an immunological basis that helps explain the earlier and more common onsets of mucosal candidiasis in progressive HIV-infected patients .
You are an expert at summarizing long articles. Proceed to summarize the following text: Intestinal parasitic nematode diseases are one of the great diseases of our time . Intestinal roundworm parasites , including hookworms , whipworms , and Ascaris , infect well over 1 billion people and cause significant morbidity , especially in children and pregnant women . To date , there is only one drug , albendazole , with adequate efficacy against these parasites to be used in mass drug administration , although tribendimidine may emerge as a second . Given the hundreds of millions of people to be treated , the threat of parasite resistance , and the inadequacy of current treatments , new anthelmintics are urgently needed . Bacillus thuringiensis ( Bt ) crystal ( Cry ) proteins are the most common used biologically produced insecticides in the world and are considered non-toxic to vertebrates . Here we study the ability of a nematicidal Cry protein , Cry5B , to effect a cure in mice of a chronic roundworm infection caused by the natural intestinal parasite , Heligmosomoides bakeri ( formerly polygyrus ) . We show that Cry5B produced from either of two Bt strains can act as an anthelmintic in vivo when administered as a single dose , achieving a ∼98% reduction in parasite egg production and ∼70% reduction in worm burdens when delivered per os at ∼700 nmoles/kg ( 90–100 mg/kg ) . Furthermore , our data , combined with the findings of others , suggest that the relative efficacy of Cry5B is either comparable or superior to current anthelmintics . We also demonstrate that Cry5B is likely to be degraded quite rapidly in the stomach , suggesting that the actual dose reaching the parasites is very small . This study indicates that Bt Cry proteins such as Cry5B have excellent anthelmintic properties in vivo and that proper formulation of the protein is likely to reveal a superior anthelmintic . Neglected tropical diseases ( NTDs ) have a worldwide devastating impact on the lives of billions of people . Helminth infections comprise approximately 85% of the NTD burden [1] . The top three ailments on this list of NTDs are all caused by intestinal nematodes [2] . These infections consist of ascariasis ( caused by Ascaris lumbricoides ) , trichuriasis ( caused by Trichuris trichiura or whipworm ) , and hookworm disease ( caused by Necator americanus and Ancylostoma duodenale ) . Approximately 807-1 , 221 million people are afflicted with ascariasis , 604–795 million with trichuriasis , and 576–740 million with hookworm infections [3] . The widespread and detrimental effects of parasitic worm infections on human growth , nutrition , cognition , school attendance and performance , earnings , and pregnancy have been well documented [2] , [3] . These infections also contribute to increased severity/infectivity of HIV/AIDS , malaria , and tuberculosis due to compromised immune responses [3] , [4] . Furthermore , parasitic nematode infections confound vaccination efficacy [5] , [6] . Despite the high prevalence and destructive nature of these infections , there are few treatment options . Although four anthelmintics ( levamisole/pyrantel and mebendazole/albendazole ) are approved by the World Health Organization for use in humans , one , albendazole , is generally preferred in a single-dose regimen over the others since it is relatively more effective against hookworms and whipworms [7] , [8] . However , resistance to albendazole may already be appearing [9] , [10] . Furthermore , the reliance upon one compound for treating hundreds of millions of people will have devastating consequences if widespread resistance ever becomes a reality . Tribendimidine , developed by the Chinese Centers for Disease Control and Prevention , is emerging as a second anthelmintic with efficacy similar to albendazole , but is a member of the levamisole/pyrantel class to which resistance in human populations has been reported [11] , [12] , [13] . Furthermore , none of the compounds have been shown to be totally effective against all helminth infections [8] . Consequently , there is an urgent need for efficacious , safe , inexpensive , single-dose anthelmintics with new mechanisms of action . This search for new anthelmintics has led to examination of Bacillus thuringiensis ( Bt ) crystal ( Cry ) proteins . These proteins are the most extensively used biologically-produced insecticides in the world [14] . Bt is a soil bacterium that produces crystal inclusions during sporulation . These inclusions contain Cry proteins that are highly toxic to some invertebrates but nontoxic to humans and other vertebrates [15] . The high efficacy against insects , absence of toxicity towards vertebrates , and low production cost of these proteins has led to their widespread use in pesticides and in transgenic crops [14] . So far , three Bt Cry proteins toxic to a broad range of free-living nematodes and the free-living form of at least one intestinal parasitic nematode have been discovered , including: Cry5B , Cry14A , and Cry21A [16] . Cry13A may also have anti-nematode activity [17] . To date , only one of these , Cry5B , has been shown to be therapeutic in vivo with activity against intestinal hookworm parasite ( Ancylostoma ceylanicum ) infections in hamsters when delivered daily , per os , over the course of three days [18] . These studies suggest that Cry proteins could provide therapy for intestinal nematode infections . However , it remains to be shown that Cry5B can effect a cure against more than A . ceylanicum infections in hamsters or that Cry proteins are efficacious as single-dose anthelmintics . Heligmosomoides bakeri ( formerly known as Heligmosomoides polygyrus and Nematospiroides dubius ) is one of the most widely studied rodent intestinal parasite nematodes [19] , [20] . The nematode has a high infection rate and is the best model for chronic intestinal nematode infections in immunocompetent mice . H . bakeri has also played a key role in the history of anthelmintic development via its use in the discovery of ivermectin [21] . In addition , H . bakeri infections in mice are a naturally occurring infection , unlike A . ceylanicum infections in hamsters . Thus , curative experiments in H . bakeri are complementary to those in A . ceylanicum , yielding important information as to how Cry proteins may fare against a broad range of natural intestinal parasites in vivo . Herein , we report our investigations into single-dose Cry5B therapy against H . bakeri . Female Swiss Webster white mice were purchased from Harlan Laboratories and were infected at approximately 6 weeks of age at an average weight of 25g . Mice were provided with food and water ad libitum . ) . This research was approved by the UCSD Institutional Animal Care and Use Committee ( IACUC ) , protocol number S08140 . The maintenance and care of experimental animals complied with the University of California's Animal Care Program's guidelines for the humane use of laboratory animals . Crystal-deficient Bt strains HD1 and 4Q7 were transformed with a plasmid containing the Cry5B gene [23] . Bacillus thuringiensis subspecies kurstaki HD1-4D8 was ordered through the Bacillus Genetic Stock Center . Spore lysates ( SLs; HD1 and 4Q7 Cry-deficient strains ) and spore-crystal lysates ( SCLs; HD1 and 4Q7 transformed with Cry5B plasmid ) were prepared using standard methods and then stored at −80° [23] . Bioactivity of SCLs was confirmed against Caenorhabditis elegans by a mortality assay over 24 h at 25°C . SLs ( Cry-minus ) were confirmed to lack toxicity against C . elegans . On the day of use , SL and Cry5B SCL aliquots were thawed and centrifuged at 4 , 500 rpm for 15 minutes at 4°C and the supernatant was removed . The pellet was then resuspended in distilled water to a final concentration of 2 . 5 mg/mL , for the HD1 strain , and 2 . 25 mg/mL , for the 4Q7 strain ( protein concentrations were determined by comparing Cry5B band intensities for four different aliquots of SCLs to known amounts of bovine serum albumin on Coomassie-stained 8% SDS polyacrylamide protein gels ) . The placebo SL control strains were concentrated to the same extent . The samples were kept on ice until gavage . On day 0 , mice were infected per os with a suspension of 200±10 H . bakeri L3 larvae in 0 . 1 mL of distilled water . Larvae were counted under the microscope , then drawn into a pipette tip and placed into separate glass test tubes until gavage with a blunt-ended syringe . On days 14 , 16 , 18 , and 20 post-infection ( P . I . ) , fecal samples were collected from the mice . Mice were placed individually in empty plastic cages for 1 h each morning , and the fecal pellets were collected into 50 mL centrifuge tubes . The number of eggs present was counted using the modified McMaster technique [22] . Briefly , feces collected from mice were weighed and resuspended in a 1 g:15 mL volume of water . The pellets were allowed to soak overnight before being broken up for 1 h via heavy vortexing . The eggs were counted using a 2-chamber McMaster slide , each chamber holding a 0 . 6 mL volume of a 1∶1 mixture of fecal slurry and saturated sucrose solution . The number of eggs per gram of feces was thus calculated from the following equation: number of eggs counted x ( 1/0 . 3 mL slurry ) x ( 15 mL slurry/g feces ) . For each mouse and each time point , three different egg counts were made and then averaged . Each mouse was treated per os on day 15 P . I . with 0 . 1 mL of relevant treatment ( placebo or Cry protein ) through a blunt-ended syringe . All mice were killed by exposure to CO2 on day 20 P . I . and the intestines were removed in their entirety . These were opened longitudinally with a pair of blunt-ended dissecting scissors and then placed into a 50 mL centrifuge tube with 10–20 mL of pre-warmed ( 37°C ) PBS for approximately 1 h to allow worms to dislodge from the intestine . The solution and intestine were examined under a microscope , using fine tweezers when necessary for further extrication of worms from the intestine , for determination of final worm burden . Tribendimidine was kindly provided by Dr . Shu-Hua Xiao at the Chinese Centers for Disease Control and Prevention . The drug was suspended in 20 mM citrate buffer pH 7 . 3 and delivered per os on day 15 P . I . in a total volume of 0 . 1 mL as per Cry5B experiments . For these curative experiments , the mice were infected with on average 150 L3 larvae ( six/group except for placebo group , which only had five mice ) . Placebo control for these experiments was 0 . 1 mL of buffer only . Simulated gastric fluid ( SGF ) was prepared freshly as described in the United States Pharmacopeia and stored at 4° until use [24] . Cry5B SLC was added to a 1 mL solution of SGF for a final concentration of 2 . 5 mg/mL and incubated at 37°C [25] . 50 µL aliquots of the digestion stock were removed at each time point as the digestion solution was agitated . Each aliquot was immediately quenched by neutralization with 15 µL of 0 . 2 M sodium carbonate per 50 µL of SGF [25] . Quenched samples were kept on ice until 2x SDS-PAGE loading buffer was added to each sample . Mixtures were then heated for 5 min in boiling water and stored at −20°C until analysis . Data analysis of intestinal worm burdens and fecal egg counts was carried out and plotted using Prism 5 ( GraphPad Software Inc . , La Jolla , CA , U . S . A . ) . For worm burdens , average indicates the average worm burdens amongst all the mice in each treatment group . For fecal egg counts , average indicates the egg count per mouse averaged from all mice in the group at a given time point . Fecal egg count data was analyzed via pair-wise comparisons between groups and days through two-way analysis of variance ( ANOVA ) with repeated measures and Bonferroni post tests . Results were as follows: FTreatment = 70 . 69 , degrees of freedom ( df ) = 1 , P<0 . 0001; FTime = 5 . 241 , df = 3 , P = 0 . 003; FInteraction = 11 . 43 , df = 1 , 3 , P<0 . 0001 . Worm burdens for Cry5B treatment versus placebo were compared using Mann-Whitney U test ( one-tailed ) . Values are as follows: U = 7 . 5 , P = 0 . 0007 for HD1 Cry5B versus placebo; U = 2 . 0 , P = 0 . 0012 for 4Q7 Cry5B versus placebo . Worm burdens for tribendimidine experiment were compared using one-way ANOVA and Tukey's Multiple Comparison Test ( F = 9 . 387 , df = 3 , 19 , P = 0 . 0005 ) . To determine if Cry5B could provide therapy as a single-dose anthelmintic against H . bakeri , we treated H . bakeri-infected mice with Bt spore-crystal lysates expressing or not expressing ( placebo control ) Cry5B . When the bacterium Bt sporulates , it produces spores , large crystal protein-containing inclusions , and lysate produced when the mother cell that gives rise to the spore and crystal lyses upon completion of sporulation . Bt spore-crystal lysates ( SCLs ) from many Bt strains , including Bt kurstaki HD1 that targets caterpillars ( Lepidoptera ) , have been extensively tested against mammals ( including humans ) and found to be non-pathogenic [15] , [26] , [27] . We transformed a crystal protein-minus HD1 strain with a Cry5B-expressing plasmid . Twenty mice were infected with H . bakeri larvae . Fifteen days post-infection ( P . I . ) , we delivered into each mouse per os either a single 0 . 1 mL dose of Cry5B-containing HD1 SCLs ( 715 nmoles/kg or 100 mg/kg of Cry5B ) or , as a placebo control , a single 0 . 1 mL dose of spore lysates ( SLs , crystal-minus ) from the parent , untransformed HD1 strain . Beginning the day before treatment ( day 14 P . I . or day -1 treatment ) , and then continuing every other day ( day 1 , 3 , 5 post-treatment ) , we collected fecal samples from each mouse to measure parasite progeny production ( eggs/gram of feces ) . Five days after treatment ( day 20 P . I . ) , the mice were euthanized and the total number of parasites present in the small intestine tallied . With regards to progeny production , we found that on the day prior to treatment , the parasites in both groups of mice ( placebo treated and Cry5B treated ) were producing statistically indistinguishable amounts of eggs ( Figure 1 , Table 1 ) . At days 1 , 3 , and 5 post-treatment , the placebo group showed no reduction in egg production , consistent with the hypothesis that the parent Bt strain alone has no effect on the parasites . In contrast , a rapid and remarkable reduction in egg production took place in the Cry5B-treated animals , resulting in a 95% , 99% , and 98% reduction on days 1 , 3 , and 5 post-treatment respectively ( Figure 1 , Table 1 ) . With regards to parasite clearance , we found that the single dose of Cry5B achieved a remarkable therapeutic effect , clearing away 67% of the parasites relative to placebo control ( Figure 2A , Table 2 ) . Thus , a single dose of Cry5B has strong effects on parasite reproduction and the ability of parasites to maintain an infection . The reduction in fecal egg count ( >97% ) was much larger than would be expected from the final mouse worm burden of the SCL-treated animals ( 67% cleared ) . There are at least two possible explanations for this—either the treatment was affecting the status of the worms so that any worms left behind were severely compromised in health or the treatment was preferentially eliminating female over male parasites . To distinguish between these possibilities , we made a note of the number of females present in placebo versus Cry5B treated controls during the counting of the worm burdens . In placebo treated mice we found that there were 35 . 3±5 . 9 ( standard error of the mean , or sem ) females while in the Cry5B treated mice there were 7 . 8±2 . 3 ( sem ) females per mouse intestine . Thus , there was a 78% reduction in the number of females present . This drop , although greater than that for males ( 51% reduction ) , does not seem sufficient to account for the observed >97% drop in egg production seen . These data suggest that the parasites that remained in the intestine were severely compromised in health . We also determined if this capacity to clear an infection was dependent upon a particular Bt strain . We performed a similar curative experiment , measuring intestinal worm burdens after treatment , using the Bt strain 4Q7 ( derived from Bt israelensis , which targets Diptera ) either transformed with the Cry5B-expressing plasmid or untransformed . Fourteen mice were infected with H . bakeri larvae , and fifteen days P . I . a single dose of 0 . 1 mL Cry5B-containing 4Q7 SCLs or 0 . 1 mL 4Q7 SLs ( crystal-minus ) were delivered per os . The dose delivered per mouse was 644 nmoles/kg ( 90 mg/kg ) . We found a similar therapeutic effect as above—71% of the parasites were cleared relative to placebo control ( Figure 2B , Table 2 ) . We note that in this experiment the total number of parasites present in the small intestine in placebo control animals was greater than in the first experiment . The variability appears to be due to relative infectivity of different batches of L3 parasite larvae . These data demonstrate that , regardless of parent Bt strain and of the initial parasite load , a similar single dose of Cry5B is able to achieve comparable therapeutic effect . These results are significant when the relative efficacy of Cry5B is compared to other standard anthelmintic treatments . Published reports , employing a treatment timeline against H . bakeri parasites that is similar to our own , show that levamisole ( 10 mg/kg or 49 µmoles/kg delivered on day 12 P . I . ) effected a 90% reduction in worm burdens and ivermectin ( 5 mg/kg or 5 . 7 µmoles/kg ) or pyrantel ( 50 mg/kg or 84 µmoles/kg ) or piperazine ( 4000 mg/kg or 46 mmoles/kg ) delivered on day 18 P . I . effected an 87% , 98% , and 34% reduction in worm burdens respectively ( [28] , [29] . Another study showed that 2 . 9 µmoles/kg of ivermectin delivered on day 10 P . I . effected ∼70% reduction in H . bakeri worm burdens [30] . We could not find comparable studies with H . bakeri and benzimidazoles , although we did find that mebendazole delivered for 7 consecutive days , starting day 9 P . I . at 22 mg/kg/dose or 75 µmoles/kg/dose , achieved an 84% reduction in worm burdens [31] . Benzimidazoles ( including albendazole ) in general seem to be less active against H . bakeri [32] . Therefore , our single dose of ∼700 nmoles/kg ( which is the highest dose we can currently pipette with SCLs ) that achieved ∼70% reduction in worm burdens is 70X , 4–8X , 120X and 65 , 000X lower than the doses of levamisole , ivermectin , pyrantel , and piperazine used in the above studies . This comparison suggests that the efficacy of Cry proteins relative to known anthelmintics is excellent . To directly compare our results to a known anthelmintic using the same treatment conditions , we performed curative experiments using the newest human anthelmintic and the only one taken to human clinical trials in the past thirty years , tribendimidine . We performed dose-dependent curative assays with tribendimidine against H . bakeri infections , finding an estimate dose of ∼1 mg/kg or 2 . 2 µmoles/kg tribendimidine to give a curative effect similar to ∼700 nmoles/kg Cry5B ( Figure 3 , Table 3 ) . Based on this comparison , Cry5B is at least as good as tribendimidine at curing H . bakeri infections and in fact appears to be ∼2–3 fold superior . These data indicate that Cry5B is an excellent anthelmintic when delivered at a single dose . However , Cry proteins are thought to be digested rapidly in the mammalian digestive tract , most notably by the acidic stomach [33] . If so , then it is possible that the dose of Cry protein reaching the parasites might have been very small . To determine how well Cry5B would survive the mammalian stomach , we incubated Cry5B HD1-derived SCLs in simulated gastric fluids . We find that Cry5B is almost completely digested in this environment within four minutes ( Figure 4 ) . These data suggest that very little Cry5B is actually reaching the parasites . Our study demonstrates that the Bt Cry protein Cry5B is an excellent anthelmintic in vivo against a natural and chronic intestinal roundworm infection in mice , namely H . bakeri . Cry5B is able to achieve significant reductions in parasite egg production ( ∼98% ) and intestinal worm burdens ( ∼70% ) following a single dose delivered per os at ∼700 nmoles/kg . This therapeutic effect , on a mole-by-mole basis , is on par with or superior to those of other anthelmintics commonly used in human therapy . Although this level of efficacy may seem surprising at first glance , upon deeper reflection it is not . Cry proteins , although they only attack the gut cells of invertebrates , are pore-forming toxins ( PFTs; [34] ) . PFTs are the single most common virulence factors made by pathogenic bacteria and are also used by our immune system to combat pathogens [35] , [36] . PFTs are potent weapons and the consequences of their attack on the integrity of the plasma membrane are great . In combination with previous data showing that Cry5B is also able to cure A . ceylanicum infections in hamsters [18] , we have now demonstrated in vivo anthelmintic activity of Cry5B against two very different parasitic nematodes ( one a blood feeder , the other not ) in two different mammalian hosts . Taken together , along with the fact that Cry5B is active against Nippostrongylus brasiliensis larvae , against Haemonchus contortus larvae in vitro , against a phylogenetically wide range of free-living nematodes , and against the plant-parasitic nematode Meloidogyne incognita [16] , [17] , [37] , our data indicate that Cry5B has very broad anti-nematode activity and that Cry5B has superb potential in human anthelmintic therapy . As a natural product , it is interesting to compare the efficacy of Cry5B to other natural product anthelmintics . No recently investigated biological treatments against H . bakeri demonstrate comparable in vivo efficacy using single-dose regimens . Many of these natural compounds , such as the extract of Embelia schimperi , nitazoxanide , santonin , and Myrsine Africana , showed only small reductions in intestinal worm burden as a single dose , with efficacies of 30% , 21% , 18% , and 10% , respectively [29] , [38] , [39] . A single dose of 500 mg/kg of Albizia anthelmintica , not only revealed low efficacy , with a total worm burden reduction of only 3–23% , but also displayed significant toxicity [28] . Even the macrolactam N-methylfluvirucin , delivered at a daily dose of 50 mg/kg over 3 days , effected only a 42% reduction in total worm burden [40] . While other compounds were more efficient , they required extremely high doses and/or multiple-day dosing regimens . These included a daily treatment of ethanol extract of Canthium manni ( Rubiaceae ) at 5600 mg/kg , which showed a 75% decrease in fecal egg count and 84% reduction in worm burden with 7 days of treatment [31] . A 600 mg/kg treatment with extract of stem bark of Sacoglottis gabonensis was extremely effective , but exceedingly toxic , with mice showing signs such as depression , drowsiness , unsteady gait and paralysis of the hind limbs , dyspnoea , coma and death apparent within 1–2 min following intraperitoneal injection [41] . Perhaps the most promising of other natural treatments is papaya latex . A single-dose administration of papaya latex at 8 g/kg achieved an efficacy of 84 . 5% , with fecal egg count reductions of 93 . 3% [42] . Mice treated daily over 7 days with 133 nmoles of papaya latex showed a decrease in fecal egg count of 87–97% and a 92% reduction of worm burden [43] . In general , few of the natural compounds tested above proved to be practical treatments due to dosing and toxicity issues . It is clear that Cry5B has great promise as an effective , safe , and much-needed addition to anthelmintic therapy . The vertebrate and human safety profiles of Cry proteins are outstanding—Cry proteins as insecticides are used around the world on a large-scale in organic farming , in aerial spray campaigns , and in vector ( mosquito , black fly ) control programs and have even been approved for expression in transgenic foods such as corn , potatoes , and rice [14] , [44] . Although Cry5B has not been studied in this regard , it is a member of the same family of three-domain Cry proteins expressed in transgenic crops and used in all these spray programs and thus is predicted to have the same safety profile . Indeed , extensive research from our laboratory has confirmed that the receptor Cry5B needs to bind to in order to intoxicate nematodes is an invertebrate-specific glycan ( carbohydrate ) [45] . It is interesting to note that , although Cry5B has comparable if not superior activity against H . bakeri on a mole-by-mole basis with other anthelmintics , it is likely that only tiny amounts of the protein being delivered per os in our experiments are reaching the parasites . In four minutes , virtually all Cry5B is degraded in simulated gastric fluids . These experiments suggest that a simple enteric coating to protect Cry proteins against the stomach while releasing it in the small or large intestines might greatly increase the efficacy of Cry proteins . These data thus emphasize the importance of formulation in the next stage in the evolution of Cry protein anthelmintic development and suggest that such a formulation has the potential to reveal an anthelmintic with therapeutic properties comparable or superior to those currently in use .
Intestinal parasitic nematode diseases infect over one billion people and cause significant disease burden in children ( growth and cognitive stunting , malnutrition ) , in pregnant women , and via their dampening of the immune system in infected individuals . In over thirty years , no new classes of anti-roundworm drugs ( anthelmintics ) for treating humans have been developed . Because of limitations of the current drugs and the threat of parasite resistance , new anthelmintics are needed . The soil bacterium Bacillus thuringiensis ( Bt ) produces crystal ( Cry ) proteins that specifically target and kill insects and nematodes and is used around the world as a safe insecticide . Here we test the effects of the Bt Cry protein Cry5B on a chronic , natural intestinal roundworm infection in mice , namely the helminth parasite Heligmosomoides bakeri . We find that a single dose of Cry5B can eliminate 70% of the parasites and can almost completely block the ability of the parasites to produce progeny . Comparisons of Cry5B's efficacy with known anthelmintics suggest its activity is as good as or perhaps even better than those currently used . Furthermore , this protein is rapidly digested by simulated stomach juices , suggesting that protecting it from these juices would reveal a superior anthelmintic .
You are an expert at summarizing long articles. Proceed to summarize the following text: The olfactory information that is received by the insect brain is encoded in the form of spatiotemporal patterns in the projection neurons of the antennal lobe . These dense and overlapping patterns are transformed into a sparse code in Kenyon cells in the mushroom body . Although it is clear that this sparse code is the basis for rapid categorization of odors , it is yet unclear how the sparse code in Kenyon cells is computed and what information it represents . Here we show that this computation can be modeled by sequential firing rate patterns using Lotka-Volterra equations and Bayesian online inference . This new model can be understood as an ‘intelligent coincidence detector’ , which robustly and dynamically encodes the presence of specific odor features . We found that the model is able to qualitatively reproduce experimentally observed activity in both the projection neurons and the Kenyon cells . In particular , the model explains mechanistically how sparse activity in the Kenyon cells arises from the dense code in the projection neurons . The odor classification performance of the model proved to be robust against noise and time jitter in the observed input sequences . As in recent experimental results , we found that recognition of an odor happened very early during stimulus presentation in the model . Critically , by using the model , we found surprising but simple computational explanations for several experimental phenomena . Understanding how a brain encodes and decodes olfactory input has been an active field of study for decades [1 , 2] . The relatively simple circuitry in the insect brain for odor processing offers a good opportunity to understand the basic principles of sensory processing in brains . Some findings have been key in understanding how the insect brain makes sense of the olfactory information that it acquires from the outside world: ( i ) There are three stages of stimulus processing: in the antennae , the receptor neurons bond with odorants creating a time-invariant spatial pattern of activations in these neurons , which is sent to the antennal lobe [3] . In the antennal lobe , the projection neurons ( PNs ) react with odor-specific spatiotemporal patterns [4] , whose duration far surpasses that of the stimulus itself [5] . In the mushroom body ( MB ) , the target of the PNs , a small number of highly-specific Kenyon cells ( KC ) respond with short-lived activation periods , often only with a single spike . ( ii ) Odor-specific trajectories can be measured in the PN firing rate phase space , and the separation between the trajectories for different odors is greatest during a period of slow dynamics which lasts for about 1 . 5s after odor onset . ( iii ) The spatiotemporal patterns that arise in the PN population encode the identity of the odor [6] , but can be difficult to differentiate for any two odors [7] . It is only at the KC level that the trajectories are easily identifiable , due to the sparseness of KC responses [2] . In response to an odor , only a few of KCs fire spikes ( population sparseness ) and the firing rates are limited to usually one or two spikes during the presentation of the odor ( lifetime sparseness ) . The causes of this KC sparseness and its precise role in odor decoding are still unknown . It has been suggested that the KCs act as coincidence detectors [5 , 8] , i . e . , a KC becomes active only when a number of its input PNs are active . Another proposal offers an explanation for the lifetime sparseness of the response based on spike frequency adaptation [9] , albeit without providing an explicit functional role for the sparseness . During the period of slow dynamics in the response of the PNs to a stimulus , the firing rates of single PNs rise and fall sequentially in an odor-specific order , creating a sequence of “active” PNs through time [10] . Such a sequence-generating device could be the basis of an odor recognition mechanism at the KC or downstream levels . There are two previous modelling approaches addressing this . The first approach used Lotka-Volterra equations to model PN activations [11] . The solution of these equations visits the vicinity of a set of equilibrium points , giving rise to a trajectory in the phase space akin to those observed in experiments . This model is an abstract system that behaves in a similar way to that observed in the PN population , albeit without providing a mechanism for decoding stimulus responses at the KC level . The second approach presented a computational mechanism through which lifetime sparseness can be achieved in the KC population as information is passed on from the PNs [9]; however , the sequential nature of the activity at both the PN and KC level was not part of this model . Although these two previous computational models addressed fundamental questions , it is still unclear how the PNs and KCs interact mechanistically to enable odor recognition . In particular , the following two key questions remain to be answered: ( i ) How does the insect brain achieve its speed and robustness of odor recognition by transforming highly overlapping spatiotemporal PN patterns into a potentially unique and easily recognizable sequence of KC activations ? ( ii ) What is the mechanism behind the hypothesized coincidence detection of the KCs ? In this work , we present a new model that addresses these two questions . The model combines a nonlinear generative model with approximate Bayesian inference for nonlinear dynamical systems [12–14] . Specifically , as a generative model , we use a modified version of the Laurent-Rabinovich model introduced in [11] , which is based on sequential neuronal dynamics to describe the dynamics of PNs . In the proposed generative model , the PN activity is the input to the model . We propose to use Bayesian inference to infer the states of the hidden variables , i . e . the firing rates of the KCs . The model dynamics exhibit the observed behavior of both PNs and KCs in the insect brain and mechanistically explains how sparse code in the KCs emerges from the dense coding of the PNs . In addition , the Bayesian inference approach enables the recognition of odors from PN activation dynamics , which may be understood as an ‘intelligent coincidence detector’ implemented by the KCs . In sum , we present a model which ( 1 ) replicates experimentally observed dynamics at two hierarchical levels of the odor recognition system of insects , ( 2 ) provides simple explanations for several key experimental findings and ( 3 ) implements fast and robust odor recognition based on firing rate input . Previous efforts for modelling the sequential dynamics during the onset sequential phase of an odor response in the antennal lobe used the Lotka-Volterra equations [11 , 21] . This model used the Lotka-Volterra equations to obtain reproducible sequential dynamics in the PN population . The equations are the following: x˙i=xi ( σi ( I ) +∑i≠jρij ( I ) xj ) +ηi 1 where xi is the firing rate of the i-th neuron , σi is a parameter , η is noise and ρij is the connectivity matrix among the neurons; I is the input to the system , i . e . the odor being perceived . Because of the continuum of possible input odors , the parameters ρ and σ are continuous functions of the input I . Under the conditions over ρij and σi given in [22] the system has a set of equilibrium points Qi = ( 0 , 0 , … , σi , … , 0 ) , where the non-zero entry is at the i-th position , and its solution presents a stable heteroclinic sequence ( SHS ) , which is the union of these equilibrium points and trajectories that join them in a specific sequence . An odor is represented by a sequence of equilibrium points visited by the solution . When presented with a stimulus , which sets a value for σi and the connectivity matrix ρij , the system responds by visiting a sequence of equilibrium points in which the order of the points is constant across trials . Although this model ( which we call the Laurent-Rabinovich model from now on ) captures the most prominent feature of the PN data , i . e . , neuronal sequential activations , the model has several limitations . Firstly , there is no mechanism for how the sensory input is received by the model . This means that the model cannot recognize one specific odor among many possible alternatives but rather follows the dynamics of a specific , pre-set odor . Secondly , as the input is only used to fix the model parameters , there is no way of finding out how robust the model is against sensory or neuronal noise . For example , neuronal noise may mean that for a given odor a PN does not fire although it should , or if a PN does fire although it should not . Clearly , a model of odor recognition should be robust against such neuronal noise . Thirdly , the Laurent-Rabinovich model is meant to model PN activity but does not describe the sparse KC activity . This means that one of the most prominent questions , i . e . how dense PN activity is transformed into the sparse KC code , cannot be addressed by the model proposed in [11] . In this paper , we build on the core idea of the Laurent-Rabinovich model that odor recognition is based on neuronal sequences and extend the model in three ways . Firstly , experimental results show that KCs respond to more than one odor and are activated in small groups [5 , 20] . Motivated by these results , we replace the rather simple neuronal sequences of Eq 1 used in [11] by sequences of small neuronal clusters and use these equations to describe sequences in KCs as opposed to the PNs . As we will show below , this extension massively increases the number of possible odors that can be recognized by the model and is critical in explaining how a KC can represent multiple odors . In addition , this cluster extension makes the decoding at the KC level highly robust against failures of single KCs because at each point in time during odor recognition multiple KCs sparsely share the decoding . Secondly , we combine the KC-cluster sequences with a model for PN activity . This will enable us to model the hierarchical decoding of the dense PN code by sparse KC activity . Thirdly , we combine the resulting PC/KN model with Bayesian inference . This will make the model a recognition model , i . e . the model can receive and decode PN input . Critically , we will show that this recognition model can identify specific odors ( out of a selection of odors ) very rapidly , and is robust against several noise sources , for instance unexpectedly activated/inactivated PNs . In sum , these three extensions enable us to answer our two questions: ( i ) How does the insect brain achieve its speed and robustness of odor recognition by transforming highly-overlapping spatiotemporal PN patterns into a potentially unique and easily recognizable sequence of KC activations ? ( ii ) What is the mechanism underlying the hypothesized coincidence detection of the KCs ? In the following we will describe each of the three extensions in detail . In this section , we will show the usefulness of the model in explaining how odor recognition based on the PN-KC hierarchy can be both rapid and robust . In addition , we will present a mechanistic explanation for several key experimental phenomena . There are five sections , where each addresses a specific experimental aspect . Firstly , we show how the model performs fast odor recognition , in accordance with experiments . Secondly , we show that the experimentally established steady state phase of the odor response can be explained by our model as a prolonged activation of a single neuronal sequence element . Thirdly , we demonstrate the robustness of the model against several types of neurobiologically expected noise . Fourthly , we show how the KCs in our model behave as intelligent coincidence detectors of both PN and KC activity . Fifthly , we show that the model KC trajectories reduced to three dimensions look precisely like their experimentally observed counterparts . This finding in particular indicates that our model captures a fundamental aspect of KC activity measured during insect odor recognition . A key component of our model is the Bayesian inference , which helps the KCs decode the information contained in the PN activity , making the proposed model a recognition model . This decoding mechanism goes beyond a simple feedforward connection from PNs to KCs , in that it balances the expectations that the brain has about the internal dynamics of the KCs with the information contained in the PN activity . The connections that are created between PNs and KCs by the Bayesian inference through the Kalman gain ( see Methods ) are Bayes-optimal , in the sense that they minimize the so-called precision-weighted prediction error to obtain the best results possible . There is an underlying assumption in the proposed model: that PNs and KCs perform computations similar to what Bayesian inference would prescribe given a generative model ( of how PN activity arises from odor reception ) . As we proposed a firing rate model , there is no link yet to the single neuron level . It is unclear how the proposed model can be implemented by network of single spiking neurons , but the idea that the brain behaves as an optimal Bayesian observer in perceptual decision-making tasks has already been substantiated and possible neural circuits have been suggested [18 , 42–45] . In this work , we demonstrate how Bayesian inference , along with an appropriate generative model , can act as a decoding mechanism and explain the performance of the insect brain in recognizing odors , as well as the remarkable robustness , and other phenomena observed in experimental data . The exact way in which the insect brain’s PNs encode the incoming olfactory information is still unclear . Based on the comparison between our model and experimental data , in particular the tri-dimensional trajectories obtained with PCA ( section ‘Projections Of High-Dimensional Trajectories’ ) , we suggest that the antennal lobe encodes the identity of an odor as a sequence of a small number of metastable states in the configuration space of PN firing rates . This means that an odor is represented in the antennal lobe as a collection of points both in the phase space of PN firing rates and the transitions between these points ( i . e . as a stable heteroclinic sequence ) . The exact number of these points may be odor-dependent , but given the speed at which PN activity changes , which has been reported from experimental data to be around 50–300ms , this number could be as low as four . Thus the odor representation could comprise , during the onset sequential phase , five metastable points and the transitions between them . Sequential spiking of neurons or groups of neurons has been modelled before . A well-established model for spiking neurons are synfire chains , where groups of synchronously firing neurons can be set to fire in a specific and reproducible sequence , using feedfoward connections [46 , 47] . In another study , the parameters in the Hodgkin-Huxley model were modified to allow for the sequential firing of neurons in a desired sequence [48] and in [49] , the authors show how sequential switching can be obtained with random connectivity . For decoding , hidden Markov models have been used for identifying clusters of neurons firing together in a trial-invariant sequence as a response to a taste stimulus in rodents , e . g . [50] . In firing-rate models , an influential model for sequential activation is the Laurent-Rabinovich model [11 , 21] , where the connectivity between the neurons establishes a particular sequence of neuronal activations which is followed consistently across different trials . This model introduced the use of the Lotka-Volterra equations for sequential neuronal activations , which have been subsequently used successfully in areas as different as birdsong recognition [51 , 52] , visual perception [53] , handwriting recognition [54] and dendritic dynamics [55] . One interesting feature of the Lotka-Volterra equations as a modelling device of sequential activation of neurons is their remarkable robustness against unexpected input and noise and initial conditions different from zero , their trial-by-trial reproducible dynamics and the mathematical depth at which they have been studied previously [22 , 56] . Here , we incorporated both the sequential neuronal activations in the generative model , as introduced in [11 , 21] , and the decoding mechanism to identify in an online fashion the odors with Bayesian inference . The SHS in the generative model grants the model with robustness against noise in the neuronal dynamics , which is further improved by the Bayesian inference which gives the model additional robustness against neuronal and sensory noise . The inclusion of clusters of KCs in our model , coupled with the ability of the Bayesian inference to reconcile conflicting data , gives the model a reliable and flexible mechanism as implemented by PN and KC activity . The insect brain is able to recognize many odors and even mixtures of odorants with different ratios [6 , 25] and for each one of these , a representation exists in the brain . In the proposed model , a high number of these representations can be encoded in a single connectivity matrix , like in the insect brain . Exactly how many sequences can be stored and recognized by the model will depend on a number of factors , most notably on the size of the KC population . Here , we discuss the capacity of the model in terms of the KC population’s size . To explore the capacity of the model , we ran simulations with bigger population sizes to test how many sequences can be embedded while maintaining the model’s ability to accurately recognize them . We ran multiple simulations and found that the maximum number of sequences in a model grows faster than the size of the KC population and this growth is non-linear ( e . g . polynomial ) . Importantly , we found that for KC numbers below 100 , the system can store and accurately recognize only as many different odors as there are KCs ( 90 ) . However , when going beyond 100 KCs , the number of odors that can be stored and recognized grows faster than the number of KCs . For example , for 500 KCs , 900 odors could be stored and recognized accurately ( success rate > 95% ) . Simulations with more than 500 KCs are technically possible but we had to abandon these simulations because the computer run times became prohibitive ( > 1 week on a modern desktop computer to assess the 500 KCs recognition performance ) . We are confident that the current Matlab implementation can be improved upon using an implementation using parallel computing ( as used by the insect brain ) . It is an open question how many different odors could be recognized with 50 , 000 KCs ( as in the locust ) . The proposed model would be an ideal tool to address this question , once the implementation is fast enough . For future work , there are at least three ways in which one can improve the model’s capacity further , i . e . increase the number of recognizable odors while the number of KCs remains the same . Firstly , in this work we chose the sequences randomly , which means that some KCs can belong to many clusters . A more careful selection of the clusters in the sequences will most likely lead to an increase in the number of recognizable sequences . We expect that this is precisely what is happening in an insect brain , where KC connections are optimized , probably both during lifetime and by evolutionary processes . Secondly , the exact shape of the expected sequences ( both at the PN and KC levels ) plays an important role in the recognition . For our simulations , we used sequences similar to those in Fig 5B , where all KCs in a cluster rise at the same time and to the same maximum firing rate . A less stringent definition of a “sequence” will most likely lead to a much larger number of embeddable sequences . Thirdly , the exact weights of the connections between KCs may be improved further . While those used throughout this paper ( see Fig 1 ) give accurate recognition results , a further optimization of the connectivity scheme could lead to a further improvement in model capacity . We implemented a Bayesian inference scheme that allows the system to identify a perceived stimulus . Similar setups , with Bayesian inference working on a generative model based on SHS , have proven fruitful in other applications [14 , 51 , 54] . In general , Bayesian inference observes variables whose states are known to be caused by other , hidden variables . Through the nonlinear equations of a generative model , which describe the way in which the hidden states cause the observed states , Bayesian inference balances the observed data with what it knows of the dynamics of the generative model in order to estimate the values of the hidden states that best fit the observed variables . We made use of the unscented Kalman filter ( UKF ) as a Bayesian inference implementation [57] . The UKF works by comparing the data being observed at a given time step with a prediction made by the UKF in the previous step . These predictions are made using the generative model’s equations , so they follow its dynamics , and are done both for the observation and hidden variables . The difference between the observation and the prediction , called prediction error , is computed for both levels . These two prediction errors and the equations in the generative model are used to make the next-step prediction for both levels , such that the prediction error of one level can affect the prediction of the other . This back and forth exchange of information is what creates the extra connections in the model ( i . e . the connections from PNs to KCs ) . At every time step in the process , these calculations and predictions are made and the expectations adjusted . More specifically , the Bayesian inference continuously infers the states of the KCs from observed PN firing rates that were generated by Eq 5 . We used the unscented Kalman filter ( UKF ) to estimate the states of the hidden variables xt ( the firing rates of the KCs ) from the observed data yt ( the PN firing rates ) . At every time step t , the current estimates xt and yt ( starting with some initial conditions for the first step , which we set to all zeroes or randomly ) , and a minimal set of points surrounding them , called sigma points , are propagated through the nonlinear equations of the generative system ( Eq 6 ) ; assuming that the distribution of the hidden variables is Gaussian , a prediction for the mean and the variance of the hidden variables ( KCs ) and the observed variables ( PNs ) is calculated as a weighted sum of the propagated sigma points . In the next time step , these predictions are used to estimate the current states of the hidden variables with the update equation xt=x¯t+K ( yt−y¯t ) , where the Kalman gain K represents the precision expected from the data relative to the precision expected from the nonlinear dynamics of the system and is computed with the covariance matrices of the hidden and observed variables as calculated with the sigma points . In preparation for the next time step , a new prediction is calculated using the current estimation and the process is repeated . The filter takes as input the PN activity and as priors an initial condition of the KC population and the covariance matrix for the noise vectors ( H ) i = ηi from Eq 3 and Γ from Eq 5 . We set the initial state of the KCs to zeroes ( or randomly , for testing for robustness ) and the noise covariance matrices ( priors ) are unit matrices multiplied by two different constants ( Q for the hidden states , R for the observed states ) . These constants , called precisions , determine the relative importance given either to the observations ( PN readings ) or to the internal dynamics of the KCs . For all our simulations , we used values of Q = 0 . 1 and R = 0 . 001 . These values were chosen because they provide the best balance between following the observed PN data and following the expected KC dynamics . Given that Bayesian inference does not require the entire data set , but only the data for the given step and the prediction from the previous one , this process can be done online , that is , it can be done as data acquisition is happening , without having to wait for the entire process to be over . This characteristic makes the Bayesian inference a plausible mechanism through which the brain can decode the information encoded in the PN responses . In this section , we present an overview of the steps necessary to implement our model . To complement this , source code for the implementation , as well as examples , can be found at [https://github . com/dcuevasr/Olfaction/] . The simulations are divided in two steps: firstly , data generation; secondly , inversion . For the data generation phase , the following steps were followed: ( 1 ) Set-up the parameters of the system , e . g . population sizes . ( 2 ) Select the clusters that will form the sequences . We did this randomly , minimizing repetition of neurons in different sequences . ( 3 ) Create the connectivity matrix , embedding all the desired sequences in it . ( 4 ) Set-up the initial conditions for all the neurons . This step determines which sequence will be generated: to generate data with a sequence , the initial conditions must be around the first equilibrium point of that sequence , i . e . the first cluster of the sequence must be activated . ( 5 ) Integrate Eq 3 to obtain KC activity . ( 6 ) Randomly generate an observation matrix and generate PN activity using Eq 5 . Of special note are the algorithms for creating the clusters and sequences , and for generating the connectivity matrix . These can be seen in the files cgenerate_clusters . m and cget_rho . m , respectively . Additionally , the generation of the observation matrix can be seen in the file cgenerate_neurons . m . For the inversion phase , the steps are the following: ( 1 ) Set the precision parameters . ( 2 ) Set the initial conditions , which were typically set to zeroes . ( 3 ) Call the UKF using the PN data generated previously as input . The implementation of the UKF is contained in the file UKF . m . To generate the illustrative raster plots shown in Fig 3 , we adapted our generative model to generate spikes while keeping its fundamental structure . We created a hierarchical three-level model , where the third ( top ) level employs the proposed cluster-encoding Lotka-Volterra equations ( see Fig 1 ) . These are output to the second level , representing the KCs ( Fig 3A , right panel ) , which are modeled using FitzHugh-Nagumo equations to generate spikes [59] . The input from the third to the second level raises the membrane potential in the FitzHugh-Nagumo equations to the threshold of spiking . Spikes are generated by adding noise to pass the firing threshold . For simplicity , we used Gaussian noise . The states of the second level ( representing the membrane potential of the KCs ) are the input to the first ( lowest ) level , which represents the PNs , also modeled with the FitzHugh-Nagumo equations ( see Fig 3A , left panel ) in the same way: Only when the input from the second level takes the membrane potential close to threshold , spikes are occasionally generated both by Gaussian noise and by the spikes of the second level . The connections between the PNs and the KCs in this model are made in the same way as in our model: by a random 1 to 20 projection from KCs to PNs with equal weights . This model was only used to generate the data to create the plots in Fig 3 . Because of this , we did not apply Bayesian inference to it . For a full description of the three-level hierarchical model see S1 Text . When making decisions and to generate reaction times , we compare the KC response obtained by the Bayesian inference with the expected KC response ( used to generate the data ) . In particular , we compute the Euclidean distance between the two trajectories in the firing rate phase space of the KCs and define recognition of an odor as correct , if the Euclidean distance drops below a threshold anytime during the trial . We further defined reaction time as the time at which the threshold is crossed . We set a threshold of 0 . 1 for our simulations , which is 0 . 1 times the maximum value of the firing rate of a KC . We found that once this threshold is crossed , the two trajectories ( inferred and the data ) do not drift apart for the rest of the response , making this threshold a good assessment of whether the inference is choosing the correct representation . In the section ‘Intelligent coincidence detector’ , we make use of this criterion to compare the performances of our model and the single-neuron modification in order to quantify the effect of the lateral inhibition received by KCs from those KCs with which it shares a cluster . For each model , we ran a hundred trials each for all combinations of the number of extra-noisy PNs ( between one and twenty ) and extra noise added to these PNs ( with SNRs of 2 , 1 . 25 and 1 ) . For each of these trials , we considered the odor to be properly identified if the Euclidean distance between the inferred and expected responses was lower than the 0 . 1 threshold . In Fig 6 , the variable Recognition , which goes from -1 for the incorrect representation to 1 for the correct ( expected ) one , was calculated using the following formula: R=Dc−DiDri 8 where R is the recognition , Dc is the distance between the observed KC activity and the correct ( expected ) one; Di is the distance between the observed activity and the incorrect one ( the other sequence in the system ) and Dri is the distance between the two sequences embedded in the connectivity matrix . We created PN activity which presents the three phases of the response to an odor ( see Fig 7 ) . To do this , we generated the response in the KCs for one sequence for the first phase , then repeated the last point in the sequence for a certain time to generate the response in the second phase; finally , we added the response of a second sequence ( embedded in the same connectivity matrix ) at the end for the third phase ( Fig 7B ) . We created the PN response with a 1:20 projection ( i . e . each KC is connected to 20 PNs ) , as used in the other simulations . Using this full response PN activity as input to the Bayesian inference , we measured the inferred KC responses .
Odor recognition in the insect brain is amazingly fast but still not fully understood . It is known that recognition is performed in three stages . In the first stage , the sensors respond to an odor by displaying a reproducible neuronal pattern . This code is turned , in the second and third stages , into a sparse code , that is , only relatively few neurons activate over hundreds of milliseconds . It is generally assumed that the insect brain uses this temporal code to recognize an odor . We propose a new model of how this temporal code emerges using sequential activation of groups of neurons . We show that these sequential activations underlie a fast and accurate recognition which is highly robust against neuronal or sensory noise . This model replicates several key experimental findings and explains how the insect brain achieves both speed and robustness of odor recognition as observed in experiments .
You are an expert at summarizing long articles. Proceed to summarize the following text: Training can modify the visual system to produce a substantial improvement on perceptual tasks and therefore has applications for treating visual deficits . Visual perceptual learning ( VPL ) is often specific to the trained feature , which gives insight into processes underlying brain plasticity , but limits VPL’s effectiveness in rehabilitation . Under what circumstances VPL transfers to untrained stimuli is poorly understood . Here we report a qualitatively new phenomenon: intrinsic variation in the representation of features determines the transfer of VPL . Orientations around cardinal are represented more reliably than orientations around oblique in V1 , which has been linked to behavioral consequences such as visual search asymmetries . We studied VPL for visual search of near-cardinal or oblique targets among distractors of the other orientation while controlling for other display and task attributes , including task precision , task difficulty , and stimulus exposure . Learning was the same in all training conditions; however , transfer depended on the orientation of the target , with full transfer of learning from near-cardinal to oblique targets but not the reverse . To evaluate the idea that representational reliability was the key difference between the orientations in determining VPL transfer , we created a model that combined orientation-dependent reliability , improvement of reliability with learning , and an optimal search strategy . Modeling suggested that not only search asymmetries but also the asymmetric transfer of VPL depended on preexisting differences between the reliability of near-cardinal and oblique representations . Transfer asymmetries in model behavior also depended on having different learning rates for targets and distractors , such that greater learning for low-reliability distractors facilitated transfer . These findings suggest that training on sensory features with intrinsically low reliability may maximize the generalizability of learning in complex visual environments . Training in fundamental visual perceptual tasks can lead to substantial improvement , a phenomenon known as Visual Perceptual Learning ( VPL ) , which is associated with adult brain plasticity . VPL has powerful real-word applications [1–3] including improving the vision of adults with cortical blindness [4] , amblyopia [5–7] and presbyopia [8] . VPL is often specific to the trained feature and location ( reviewed by [9] ) . From a theoretical point of view , specificity can provide important insight into the neuronal mechanisms that underlie VPL . For example , specificity has been taken to imply plasticity in early-stage visual processing ( e . g . , [10 , 11] ) . However , from a practical or clinical viewpoint , specificity can be a major obstacle in the development of effective training protocols , and it is therefore critical to understand the factors that determine VPL specificity and the conditions that lead to transfer . For complete transfer to occur , the visual system needs to apply learning for one stimulus to another stimulus . The ability to generalize improvements across stimuli may be most likely when the representation of the stimuli is intrinsically similar . However , the visual system has intrinsic variations in its representation of different feature values . In particular , the reliability with which different feature values are represented can vary considerably within a feature dimension . For example , the reliability of orientation representation in V1 strongly depends on the orientation value . Cells responding to orientations around cardinal are larger in number and have smaller response variability compared to cells responding to orientations around oblique [12 , 13] . In human V1-V3 , sensory uncertainty estimated from the fMRI BOLD signal is higher near oblique orientations than near cardinal orientations , which correlates with orientation estimation behavior [14] . These studies show a gradual variation in representational reliability as a function of orientation , with higher reliability for orientations closer to cardinal ( especially horizontal ) and lower reliability for more oblique orientations . These intrinsic differences have been linked to substantial behavioral effects unrelated to learning . They explain the advantage that observers have in discriminating orientations around cardinal compared to around oblique [13–16]: the oblique effect [17]; and in detecting oblique targets among cardinal [18–20] or near-cardinal [21] distractors over the reverse: orientation search asymmetry [22] . Explanations of search asymmetry propose that oblique distractors have less reliable representations than cardinal distractors and thus hinder target detection more [19 , 20] . Intrinsic variations in representational reliability are not limited to orientation; for example , stimulus processing also varies across spatial frequency [23] . Thus far , however , no study has directly investigated the effect of these preexisting variations in representational reliability on VPL transfer and specificity . Investigation of VPL has focused instead on the manipulation of task properties . By varying task difficulty [10 , 24] and task precision ( e . g . , orientation difference in a discrimination task; [25] ) , researchers varied the representational precision required to successfully perform the task , and studied its effect on VPL specificity . However , variability in task demands is distinct from initial variability in the underlying representation and may invoke different learning mechanisms . For example , increased specificity in difficult or high-precision tasks has been attributed to changes in the modulated level of representation in the visual processing hierarchy [10 , 24] , whereas intrinsic differences in representational reliability are present within the same hierarchical level . Here , we asked whether variations in representational reliability alone can explain VPL and its specificity and transfer , when task properties such as difficulty and precision are the same . Our results show that near-cardinal and oblique orientations not only yield an orientation search asymmetry [18–22] but also show asymmetric transfer of VPL in visual search . Conversely , task difficulty , which was independently manipulated by varying the stimulus onset asynchrony ( SOA ) between a mask and the search display , did not affect the pattern of transfer . To test the sufficiency of a reliability-based account , we fit a computational model that combines learning-related increases in the reliability of stimulus representations with a Bayesian search strategy based on Ma et al . [26] . This Bayesian search model was well-suited to test our hypothesis , because it explicitly represents orientation reliability . Using an unchanging optimal decision rule , the model accounts for both search and transfer asymmetry via initial differences in near-cardinal and oblique orientation reliability . To test for learning , we compared the first and the last training days . For all three dependent measurements ( sensitivity , bias and RT ) we conducted a ( 2X2X2 ) three-way mixed design analysis of variance ( ANOVA ) with training effect ( training day 1 vs . 6 ) and SOA ( 35 , 59 , 94 and 129 ms ) as within-observers factors and group ( near-cardinal vs . oblique training ) as a between-observers factor . To test for the transfer of learning for each of the three dependent measurements , we conducted a ( 2X2X2 ) three-way mixed design ANOVA with tests ( color test vs . orientation test ) and SOA ( 35 , 59 , 94 and 129 ms ) as within-observers factors and group ( near-cardinal vs . oblique group ) as a between-observers factor . As Fig 1E reveals , whereas color test performance was very similar to the last day of learning in both groups , orientation test performance was dependent on the group . Because baseline performance ( training day 1 ) for the near-cardinal condition was lower than for the oblique condition , it may be that during the orientation test ( when target and distractor orientations swapped ) specificity was inflated by the baseline difference . In order to control for this possibility , we additionally assessed transfer and specificity by comparing performance ( d′ ) in the orientation transfer test from one group with the baseline performance ( training day 1 ) and trained performance ( training day 6 ) of the other group , such that the orientation condition was the same within each comparison ( Fig 2B ) . First we tested whether transfer performance is higher than baseline , which would indicate that at least some learning partially transferred to the untrained orientation . Two independent sample one-tailed t-tests revealed significant transfer both to near-cardinal and to oblique orientations , t ( 8 ) = 3 . 08 , p = 0 . 007 , Cohen’s d = 1 . 94 , t ( 8 ) = 2 . 01 , p = 0 . 039 , Cohen’s d = 1 . 28 , respectively . Next we tested whether transfer performance is different than trained performance; a difference would indicate specificity , while no difference would indicate full transfer of learning to the untrained orientation . Two independent sample t-tests revealed significant partial specificity following oblique orientation training , t ( 8 ) = 2 . 96 , p = 0 . 018 , Cohen’s d = 2 . 05 , but not following near-cardinal orientation training , t<1 . The same results were obtained when a nonparametric test was used ( S1 Table ) . Thus , learning only partly transferred to the near-cardinal orientation but fully transferred to the oblique orientation . Because we found that VPL specificity and transfer depended on the trained orientation–despite equated task difficulty and task precision–we hypothesized that differences in the representational reliability of near-cardinal and oblique orientations may lead to both search and VPL transfer asymmetries . To investigate this possibility , we used computational modeling . We developed a model that consists of two parts: optimal orientation search [26 , 28] and reliability improvement over the course of learning . The goal was to determine whether orientation reliability and its improvement with learning could explain the behavioral data . We compared four models to test different hypotheses about the role of orientation reliability in learning and transfer in the orientation search task . We tested whether initial reliability differences between near-cardinal and oblique orientations alone ( Reliability model ) , different learning rates for targets and distractors alone ( Learning model ) , both of these factors together ( Reliability-and-Learning model ) , or these factors with independent learning rates for the two groups ( Reliability-Learning-Group model ) best accounted for the data . Detailed descriptions of the models can be found in the Methods , and all model fits are shown in S1 Fig . Model comparison using the AICc metric indicated that initial reliability differences between near-cardinal and oblique orientations were critical to explain the data . The Reliability model ( three parameters , AICc = 10 . 61 ) and the Reliability-and-Learning model ( four parameters , AICc = 13 . 00 ) outperformed the Learning model ( three parameters , AICc = 21 . 05 ) and the Reliability-Learning-Group model ( six parameters , AICc = 24 . 56 ) . When we compared cross-validated r2 , the Reliability-and-Learning model fit the data better than the Reliability model . For the Reliability-and-Learning model , cross-validated r2 was 0 . 81 ( SD 0 . 09 ) , falling within the noise ceiling ( lower and upper bounds , [0 . 75 0 . 84] ) , Model performance was therefore as good as possible given the noise in the data . For the Reliability model , cross-validated r2 was 0 . 70 ( SD 0 . 24 ) , falling below the noise ceiling . To determine whether transfer and specificity in the two best models could be predicted based only on the learning phase , we fit the models to the training days only and predicted the transfer test performance for each group . For the Reliability-and-Learning model , the predicted orientation test performance was similar to the observed performance , namely , transfer in the near-cardinal group and specificity in the oblique group ( Fig 3A , stars ) . The Reliability model predicted more transfer in the oblique group than was observed in the data ( Fig 3A , plus signs ) , similar to its fit to all data points ( S1 Fig ) . The pattern of transfer and specificity therefore did not depend on including the test session data when fitting the model , and the Reliability-and-Learning model better explained transfer behavior . The Reliability-and-Learning model , then , captured the three key features of the data: 1 ) the search asymmetry at baseline , 2 ) the performance improvement with learning , and 3 ) the orientation dependence of VPL specificity and transfer ( Fig 3A ) . Learning in the oblique group maintained the difference in reliability between the near-cardinal and oblique orientations , thereby maintaining the search asymmetry present at baseline and preventing full transfer . Conversely , learning in the near-cardinal group decreased the reliability difference between orientations , effectively overcoming the search asymmetry and allowing similar near-cardinal and oblique performance by the end of training . Fig 3B shows Reliability-and-Learning model estimates of near-cardinal and oblique reliability as a function of training session for each group . The model estimated greater sensory uncertainty ( lower reliability ) for the oblique than for the near-cardinal orientation , consistent with physiological and behavioral findings [12 , 13] . For the best-fitting parameter estimates , the distractor learning rate was 0 . 65 and the target learning rate was 0 . 24 . Existing models of VPL predict the same level of specificity across the same levels of task-difficulty [24] , task precision [25 , 29] and feature exposure during training [30] . The demonstration that a mere difference in the trained feature value , near-cardinal vs . oblique orientation , determined VPL specificity challenges these views . Supported by computational modeling , we suggest that intrinsic differences in the representational reliabilities of near-cardinal and oblique orientations governed VPL specificity and transfer in orientation search . Our design enabled us to control for the involvement of task-related factors and to assess the effect of representational reliability per se . In both groups the equal orientation difference between targets and distractors ( 30° ) , equated performance controlled by SOA , and identical exposure to the transfer feature insured independence from task precision [25] , difficulty [24] , and feature exposure [30] , respectively . Our analyses confirmed that both learning rate and magnitude were equal for the two groups . In addition , our results cannot be explained in terms of differences in number of difficult trials during training . A larger number of difficult trials during training has been found to increase specificity [31] . This relationship would predict a result opposite to ours: specificity in the near-cardinal group , which was more difficult on average ( across SOAs ) . Thus , stimulus-related properties , rather than task , determined specificity here . The dependence of transfer on the specific orientation value has implications for the investigation and interpretation of VPL transfer and specificity using oriented stimuli . Indeed such stimuli have been commonly used to investigate VPL , including in orientation discrimination tasks ( e . g . , [25 , 30 , 32–34] ) , visual search ( e . g . , [10 , 30 , 35 , 36] ) and texture discrimination tasks ( e . g . , [37–41] ) . Some VPL studies have varied orientation values to manipulate task properties , such as task difficulty , and then linked those task properties to the resulting feature specificity ( e . g . , [10 , 30 , 37] ) . Our study suggests that orientation differences alone can affect the pattern of feature specificity and transfer and therefore should be controlled , particularly in displays with more than one orientation . Researchers have inferred the site of the underlying plasticity in VPL based on specificity and transfer results ( reviewed by [42] ) . Specificity and transfer have been taken to indicate learning in early and late visual areas , respectively [10 , 11 , 24 , 30] . Here we show that preexisting variation in representational reliability , which can occur within the same level of processing , can determine VPL transfer . Our findings , therefore , suggest that specificity and transfer are not always appropriate diagnostic tools for the level of VPL plasticity . Our model combined orientation-dependent reliability , improvement of reliability with learning , and an optimal search strategy . We based the search strategy on the optimal visual search model by Ma et al . [26] , because that model provides a parsimonious explanation of orientation search with minimal parameters . We found that a single change to the model–letting reliability depend on orientation–captured orientation search asymmetry prior to learning . According to the model , the lower reliability of oblique compared to near-cardinal stimuli leads to more uncertainty during the local decision regarding the identity of an item . The disrupting effect of this uncertainty on visual search performance is larger with oblique distractors ( near-cardinal target ) than with near-cardinal distractors ( oblique target ) , simply because there are many distractors but only one possible target in any given display . The improvement of reliability across training days captured the behavioral pattern of both learning and transfer . Importantly , the model uses the same optimal decision rule throughout training and during the transfer tests . Search asymmetry and learning , therefore , could be attributed to variation in sensory reliability only , rather than changes in decision strategy and rule based learning [30] . Comparing alternative versions of the model allowed us to determine which factors were critical to explain the behavioral data . Preexisting differences in reliability were essential–a model without this component failed to fit the data–but independent learning for targets and distractors also improved model performance , particularly in capturing transfer behavior . This result is consistent with a previous study that found independent target and distractor learning in an orientation search task [43 , 44] . Our learning rate estimates correspond well to that study’s finding of about twice as much learning for distractors as for targets [43] . It is therefore the combination of preexisting reliability differences and greater learning for distractors than targets that best explained behavior , in this family of models . Specifically , greater learning for the initially low-reliability oblique distractors eliminated the search asymmetry and enabled full transfer for the near-cardinal group . Our model follows the account that differences between the reliabilities of the cardinal ( or near-cardinal ) and oblique representations cause orientation search asymmetry [18–20] . A key component of these accounts is the ratio of target signal to background noise , which depends on the target and distractor identities [18 , 19 , 45] . Alternative accounts have also been proposed . One influential theory explains visual search asymmetries by considering a map of feature dimensions and their interactions [46] . This theory suggests that targets with larger feature values ( e . g . more oriented , i . e . oblique ) are inherently more detectable than targets with smaller values ( e . g . less oriented , i . e . cardinal ) ( e . g . , [46 , 47] ) . Based on this theory , a neural computational model was developed that explains search asymmetry in terms of a salience map in V1 [48] . However , it is unclear how the elimination of search asymmetry following near-cardinal training could be predicted if search asymmetry arises from inherent feature properties like “more tilted” [46 , 48 , 49] . Moreover , no previous model addresses VPL in orientation search . Analogous to the reliability differences between orientation values represented by our model , neurons responding to oblique orientations have larger tuning curve widths than those responding to near-horizontal orientations in macaque V1 [13] , and there is more cortical area tuned to near-cardinal orientations than to oblique orientations in ferret cortex [12] . Higher sensory uncertainty has also been estimated for oblique compared to near-cardinal orientations in human V1-V3 [14] . The Ma et al . [26] model on which the orientation search component of our model is based has been implemented as a biologically plausible neural network model , strengthening the connection between the physiological literature and our current computational results . Learning was modeled as an increase in the representational reliability of the stimulus orientations . This increase could be implemented either as a reduction of the tuning curve width of V1 or V4 neurons with training [50–53] or as an improvement in readout from the early sensory response [29] . Both mechanisms have been proposed previously for an orientation discrimination task . Our model , therefore , applies VPL principles derived from orientation discrimination tasks to explain VPL for more complex visual search tasks . Our findings are limited to VPL in orientation search , and more study is required to determine whether they generalize to other stimuli and tasks . Our study also does not rule out alternative models for orientation search asymmetry and VPL in visual search , but it shows that a parsimonious optimal decision rule , preexisting differences in orientation reliability , and reliability learning suffice to explain both search and transfer asymmetry . For simplicity our model assumes the same representational reliability for all stimulus locations . However , stimulus reliability can vary as function of eccentricity ( e . g . , [18 , 23] ) and polar angle [54 , 55] . It will be interesting to test the relation between location-dependent feature reliability and VPL transfer and specificity . Researchers have sought to understand the perceptual and neuronal processes that underlie VPL by studying how task demands affect VPL specificity . In the present study we control for task while testing the effect of the intrinsic reliability of feature representations on VPL specificity in visual search . We found a striking difference in VPL transfer depending on the orientation of the trained target , which we interpret as an effect of representational reliability . This interpretation is supported by both previous neurophysiological findings and computational modeling of the present data . We conclude that preexisting variation in the reliability of feature representations within a single level of processing may have a critical effect on VPL transfer and specificity , calling into question the logic that the degree of feature specificity can be used to infer the neural level at which VPL occurs , especially for complex visual displays . A growing body of research demonstrates the potential benefits of VPL in clinical ( e . g . , [4–8 , 56 , 57] ) and professional ( e . g . , [58] ) applications . Our study suggests a testable hypothesis: to increase the generalizability of perceptual learning in real-world applications , efficient training protocols should focus training on low-reliability features–oblique orientations and motion directions [59] , peripheral spatial locations [60] , and so forth–which may limit performance in a variety of natural tasks . We developed a model that consisted of two parts: optimal search ( based on Ma et al . [26] ) and reliability improvement over the course of learning . We compared alternative versions of the model to determine which parameters were required to explain , in a single fit , the data from both observer groups , including the initial search asymmetry , performance improvement over the course of training , and the transfer asymmetry .
Training can modify the visual system to produce improvements on perceptual tasks ( visual perceptual learning ) , which is associated with adult brain plasticity . Visual perceptual learning has important clinical applications: it improves the vision of adults with visual deficits , e . g . amblyopia and cortical blindness , and even presbyopia ( aging eye ) . A critical issue in visual perceptual learning is its specificity to the trained stimulus . Specificity gives insight into the processes underling experience-dependent plasticity but can be an obstacle in the development of efficient rehabilitation protocols . Under what circumstances visual perceptual learning transfers to untrained stimuli is poorly understood . Here we report a qualitatively new phenomenon: specificity in visual search depends on intrinsic variations in the reliability of feature representations; e . g . , vertically oriented lines are represented in V1 with greater reliability than tilted lines . Our data and computational model suggest that training on sensory features with intrinsically low reliability can maximize the generalizability of learning , particularly in complex natural environments in which task performance is limited by low-reliability features . Our study has possible implications for the development of efficient clinical applications of perceptual learning .
You are an expert at summarizing long articles. Proceed to summarize the following text: Axis specification and segment determination in dipteran insects are an excellent model system for comparative analyses of gene network evolution . Antero-posterior polarity of the embryo is established through systems of maternal morphogen gradients . In Drosophila melanogaster , the anterior system acts through opposing gradients of Bicoid ( Bcd ) and Caudal ( Cad ) , while the posterior system involves Nanos ( Nos ) and Hunchback ( Hb ) protein . These systems act redundantly . Both Bcd and Hb need to be eliminated to cause a complete loss of polarity resulting in mirror-duplicated abdomens , so-called bicaudal phenotypes . In contrast , knock-down of bcd alone is sufficient to induce double abdomens in non-drosophilid cyclorrhaphan dipterans such as the hoverfly Episyrphus balteatus or the scuttle fly Megaselia abdita . We investigate conserved and divergent aspects of axis specification in the cyclorrhaphan lineage through a detailed study of the establishment and regulatory effect of maternal gradients in M . abdita . Our results show that the function of the anterior maternal system is highly conserved in this species , despite the loss of maternal cad expression . In contrast , hb does not activate gap genes in this species . The absence of this activatory role provides a precise genetic explanation for the loss of polarity upon bcd knock-down in M . abdita , and suggests a general scenario in which the posterior maternal system is increasingly replaced by the anterior one during the evolution of the cyclorrhaphan dipteran lineage . Axis formation and segment determination in the vinegar fly Drosophila melanogaster are among the most thoroughly studied developmental processes today [1–5] . They offer an ideal starting point for the comparative study of development and the evolution of pattern-forming gene regulatory networks . Axis formation in flies is based on the graded distribution of morphogens established through a number of different maternal regulatory systems . In this study , we will be focusing on two of those in particular: the anterior and posterior systems [4] . In D . melanogaster , maternal protein gradients are either formed by localisation of mRNA at the anterior or posterior pole of the embryo , or by regionally specific translational repression of ubiquitous maternal transcripts [4 , 5] . The anterior system centres around the anterior determinant Bicoid ( Bcd ) . bcd mRNA is localised to the anterior pole of the embryo and an antero-posterior ( A–P ) protein gradient forms through diffusion from that source [6–8] . Bcd regulates the translation of uniformly distributed maternal mRNA of caudal ( cad ) [9 , 10] , which leads to a graded distribution of Cad protein with high concentration levels in the posterior [6 , 9 , 11–14] . In addition , Bcd acts as a concentration-dependent transcriptional regulator of zygotically expressed segmentation genes—such as gap or pair-rule genes [6 , 15–20] . In the case of the posterior maternal system , nanos ( nos ) mRNA is localised in the posterior pole region forming the source of the Nos protein gradient [21–23] . Unlike Bcd , Nos is not a transcriptional regulator: its only role is to translationally regulate ubiquitous maternal hunchback ( hb ) mRNA , leading to an anterior gradient of maternal Hb protein [24–26] . Evidence for the presence of localised determinants in dipterans goes back to early studies that utilised UV irradiation or RNAse treatment on embryos of chironomid midges ( Fig . 1 , Nematocera: Culicomorpha ) . These experiments produced mirror-duplicated abdomens , so-called bicaudal phenotypes , in which anterior structures are missing and replaced by duplicated organs usually found in the posterior [27–29] . The observed effects were attributed to the destruction of an anteriorly localised mRNA . However , the identity of the anterior determinant is still unknown in the majority of dipteran infraorders . The bcd gene arose through a duplication of the hox3 factor zerknüllt ( zen ) at the base of the cyclorrhapha ( Fig . 1 ) [30–33] . While its spatial distribution and role as transcriptional regulator are highly conserved among cyclorrhaphans [30–32 , 34–39] , it is not present in other flies . Interestingly , anterior UV irradiation of D . melanogaster embryos—or mutations to the bcd gene—never produce bicaudal phenotypes [40 , 41] . This hints at the presence of an additional non-localised factor . This factor is hb , which contributes to axis specification and A–P polarity in D . melanogaster . The ubiquitous distribution of its maternal mRNA may explain why it is resistant to localised UV irradiation . This interpretation is consistent with the fact that only embryos lacking both bcd and hb show bicaudal phenotypes in this species [24 , 42 , 43] . While the roles of bcd and hb in axis specification appear to be somewhat redundant in D . melanogaster , the situation is different in other cyclorrhaphan flies . The hoverfly Episyrphus balteatus , for example , has secondarily lost maternal hb expression ( Fig . 1 ) [38 , 44] . Consequently , knock-down of bcd by RNA interference ( RNAi ) leads to bicaudal phenotypes in this species [38] . In this paper , we study axis specification and maternal regulation of segmentation genes in another non-drosophilid cyclorrhaphan species , the scuttle fly Megaselia abdita ( Fig . 1 ) . M . abdita belongs to the most basally branching cyclorrhaphan lineage , the Phoridae [45 , 46] . While maternal cad expression has been lost in this species ( Fig . 1 ) [47] , hb retains its maternal contribution [31] . In light of this , it is surprising that knock-down of bcd does lead to bicaudal phenotypes . We investigate the regulatory causes of this phenomenon through a detailed study of the establishment and regulatory role of maternal gradients in M . abdtia . Our results reveal that the anterior and posterior systems are much less redundant compared to D . melanogaster . In particular , the difference between the two species can be explained by the loss of gap gene activation through maternal hb in M . abdita . Our results indicate that the role of the posterior system in axis specification has been lost in E . balteatus and M . abdita , while it still retains some of its ancestral functionality in D . melanogaster . In this general scenario , the anterior system is gradually replacing the posterior one during the evolution of the cyclorrhaphan flies . The posterior maternal system is based on maternal gradients of Nos and Hb protein . In M . abdita , nos mRNA is localised posteriorly during early cleavage stages ( Fig . 2A ) becoming restricted to the pole cells by C10 ( Fig . 2B ) as in D . melanogaster . Previous reports have documented ubiquitous maternal hb mRNA [31] as well as conserved zygotic hb expression in an anterior and a posterior domain [48] . Antibody stainings reveal a distribution of Hb protein very similar to the zygotic mRNA pattern during the late blastoderm ( Fig . 2C , D ) . Furthermore , an anterior Hb protein gradient is present at cleavage and early blastoderm stages ( Fig . 2E ) . In order to investigate the role of the posterior system in the formation of this gradient , we treated M . abdita embryos with nos RNAi . These embryos show no effect on hb mRNA , while ectopic Hb protein is present in the posterior of the embryo ( effect detectable in 15 out of 16 RNAi-treated embryos; Fig . 2F ) . We conclude that the maternal Hb gradient is set up through translational repression by Nos in M . abdita as in D . melanogaster . The anterior maternal system of M . abdita is less conserved than the posterior one . Unlike D . melanogaster [9 , 10] and E . balteatus [44] , M . abdita lacks maternal cad transcripts [47] and consequently maternal Cad protein . Zygotic expression of cad , on the other hand , is qualitatively similar in D . melanogaster , E . balteatus , and M . abdita [9–12 , 14 , 37 , 42 , 47–49] . The only notable difference is that abdominal cad expression reaches further anterior in the latter two species compared to Drosophila [44 , 48] . In order to test how zygotic cad expression is regulated in M . abdita , we knocked down bcd , hb , and the head gap gene orthodenticle ( otd ) . In bcd RNAi-treated embryos , we observe a derepression of cad transcripts in the anterior ( 38/48; Fig . 3A–F ) . At cleavage cycle 13 ( C13 ) , cad expression appears uniform throughout the embryo ( Fig . 1D ) . During early C14A ( time class 2 , T2 ) , cad becomes expressed at higher levels in the anterior than in the posterior ( Fig . 3E ) . This effect is specifically confined to the region that is free of cad expression in wild-type embryos ( compare to Fig . 3B ) . At later stages , an ectopic domain resembling the posterior cad stripe forms in the anterior ( Fig . 3F ) . Similar ectopic cad stripes have been observed in the anterior of D . melanogaster bcd mutants [14] , cad reporter assays in D . melanogaster [47] , and E . balteatus embryos treated with bcd RNAi [38] . In hb knock-down embryos , we observe a small anterior expansion of cad expression in a minority of specimens ( 4/13; Fig . 3G–I; S1 File ) . Anterior derepression is much more subtle in this case than in bcd knock-downs ( Fig . 3D–F ) . This effect is similar to hb mutants of D . melanogaster [42] . Given the difference between bcd and hb knock-downs , we investigated potential additional contributions by otd , a factor known to act as a transcriptional repressor of cad in the jewel wasp Nasonia vitripennis [50] . otd expression is lost in bcd RNAi-treated embryos ( 12/16; S1 Fig ) . However , expression of cad appears normal in embryos treated with otd RNAi ( 25/25; Fig . 3J–L; S1 File ) . This indicates that otd is not involved in cad regulation , consistent with results from D . melanogaster [50] and E . balteatus [44] . In summary , anterior repression of cad in D . melanogaster is due mainly to a combination of translational repression by Bcd—acting on ubiquitous maternal cad mRNA—and transcriptional repression by hb—acting on the zygotic abdominal cad domain [14 , 42] . Transcriptional regulation of cad by Bcd plays a minor role , if any [47] . In contrast , repression of cad by Bcd occurs predominantly at the transcriptional rather than the translational level in M . abdita , similar to E . balteatus [38] . Our evidence does not conclusively establish whether this interaction is direct . However , we have shown that potential intermediate factors such as Otd and Hb are not involved in cad regulation , or show regulatory effects that are far too subtle to account for anterior repression in M . abdita . Previous work has shown that bcd mRNA is localised anteriorly in M . abdita [30–32 , 48] , and that it regulates hb transcription through the P2 promoter [31 , 37] . To assess the effect of bcd on gap gene regulation and embryo polarity in general , we characterised expression patterns of the trunk gap genes hb , giant ( gt ) , knirps ( kni ) , Krüppel ( Kr ) , and the pair-rule gene even-skipped ( eve ) in M . abdita embryos treated with bcd RNAi . We used single- and double-stained embryos to assess severity of the knock-down and spatial registration of expression patterns—between gap domains ( Fig . 4 ) as well as between Kr and the pair-rule gene eve ( Fig . 5 ) . We take advantage of the variable knock-down efficiency in RNAi experiments , which acts similar to an allelic series in classical genetics , to measure the sensitivity of specific gap domain boundaries towards decreasing levels of Bcd . In general , we find that all of these boundaries are highly sensitive to changes in Bcd concentration ( Figs . 4 and 5; see also S1 File ) . Wild-type embryos of M . abdita show a broad , bcd-dependent , anterior domain of zygotic hb expression , which gradually retracts from the pole ( Fig . 4B ) [31 , 37] . The posterior boundary of this domain shifts in anterior direction over time [48] , unlike its equivalent in D . melanogaster . In embryos treated with bcd RNAi , we observe an anterior cap of hb expression which never retracts from the pole ( 35/42; Fig . 4C–F; S1 File ) . It reduces in size with the severity of the bcd knock-down ( Fig . 4C–E ) indicating dependence on Bcd concentration . Similar anterior domains have been observed in embryos derived from bcd mutant mothers in D . melanogaster [51] and in bcd RNAi-treated embryos of E . balteatus [38] . In both of these cases , the anterior cap of hb expression has been interpreted as an anterior mirror duplication of the posterior hb domain [38 , 51] . The posterior hb domain is also conserved in M . abdita ( Fig . 4B ) [48] . It exhibits a slight anterior expansion in some embryos treated with bcd RNAi ( Fig . 4C–F; S1 File ) . In contrast , the posterior hb domain remains unaffected in D . melanogaster embryos lacking bcd [51] . Wild-type embryos of M . abdita have a broad anterior domain of gt , with a stationary posterior boundary , plus a posterior domain that shifts anteriorly over time ( Fig . 4H ) [48] . In embryos treated with bcd RNAi , we observe either loss ( 10/18 ) or strong reduction ( 8/18 ) of the anterior gt domain at early stages ( before T3 ) , while most embryos exhibit expression in a small anterior cap at later time points ( 14/15; Fig . 4I–L; S1 File ) . This anterior cap retracts from the pole around T8 ( 1/1 ) . As for hb , the extent of anterior gt expression decreases with increasing strength of the knock-down effect ( Fig . 4I–K ) . We interpret these observations as follows: delay and reduction of anterior gt expression are due to a lack of activation by Bcd , while the late anterior cap domain may be induced by ectopically expressed Cad ( see Fig . 3E , F ) . The effect of bcd knock-down on the posterior gt domain is more modest . This domain is always present in bcd RNAi embryos but exhibits some anterior displacement of both its boundaries ( Fig . 4I–L; S1 File ) . D . melanogaster embryos from bcd mutant mothers show a similar anterior displacement of the posterior gt domain , but no expression of gt in the anterior [52 , 53] . In contrast , E . balteatus embryos treated with bcd RNAi show broad derepression of gt , whose expression is only excluded from the anterior and posterior tip of the embryo [38] . In wild-type embryos of M . abdita , kni is expressed in an L-shaped anterior head domain , plus an abdominal domain that shifts to the anterior over time ( Fig . 4N ) [48] . In embryos treated with bcd RNAi , the head domain disappears , while the abdominal domain of kni expands and becomes displaced towards the anterior ( 38/38; Fig . 4O–R; S1 File ) . As in the case of hb and gt , the amount of expansion depends on the severity of the knock-down . This is qualitatively similar to embryos derived from bcd mutant mothers in D . melanogaster , but the effect is more severe in M . abdita and resembles kni expression in bcd mutants which are also heterozygous for maternal hb [24] . The effect of Bcd on kni is even more pronounced in E . balteatus where kni becomes drastically derepressed—showing ubiquitous expression in extreme cases—in embryos treated with bcd RNAi [38] . Wild-type M . abdita embryos have a central Kr domain , which is wider than its equivalent in D . melanogaster ( Fig . 4A , G , M ) [48] . As is the case for other gap domains , it shifts anteriorly and contracts over time . In embryos treated with bcd RNAi , the central domain of Kr expands towards the anterior ( 94/116; Fig . 4C–E , I–K , O–Q; Fig . 5B–F; S1 File ) . Yet again , the extent of the expansion is correlated with the strength of the knock-down . In the strongest cases , Kr expression is entirely missing ( 22/116; Fig . 4E , K , Q ) . A similar expansion of the central Kr domain has been observed in embryos from bcd mutant mothers in D . melanogaster [24] . However , these embryos never show a complete lack of Kr expression; it is only abolished by the additional removal of maternal hb [24 , 54] . Knock-down of bcd in E . balteatus , which lacks maternal hb expression altogether , leads to a complete absence of Kr expression in all RNAi-treated embryos [38] . In summary , our results suggest that Bcd is a concentration-dependent transcriptional regulator of gap genes in M . abdita . The observed effects of Bcd on gap gene expression are more severe than in D . melanogaster ( resembling gap gene patterns in mutants affecting both bcd and hb ) , but milder than in E . balteatus . M . abdita embryos treated with bcd RNAi can exhibit a bicaudal phenotype with complete axis polarity reversal and mirror-duplicated posterior structures in the anterior [31] . These severe knock-down phenotypes have their plane of symmetry at abdominal segment 5 ( A5 ) , and express four eve stripes—the two anterior ones probably being mirror-duplicated stripes 6 and 7 [31] . Such polarity reversal is never observed in embryos derived from bcd mutant mothers in D . melanogaster [41] , only in embryos that lack both bcd and maternal hb [24 , 43 , 54] . While the former still have a residual Kr domain , the latter lack Kr expression completely . Polarity reversal is also observed in E . balteatus embryos treated with bcd RNAi , which show no Kr expression at all [38] . We tested the relationship between the bicaudal phenotype and the presence or absence of Kr by co-staining bcd knock-down embryos for both eve and Kr ( Fig . 5 ) . The pair-rule gene eve is expressed in seven stripes in wild-type M . abdita embryos ( Fig . 5A ) [44 , 55 , 56] . Weak bcd knock-down phenotypes show a full complement of seven eve stripes that are displaced towards the anterior , with a correspondingly mild anterior displacement of Kr ( Fig . 5B; compare to Fig . 4C , I , O ) . Increasing severity of the knock-down results in the progressive loss of anterior eve stripes and more pronounced anterior displacement of the central Kr domain ( Fig . 5C–E; compare to Fig . 4D , J , P ) . In the strongest cases , we detect four eve stripes only ( as in [31] ) , and no or very little Kr expression ( Fig . 5F; compare to Fig . 4E , K , Q ) . This suggests that the absence of Kr expression is correlated with polarity reversal in bcd knock-down embryos . Why does lack of Bcd induce a bicaudal phenotype in M . abdita if it has a maternal Hb gradient very similar to D . melanogaster ? To answer this question , we compared the role of maternal Hb in gap gene regulation in both species . We have previously characterised the effect of Hb on Kr , kni , and gt in M . abdita [48] . Expression of kni and gt in embryos treated with hb RNAi is very similar to the corresponding patterns in hb mutants of D . melanogaster . In contrast , the effect of Hb on Kr differs between the two species: both show an anterior expansion of the central Kr domain ( 24 out of 53 RNAi-treated embryos in M . abdita ) , but only D . melanogaster embryos lacking maternal Hb exhibit a decrease in Kr expression levels [24 , 54] . We never observe such down-regulation in M . abdita embryos treated with hb RNAi ( S2 Fig ) [48] . Together with the absence of Kr expression in strong bcd knock-down phenotypes ( Fig . 4E , K , Q , Fig . 5E ) , this indicates that Hb is unable to activate Kr in M . abdita . In contrast , several authors have interpreted the reduced levels of Kr expression in hb mutants as evidence for activation of Kr by Hb in D . melanogaster [24 , 42] . However , it has never been shown whether this activating effect is direct or indirect—via repression of the repressor Kni by Hb ( see [5] , for a detailed discussion ) . To distinguish between these two possibilities , it is necessary to suppress kni in a background lacking maternal and zygotic hb . Direct activation is supported if levels of Kr expression remain low in embryos lacking both hb and kni , while an indirect effect via kni is supported if Kr levels are restored in these embryos compared to hb mutants alone . Unfortunately , it is not straightforward to create such double mutants , since both hb and kni are located on the same chromosome in the D . melanogaster genome , and germ line clones must be induced to eliminate both maternal and zygotic activities of hb . This may be the reason why this experiment has never been carried out . To overcome this challenge , we used RNAi-mediated double knock-down of hb and kni , and knock-down of hb in a kni mutant background . In D . melanogaster hb knock-down embryos , we observe anterior expansion and strong down-regulation of Kr ( 5/9; Fig . 6A , B; S2 File ) , as well as considerable anterior displacement of kni ( 3/5; S3 Fig; S2 File ) . These patterns correspond precisely to Kr and kni expression in embryos mutant for both maternal and zygotic hb [24] . Similarly , kni knock-down embryos show a Kr pattern which is identical to that observed in kni null mutants: we observe no posterior expansion of Kr ( Fig . 6E; S2 File ) , in accordance with a recent quantitative study [57] , but in disagreement with earlier qualitative reports [58–60] . These results indicate that our early embryonic RNAi knock-downs mimic strong null mutant phenotypes . In D . melanogaster hb/kni double knock-down embryos , we observe an anterior expansion of Kr , but no restoration of expression levels ( 12/18; Fig . 6C; S2 File ) . We confirm this result in kni mutant embryos treated with hb RNAi , which exhibit an identical anterior expansion of Kr and no restoration of expression levels ( 12/14; Fig . 6F; S2 File ) . Taken together , these results demonstrate that kni is not responsible for Kr down-regulation in D . melanogaster embryos lacking maternal and zygotic Hb . Therefore , activation of Kr by Hb is direct in this species . In contrast , this activatory role is absent in M . abdita where Hb acts as a repressor only , which leads to a lack of Kr expression and mirror symmetrical expression of the remaining gap genes in bcd knock-down embryos ( see also Conclusions ) . In D . melanogaster , maternal and zygotic Cad contribute to the activation of posterior gap domains [13 , 42] and—at least partially independently of gap gene regulation—activate posterior stripes of pair rule gene expression [11 , 50 , 61–64] . To investigate the exclusively zygotic contribution of Cad to gap and pair-rule gene expression in M . abdita , we characterised the expression patterns of hb , gt , Kr , kni , and eve ( Fig . 7A–L; S1 File ) as well as the cuticle phenotype ( Fig . 7O ) of embryos treated with cad RNAi . The cad knock-down phenotype of M . abita exhibits deletions of all segments posterior of T3 , and T3 itself is also disrupted in some embryos ( Fig . 7O ) . This phenotype is more similar to D . melanogaster than to E . balteatus . Embryos of the latter treated with cad RNAi exhibit a strongly reduced cephalopharyngeal skeleton , in addition to an almost complete loss of abdomen and thorax [44] . In contrast , D . melanogaster embryos mutant for both maternal and zygotic cad have an intact head and thorax and , although there is extensive loss of abdominal segments , often even retain some abdominal structures [11] . The fact that the M . abdita phenotype is stronger than that of D . melanogaster suggests that cad still plays an essential role in posterior segmentation in this species despite the loss of its maternal contribution . In light of this , it is surprising that knock-down of cad in M . abdita does not have a strong effect on gap gene expression . The only clearly detectable defect is a slightly reduced posterior hb domain ( 7/16; Fig . 7A–C ) . All other domains of hb , gt , Kr , and kni seem unaffected ( Fig . 7A–L; see also S1 File ) . Expression levels of Kr , kni , and gt appear similar to wild-type , although we cannot completely rule out a marginal decrease due to lack of sensitivity of our enzymatic detection method . This stands in contrast to D . melanogaster , where expression levels in the abdominal domain of kni and the posterior domain of gt are reduced in mutants lacking both zygotic and maternal cad ( while hb and Kr are expressed as in wild-type ) [13 , 42 , 50] . In E . balteatus cad knock-down embryos , anterior hb and Kr are normal , while the posterior kni , gt , and hb domains are absent or severely reduced [38 , 44] . To test if activation of gap genes by Cad is present but redundant with the complementary contribution by Bcd , we characterised the expression of kni and gt in embryos treated with RNAi against both bcd and cad ( Fig . 8; see also S1 File ) . We observe a large anterior displacement in the position of the abdominal kni domain ( 36/36 ) , as is seen in bcd RNAi-treated embryos . This was associated with a strong reduction in expression levels , particularly before T3 ( Fig . 8B; 14/15 ) , though after this stage levels of expression begin to resemble those in the wild-type . Embryos treated with bcd or cad RNAi alone , never show such reduction ( Fig . 4N–Q , Fig . 7J , K ) . Expression of gt is absent before T2 ( 6/10 ) , and only becomes detectable as a weak posterior domain at later stages ( Fig . 8D , F ) . In contrast to bcd RNAi-treated embryos ( Fig . 4H–L ) , we do not observe any anterior displacement of this domain ( see S1 File ) . In the anterior , we observe a cap of gt expression at the late blastoderm stage ( Fig . 8H ) , which closely resembles the anterior cap in embryos treated with RNAi against bcd alone ( Fig . 4I , J; S1 File ) . Our observations stand in contrast to those from D . melanogaster mutants lacking both maternal and zygotic cad and bcd . Such mutants show complete absence of both kni and gt expression [13] . Taken together , our results suggest that Cad contributes to early activation of both abdominal kni and posterior gt in M . abdita , in a way which is largely redundant with activation by Bcd . Surprisingly , late expression of both kni and gt in the posterior of the embryo seems to be at least partially independent of both Bcd and Cad activation . This suggests that a third , yet unknown , factor must contribute to gap gene activation in this species . Finally , we investigated the contribution of M . abdita cad to pair-rule expression . In embryos treated with cad RNAi , we observe a reduction in the number of eve stripes: 2 out of 12 embryos showed three , 5/12 four , and 5/12 five eve stripes ( Fig . 7M , N ) . Similarly , D . melanogaster embryos mutant for both maternal and zygotic cad have four eve stripes [50] . The most drastic effect of cad on pair-rule gene expression is observed in E . balteatus , where embryos treated with cad RNAi exhibit the loss of all but the first stripe of eve [44] . Taken together , our evidence demonstrates that zygotic cad still plays an important role in the determination of posterior segments of M . abdita . In contrast to D . melanogaster and E . balteatus , where eliminating cad has a clearly detectable effect on gap gene expression [13 , 42 , 44] , it is largely redundant for gap gene activation in M . abdita . This implies that cad performs its pattern-forming role mainly at the level of the pair-rule genes in this species . In this study , we have investigated the establishment of maternal gradients and their role in gap gene regulation in the scuttle fly M . abdita . We compare our results with the evidence from the vinegar fly D . melanogaster as well as the marmalade hoverfly E . balteatus ( Fig . 9 ) . On the one hand , we find that important aspects of maternal regulation are highly conserved among cyclorrhaphan flies . Bcd acts as a concentration-dependent transcriptional regulator , and Cad is involved in posterior patterning in all three species . On the other hand , we find a number of interesting differences between M . abdita , E . balteatus , and D . melanogaster . The first difference concerns the regulation of cad . Even though maternal cad expression can be detected in nematocerans , and basally branching non-cyclorrhaphan brachycerans ( Fig . 1 ) , maternal expression of cad has been lost in M . abdita [47] . Zygotic expression of cad is qualitatively similar between species , but reaches further anterior in M . abdita and E . balteatus than in D . melanogaster , creating a large overlap of cad and hb in these flies . Consistent with the absence of strong repression between these two genes , hb only weakly affects cad expression in M . abdita . In contrast , cad is completely de-repressed anteriorly in bcd knock-down embryos ( see Fig . 3 ) . There is some evidence from reporter assays that Bcd may regulate cad transcriptionally in D . melanogaster as well [47] . The situation is much less ambiguous in the case of E . balteatus , where cad is strongly up-regulated in the anterior upon bcd RNAi knock-down [38] . This similarity between M . abdita and E . balteatus suggests that transcriptional repression of cad by Bcd is much more prominent in these flies compared to D . melanogaster . Whether this interaction is direct in any of the three species remains to be shown . The second difference concerns the roles of bcd and hb in axis specification and gap gene patterning . Knock-down of bcd in M . abdita and E . balteatus leads to bicaudal phenotypes , as observed in bcd/hb double mutants but not in bcd mutants in D . melanogaster [24 , 41–43] . It is important to note that the situation in M . abdita is distinct from both D . melanogaster and E . balteatus ( Fig . 9 ) . More positional information is retained in bicaudal embryos , resulting in a more anterior ( A5 ) plane of symmetry , compared to A6 in the latter two species [24 , 31 , 38] . This difference is also reflected at the level of gap gene expression . Severe M . abdita knock-down phenotypes for bcd , which lack Kr expression , show a sequence of hb-gt-kni-gt-hb domains along the antero-posterior axis ( Fig . 9 ) ( this paper and [31] ) . D . melanogaster hb/bcd double mutants only have overlapping central gt and kni domains ( Fig . 9 ) [24 , 51 , 52 , 65] . E . balteatus knock-down embryos show an almost complete de-repression of gt and kni throughout the embryo ( Fig . 9 ) [38] . The anterior gradient of Bcd is an evolutionary innovation of the cyclorrhaphan lineage ( Fig . 1 ) [30–33] . The evidence suggests that it is completely sufficient for axis specification and embryo polarity in M . abdita and E . balteatus . In contrast , both maternal Bcd and Hb contribute synergistically to axis specification and gap gene patterning in D . melanogaster . While differences in the effect of Bcd between D . melanogaster and E . balteatus are easily explained by the absence of maternal hb in the latter [38] , it is less straightforward to pinpoint the cause for polarity reversal in bcd knock-down embryos of M . abdita . Our evidence suggests that this difference lies in the ability of maternal Hb to activate Kr in D . melanogaster , but not M . abdita ( see Fig . 4 , and [48] ) . Kr expression in the anterior of the embryo is correlated with the maintenance of polarity in D . melanogaster bcd mutants , and weak bcd knock-down phenotypes in M . abdita ( Figs . 4 and 5 ) . In D . melanogaster , maternal Hb is required for Kr expression in the absence of Bcd [24 , 42] , and we have shown here that this activating interaction is indeed direct and not caused by the indirect repression of the Kni repressor ( Fig . 6 ) . It remains unclear whether activation of Kr by maternal Hb has been gained in D . melanogaster or lost in M . abdita . However , there is some evidence that favours the latter scenario . Maternal hb expression is strongly conserved across arthropods far beyond the cyclorrhaphan lineage [66–74] , and hb is involved in axis patterning in many of the species where it has been studied [67 , 68 , 70–72 , 75 , 76] . Most interestingly in our context , Hb activates Kr in the flour beetle Tribolium castaneum [75] , the honeybee Apis mellifera [72] , the hemipteran milkweed bug Oncopeltus fasciatus [76] , and the cricket Gryllus bimaculatus [69] . The fact that this activating role of hb is conserved , and is only present in the one cyclorrhaphan species that retains some activity of maternal Hb in axis formation , seems to suggest that it may represent the ancestral state , and that activation of Kr by Hb has been lost in M . abdita and E . balteatus . We have previously demonstrated that the gap gene system of M . abdita compensates for the significant differences in the distribution of maternal factors compared to D . melanogaster , such that gap gene expression converges to equivalent patterns in both species by the onset of gastrulation [48] . Such compensatory evolution is called developmental system drift or phenogenetic drift [77–81] . At the level of the gap genes , this is achieved through quantitative changes in the strength of otherwise wholly conserved gap-gap interactions [48] . In contrast , our study shows that system drift at the level of maternal-to-gap interactions is mediated by both quantitative and qualitative differences in gene regulation . While inter-species differences in the effect of Bcd and Cad mainly consist in changes in activation strength , the activating role of Hb on Kr has changed in a qualitative way: while Hb activates Kr in D . melanogaster , this activating role is absent in both M . abdita and E . balteatus ( Fig . 9 ) . In summary , we observe a trend towards replacing the role of maternal Hb with activity of the anterior maternal system—Bcd and Cad—in non-drosophilid cyclorrhaphan lineages through the process of developmental system drift . This is reflected by the stronger phenotypes of bcd and cad knock-downs in both E . balteatus and M . abdita compared to D . melanogaster . In this view , axis formation and gap gene patterning in D . melanogaster retains more ancestral characteristics than these early-branching non-drosophilid cyclorrhaphans . Further corroboration of these insights will have to come from functional studies of axis specification and gap gene patterning in an appropriate outgroup ( Fig . 1 ) : non-cyclorrhaphan brachycerans or emerging nematoceran model systems such as the chironomid midge Chironomus riparius or the moth midge Clogmia albipunctata . M . abdita fly culture , embryo collection and fixation were carried out as described in [82 , 83] . Enzymatic mRNA in situ hybridisation , image acquisition , and data processing were carried out as described in [84 , 85] . We use an embryonic staging scheme—homologous to the one already established for D . melanogaster [86]—which is described in detail in [56] . Embryo morphology and developmental timing are remarkably similar in both species . Embryos are classified into cleavages cycles C1–C14A according to nuclei number; C14A is further subdivided into eight time classes T1–8 based on nuclear and membrane morphology . Polyclonal antiserum was raised against M . abdita Hb protein expressed by means of a pET-DEST42 vector ( Invitrogen ) containing a full length cDNA insert . Purified Hb protein dissolved in 6M urea was used to raise rat antibodies by Primm Biotech ( primmbiotech . com ) using standard protocols . For antibody stains , wild-type blastoderm-stage embryos were collected after 4 hrs of egg laying and stained with a colorimetric protocol adapted from the in situ protocol published in [85] . In brief , fixed and dehydrated embryos were re-hydrated by washing 1x5min in PBT/methanol ( embryos were allowed to sink before the solution was removed ) , 2x in PBT , and 1x20 min in PBT . Embryos were washed 1x , then blocked with 2x30 min in western blocking reagent ( Roche ) in PBT followed by incubation with primary antibodies in blocking solution overnight . Unbound antibody was removed washing 3x in PBT followed by 4x15 min washes in PBT . Embryos were then re-blocked and incubated with secondary antibodies conjugated with alkaline phosphatase ( Roche ) at 1:3000 in blocking solution for 1 hr . Unbound antibody was removed as before . To prepare for staining , embryos were washed 2x5 min in AP buffer ( 100 mM NaCl , 50 mM MgCl , 100 mM Tris pH 9 . 5 , 0 . 1% tween-20 ) . Staining was carried out in the dark by the addition of AP buffer containing 0 . 1 mg/ml NBT and 0 . 05 mg/ml BCIP . Staining was stopped with 3x1 min followed by 3x10 min washes in PBT . Nuclei were counter-stained by a 10-min incubation in PBT containing 0 . 3 μM DAPI , followed by 3x washes and 3x10 min washes in PBT . Embryos were cleared through a series into 70% glycerol:PBS , of which 30 μl were mounted per slide . All washes were done on a nutator . We used RNAi knock-down protocols adapted from [31 , 37 , 87] . See [48] for further details . All expression boundaries plotted as graphs were extracted from NBT/BCIP stained embryos , except for Kr expression in M . abdita bcd RNAi-treated embryos , where boundaries were extracted from FastRed stains . Differences in expression levels in Fig . 6 and S2 Fig were assessed through simultaneous staining of wild-type and RNAi-treated embryos using NBT/BCIP to ensure a robust signal . Quantified expression data for M . abdita wild-type and RNAi knock-down embryos are available online through figshare ( http://dx . doi . org/10 . 6084/m9 . figshare . 1252195; [88] , and the SuperFly database ( http://superfly . crg . eu; [89] ) . Plots of gene expression boundaries from RNAi-treated or mutant embryos can be found in S1 File ( M . abdita ) and S2 File ( D . melanogaster ) . nos ( KP232978 ) was cloned from cDNA using data from our published early embryonic transcriptome ( http://diptex . crg . es; MAB_comp4961 ) [46] . All other genes were cloned as described in [48] . Embryo collection , fixation , RNAi treatment , and in situ hybridisation in D . melanogaster was carried out as for M . abdita [85 , 87] . D . melanogaster kni mutants correspond to deletion strain 3127 ( Bloomington Drosophila Stock Center ) with genotype Df ( 3L ) ri-79c/TM3 , Sb1 . Homozygous mutants were detected by an absence of FastRed kni staining during in situ hybridisation .
The basic head-to-tail polarity of an animal is established very early in development . In dipteran insects ( flies , midges , and mosquitoes ) , polarity is established with the help of so-called morphogen gradients . Morphogens are regulatory proteins that are distributed as a concentration gradient , often involving diffusion from a localised source . This graded distribution then leads to the concentration-dependent activation of different target genes along the embryo’s axis . We examine this process , which differs to a surprising extent between dipteran species , in the scuttle fly Megaselia abdita , and compare our results to the model organism Drosophila melanogaster . In this way , we not only gain insights into how the mechanisms that establish polarity function differently in different species , but also how the system has evolved since these two flies shared a common ancestor . Specifically , we pin down the main difference between Drosophila and Megaselia in the altered function of the maternal Hunchback morphogen gradient , which activates target genes in the former , but not the latter species , where it has been completely replaced by the Bicoid morphogen during evolution .
You are an expert at summarizing long articles. Proceed to summarize the following text: Oscillations are observed at various frequency bands in continuous-valued neural recordings like the electroencephalogram ( EEG ) and local field potential ( LFP ) in bulk brain matter , and analysis of spike-field coherence reveals that spiking of single neurons often occurs at certain phases of the global oscillation . Oscillatory modulation has been examined in relation to continuous-valued oscillatory signals , and independently from the spike train alone , but behavior or stimulus triggered firing-rate modulation , spiking sparseness , presence of slow modulation not locked to stimuli and irregular oscillations with large variability in oscillatory periods , present challenges to searching for temporal structures present in the spike train . In order to study oscillatory modulation in real data collected under a variety of experimental conditions , we describe a flexible point-process framework we call the Latent Oscillatory Spike Train ( LOST ) model to decompose the instantaneous firing rate in biologically and behaviorally relevant factors: spiking refractoriness , event-locked firing rate non-stationarity , and trial-to-trial variability accounted for by baseline offset and a stochastic oscillatory modulation . We also extend the LOST model to accommodate changes in the modulatory structure over the duration of the experiment , and thereby discover trial-to-trial variability in the spike-field coherence of a rat primary motor cortical neuron to the LFP theta rhythm . Because LOST incorporates a latent stochastic auto-regressive term , LOST is able to detect oscillations when the firing rate is low , the modulation is weak , and when the modulating oscillation has a broad spectral peak . Neural oscillations have generated considerable interest for their roles in cognition and as indicators for disease [1–14] . Electroencephalograms ( EEGs ) and local field potential ( LFPs ) recordings have revealed transient oscillations in many cortical and subcortical structures , to which neurons both near and far from the LFP recording site show phase preferences in spiking . Groups of neurons that are locked to a common oscillation , and are therefore active in tightly confined temporal windows , may define a cell assembly whose synchronous spiking could select and activate afferent structures [15–17] . Such transient cell assemblies may form and disband as objects are attended to in the visual scene [3 , 6–8 , 11] , as preparation for movements are made [2 , 13] , or during choice points for reward [9] . Prominent slow oscillations are observed as patients undergo anesthesia [10] , and exhibit changes in dynamics during transitions in unconscious brain state [14] . Furthermore , strong oscillatory signals are prominent in neurological disorders such as Parkinson’s disease , and can be used to characterize pathophysiology and its relation to behavior [12 , 18 , 19] . The oscillatory structure of individual neurons can thus provide insight into the dynamics of cognitive processing and motor planning , and their implementation in health and disease . However , identifying oscillations in neural spike trains , particularly within individual experimental trials , can be challenging because they occur in conjunction with both non-oscillatory fluctuations in neural firing rate and extraneous noise . To tease apart multiple factors that affect spiking activity , statisticians have suggested the use of point process models together with modern regression methods associated with generalized linear model ( GLM ) technology [20 , 21] . In this paper we develop a method that can find oscillations in spike trains even when the signal is comparatively weak , and can track changes in oscillatory behavior across trials . The starting point for our approach is the observation by Smith and Brown [22] that , for many purposes , evolving neural firing rates can be described using state-space models imbedded in point processes . We modify the models used by Smith and Brown , replacing their first-order autoregressive processes with higher-order processes [23] that can fit oscillations found in spiking neurons . This requires careful attention to the form of the higher-order autoregressive process . We take advantage of a Bayesian time-series decomposition introduced by Huerta and West [24] , using prior probability distributions to constrain the fit so that it has appropriate power spectral content . We also use a Gibbs sampling method developed recently in a different context [25] to compute posterior distributions efficiently . Our approach is superficially related to that of Allcroft et al . [26] , who used autoregressive moving average models to estimate spectral content from censored data , but their method does not accommodate easily the special situation we face with spiking neurons , including history effects that can account for hard and soft refractory periods . Point process models employing the GLM framework [21 , 27] have been employed to explain the spiking behavior by taking into account spiking refractoriness , behavioral and stimulus-induced non-stationarities , and spiking of neighboring neurons [20] . A series of investigations [12 , 18 , 19] have added long-term history effects to capture oscillation in the history dependence and have also used a state-space smoothing algorithm to track changes in the oscillatory dynamics over time . Eden et al used a long history to capture both , but as we shall see , LOST can better capture the irregularities present in realistic biological oscillations . Our approach is based on what we call the Latent Oscillatory Spike Train ( LOST ) model , which not only includes terms to describe the spiking refractoriness , event ( behavior or stimulus ) -locked effects , and trial-to-trial variability in firing rate , but also explicitly models the dynamics of the modulating oscillation itself . However , the latent state cannot capture the discontinuous change in the spiking probability that occurs after every spike , which the aforementioned GLM models do well in characterizing , so we utilize a short-term spiking history to capture the refractory period , while also introducing the latent state to model the oscillatory dynamics . Oscillatory structure is often considerably degraded when it is observed as a spike train . On the one hand , if the firing rate is low , or the oscillatory modulation is weak , noise associated with erratic spiking will dominate . On the other hand , even if the modulation is strong and the firing rate is high , the oscillatory signal may itself be unsteady , with substantial variation in period from cycle to cycle . LOST accommodates both spiking noise and oscillatory variation by imbedding into the point process intensity function a latent auto-regressive process , and then imposing a suitable soft constraint ( in the form of a prior probability distribution ) on the oscillatory dynamics . We provide details of the model , along with the posterior sampling scheme and a discussion on how to interpret the posterior samples , and study its effectiveness with leaky integrate-and-fire neural simulations . We then demonstrate the ability of the LOST framework to find interesting trial-to-trial variation in motor cortical neuron during a lever-pulling task . Gibbs sampling [28] and data augmentation [29 , 30] are used to infer the model parameters Θ = [F1:p , σ2 , α , μ , v] and the oscillation x . The spike history and TAE are functions of time relative to the last spike of an event or behavior , respectively , and they are estimated by parameterizing a subset of continuous functions with splines . The estimation requires us to choose a set of basis splines , which we choose heuristically , using some prior belief about their general shape to choose the knot locations . In the current work , the number and locations of the knots are not found with the Gibbs sampling procedure , so we briefly describe how they are determined before we describe the Gibbs sampling procedure . We jointly sample model parameters and latent states from the joint posterior distribution , Eq ( 4 ) , using Gibbs sampling with data augmentation . The conditional posterior distribution can be read off from the full joint posterior distribution by considering all parameters fixed , except the parameter whose conditional posterior we are interested in . Our problem deviates from the standard Gibbs sampling by the missing data x in Eq ( 4 ) , and we utilize the data augmentation strategy [29 , 30] , a scheme of simplifying analysis by augmenting the observed data with missing values , to generate samples of X from the predictive distribution p ( X|y , Θ ) in between sampling parameters from the standard conditional posteriors . Even with samples of X in hand , the posterior distribution does not yield conditional posteriors that can be sampled easily . The recently developed Pólya-Gamma data augmentation scheme [25] allows sampling from simple Gaussian conditional posteriors at the cost of introducing the M × N matrix of Pólya-Gamma variables ω through the following identity for the spike probability: p ( y m n | Θ , H n , l , x m n ) = ( e x m n + μ m + λ l ( m , n ) R + f n ) y m n 1 + e x m n + μ m + λ l ( m , n ) R + f n = 1 2 e κ m n ( x m n + f n + μ m + λ l ( m , n ) R ) × ∫ 0 ∞ e - ω m n ( x m n + f n + μ m + λ l ( m , n ) R ) 2 2 P G ( ω m n | b = 1 , z = 0 ) d ω m n ∝ ∫ 0 ∞ N ( x m n + f n + μ m + λ l ( m , n ) R | κ m n ω m n , 1 ω m n ) P G ( ω m n | b = 1 , z = 0 ) d ω m n ( 8 ) where PG ( ωmn|b = 1 , z = 0 ) is the Pólya-Gamma distribution with parameters b = 1 and z = 0 , and κ ≡ y - 1 2 . With the introduction of the Pólya-Gamma variables , the joint posterior Eq ( 4 ) becomes p ( Θ | y ) ∝ p ( Θ ) ∫ X [ ∏ m = 1 M ∏ n ′ = 1 N p ( X m n ′ | X m , n ′ − 1 , Θ ) p ( X m 0 | Θ ) × ∏ n = 0 N ∫ 0 ∞ N ( x m n + f n + μ m + λ l ( m , n ) R | κ m n ω m n , 1 ω m n ) P G ( ω m n | b = 1 , z = 0 ) d ω m n ] d X , ( 9 ) from which we derive the conditional posteriors for the parameters and the predictive distributions for the augmented variables . We refer to the set of augmented variables as V+ = {X , ω} , and denote by Θ\x to be the set of all parameters except parameter x . Gibbs samplding was implemented in the Python programming language using the Numpy , SciPy , Matplotlib , StatsModels , and Patsy toolboxes . Numerical routines were written in C/C++ and Cython . Software for the Gibbs sampler and a Python wrapper for the Pólya-Gamma routine based on the original code by Jesse Windle , available at https://github . com/jwindle , is available at https://github . com/AraiKensuke/PP-AR and https://github . com/AraiKensuke/pyPG , respectively . The stochastic oscillation for the mth trial wm is generated by first generating a stochastic phase tm and stochastic amplitude Am as t m , n + 1 = t m n + Δ t ( 1 + ξ m n C ξ ) w m n = 1 + A m n C A sin ( 2 π ν t m n ) . ( 44 ) where ξm and Am are generated by AR ( 1 ) processes , and tm0 ∈ [0 , 1] a uniform random number . These are then used to generate the irregular oscillation Cξ and CA control the size of deviation from uniformity , while the timescale of the AR ( 1 ) processes controls the timescale of variation of amplitude and phase in the oscillation . AR ( 1 ) s with timescales on the order of the oscillation period T = 1/ν produce fluctuations that causes considerable variability of period and amplitude in w , which we quantify with the oscillator irregularity , the oscillatory coefficient of variation ( OCV ) of the instantaneous periods T of oscillation , defined as the ratio of the standard deviation to the mean of time intervals between phase 0 crossings of an oscillatory signal . Fig 1 shows 2 example oscillations generated by this procedure , their OCVs and their power spectral densities . Irregular oscillations have a higher OCV and a less peaked spectral density . To generate spikes , we employ an inhomogeneous renewal process or the LIF model , driven by a stochastic model of oscillation to modulate the firing of spikes . For the inhomogeneous renewal process , for each trial m and time n , if for a uniform random number rmn ∈ [0 , 1] , r m n < Δ t ( e μ m + w m n + λ l ( m , n ) G ) , we generated a spike , with λ l ( m , n ) G the user-defined ground truth refractory history function . For the LIF model , spikes were generated using Δ V m n = ( - V m n τ + f n + μ m + w m n + χ m n ) Δ t , ( 45 ) where the Gaussian random variable χ m n ∼ N ( 0 , σ b 2 ) represents irregular arrival times of afferent spikes , τ = 0 . 2 the membrane time constant , μm a baseline DC current and σ b 2 = 210 the amplitude of background fluctuation . Spikes are elicited when Vmn ≥ 1 passes the threshold value of 1 , which then causes a reset to Vm , n+1 = 0 . LIF have a refractory period that depends on the model parameters , and therefore the CIF naturally is dependent on the last spike time . We analyze neurons from M1 of rat performing a self-paced lever push-hold-pull task [13 , 41] that have been found to be significantly modulated to the theta rhythm in the LFP . These 2 neurons were recorded on separate electrodes , with “neuron 1” in Figs 10 and 11 recorded and spike sorted from a tetrode on a siliconprobe , and “neuron 2” recorded using juxtacellular ( cell-attached ) recording , where the spike trains did not need to be sorted . Both neurons were located in layer 1/2 , and “neuron 2” identified morphologically as an interneuron , while “neuron 1” is likely to be an interneuron based on spike shape . We identified trials by selecting lever hold periods lasting more than 1 second followed by a large-amplitude pull , and analyzed the 1 . 2 second period encompassing the hold-pull period . We have defined the LOST model , together with accompanying posterior simulation technology , in order to detect the presence of oscillatory firing-rate modulation in a spike train , and infer its phase of oscillation in the presence of a variety of non-stationarities in firing rate that are present in experimental data . Previous methods have assessed the oscillatory content in spike trains by comparing the spiking to a known oscillatory signal like a band-passed LFP [42 , 43] , by detecting oscillation directly from the spike train [12 , 18 , 44–46] , or by point process regression using the oscillatory signal as a covariate [47] . The LOST model not only detects oscillatory modulation from the spike train itself , but does so in the presence of both spiking noise and oscillatory irregularity , and also allows extensions to inferring additional latent structure , such as non-stationarities in the modulational strength across trials . In addition , LOST is able to separately account for modulational signals of different frequency band , which has proven to be vital in the analysis of real spike trains . Structural priors on the latent state dynamics together with explicit consideration of spiking noise and oscillatory irregularity allows LOST to uncover oscillatory structure even when the firing rate is low , the modulation weak or when the oscillation is irregular , compared to methods that directly regressed the spiking probability on the raw spiking history itself without an intermediary latent state [12 , 18 , 19] . LOST uses the intermediary latent state to addresses the irregularity characteristic of neural oscillations . Another approach to modeling the spectral features of time series using Gaussian processes has recently been developed by Wilson et al [48] , where different classes of covariance kernels , the SE and SM , respectively , model non-oscillatory and oscillatory structures , analogous to the real and imaginary roots of the characteristic polynomials . It would be interesting to compare the two approaches in future work . The increased sensitivity and flexibility in specifying latent structures , should allow LOST to be used in investigating the role of oscillations in cognitive functions in the cortex . Investigators are increasingly interested in characterizing the change in neural responses to time-varying stimulus or behavior [49 , 50] . Theta and gamma oscillations in hippocampus change their coupling structure and prevalence during learning and memory acquisition [51 , 52] . The inclusion of trial-specific structure independently of the LFP in the LOST model may also allow detection of increased recruitment of a given neuron into cell assemblies during periods when oscillations in the LFP are changing . Further , the variability of the timing and presence of oscillations in the LFP seen in many areas of the cortex and hippocampus [2 , 13 , 53] , suggests oscillatory modulation in single neurons may likewise exhibit finer structure on a per-trial basis . Use of the LOST model in the analysis of such systems may reveal richer dynamics of recruitment into cell assemblies , and a better understanding of the role of single neurons in cognition .
Oscillatory modulation of neural activity in the brain is widely observed under conditions associated with a variety of cognitive tasks and mental states . Within individual neurons , oscillations may be uncovered in the moment-to-moment variation in neural firing rate . This , however , is often challenging because many factors may affect fluctuations in neural firing rate and , in addition , neurons fire irregular sets of action potentials , or spike trains , due to an unknown combination of meaningful signals and extraneous noise . We have devised a statistical Latent Oscillatory Spike Train ( LOST ) model with accompanying model-fitting technology , that is able to detect subtle oscillations in spike trains by taking into account both spiking noise and temporal variation in the oscillation itself . The method couples two techniques developed for other purposes in the literature on Bayesian analysis . Using data simulated from theoretical neurons and real data recorded from cortical motor neurons , we demonstrate the method’s ability to track changes in the modulatory structure of the oscillation across experimental trials .
You are an expert at summarizing long articles. Proceed to summarize the following text: Weekly iron-folic acid ( IFA ) supplementation and regular deworming is effective for the prevention of iron deficiency and anaemia in women of child-bearing age . Between 2006 and 2013 , a program of weekly IFA and biannual deworming was implemented in Yen Bai province , Vietnam . In this study we aimed to determine the effectiveness of the program in reducing anaemia and the prevalence of hookworm infection after 72 months ( six years ) . This prospective cohort study followed up a cohort of 389 women of child-bearing age from baseline until six years after the introduction of the weekly IFA ( one tablet containing 200 mg ferrous sulphate , 0 . 4mg folic acid ) and deworming ( one 400mg tablet of albendazole given twice yearly ) program ( May 2006 to 2012 ) . In each of the six surveys ( baseline and five follow-up surveys ) we measured haemoglobin and ferritin , and the burden of soil transmitted helminth ( STH ) infections , and in the 72 month survey we also administered a questionnaire to assess adherence and possible impediments to participating in the program . Two hundred and fifty six ( 65 . 8% ) of the original 389 women enrolled in the cohort attended the final 72 month survey . Haemoglobin levels were 122 g/L [95% C . I . 120 , 124] at baseline and increased to 135g/L [95% C . I . 133 , 138] after 72 months . The prevalence of anaemia was 37 . 8% [95% C . I . 31 . 0 , 44 . 7] at baseline and reduced to 14 . 3% [95% C . I . 9 . 5 , 19 . 1] . Hookworm infection prevalence , 75 . 9% [95% C . I . 68 . 1 , 83 . 8] at baseline , reduced to 10 . 2% [95% C . I . 5 . 4 , 15 . 0] with no moderate or heavy intensity infections . Seventy-two percent of participants reported still taking at least 75% of the weekly supplements , and 85 . 0% had taken the most recent deworming treatment . Anaemia rates fell significantly during the six-year program , and STH infections were eliminated as a public health risk . Adherence was well maintained but long-term sustainability is challenging in the absence of ongoing external support . Anaemia ( haemoglobin < 120g/L ) is estimated to affect 29 . 0% of non-pregnant women world-wide . [1] Iron deficiency anaemia ( IDA ) is the most common form of anaemia in many resource-poor settings . Nutritional anaemia can also be caused by vitamin deficiencies ( A , B2 , B6 , folate , B12 , C , E ) and deficiencies of other minerals—such as copper , zinc , and in some cases selenium; and severe protein-energy malnutrition . [2] Intestinal infections causing diarrhoea , malabsorption and blood loss ( especially hookworm infection ) may cause depleted body iron stores and worsen the risk of other micronutrient deficiencies by interfering with digestion and absorption ( e . g . , vitamin A ) . [3] The consequences of IDA are most evident in women of child-bearing age and children . In pregnancy IDA has been linked to premature delivery , higher maternal morbidity , and infants with low birth weight ( LBW ) , lower iron stores and higher anaemia rates . [4 , 5] Iron deficiency anaemia in infants and young children may lead to impaired development [4] with long term health implications . [6 , 7] Weekly iron-folic acid ( IFA ) supplementation is now recommended by WHO for nonpregnant women of child-bearing age living in areas with a prevalence of anaemia above 20% [8 , 9] , with the aim of improving a women’s iron and folate status prior to conception . The rationale for using intermittent rather than daily supplemental iron relates to the adverse impact of high levels of luminal iron in the gut and the concept that taking iron less frequently may facilitate absorption of iron by mucosal cells and lead to fewer side-effects [10–12] . WHO also recommends that preventive chemotherapy for soil transmitted helminths ( STH ) be considered in this group in endemic areas where the prevalence is 20% or higher . [13] However , it is not clear that compliance and effectiveness will be maintained over many years . Long-term sustainability is also of concern , especially when program implementation is dependent on external donor funding . Although the cost of weekly IFA supplementation per woman is estimated to be as little as USD 0 . 76 cents per year , this still equates to $76 , 000 . 00 per 100 , 000 women per year , a cost few local health administrations can afford . [14] Between 2006 and 2013 , we initiated and supported a program of weekly IFA supplementation , with regular deworming , to a target population of approximately 250 , 000 women of child-bearing age in Yen Bai Province , Vietnam . [15–17] Our objectives in this study were to document the adherence and effectiveness of weekly IFA supplementation and deworming on rates of hookworm infection , anaemia , and iron deficiency in a cohort of participants followed from baseline to 72 months ( 6 years ) post intervention . We also sought to identify administrative challenges and barriers to ongoing sustainability . The intervention has been described previously . [18] Briefly , a demonstration project of weekly IFA ( one 200mg tablet of Ferrous Sulphate—equivalent to 60mg elemental iron , 0 . 4mg folic acid ) supplementation combined with four monthly deworming ( Albendazole 400mg ) for non-pregnant women of child bearing age was initiated in May 2006 in two districts in Yen Bai province over a period of 12 months . Tran Yen and Yen Binh districts were selected for the intervention , being easy to reach , and with both Kinh and ethnic minority populations . All nonpregnant women of reproductive age between 16 and 45 years were eligible for the intervention . Supplements were supplied free of charge . Village health workers ( VHWs ) were central to the delivery of the intervention to women in their communities , and worked closely with the Women’s Union to mobilize the target population . A baseline survey was undertaken in November 2005 in a randomly selected cohort of women who were then followed up at 3 and 12 months post introduction of the intervention . [19] Sample selection for the cohort used a stratified multi-stage cluster design , in which 'probability proportional to size' random sampling was used to select primary sampling units ( villages ) within each district , and secondary sampling units ( women ) were selected randomly from each village using provincial lists . A sample size of at least 280 was required in the baseline and follow-up surveys to detect an increase in haemoglobin of 5 g/L with a power of 0 . 9 , a type 1 error of . 05 , and accounting for clustering with an intraclass correlation of 0 . 2 . This number was also sufficient to detect a reduction in hookworm prevalence of 30% . There was no control group as it was considered unethical to actively withhold IFA supplementation and deworming treatment from this group over several years . After 12 months of the demonstration project , improvements were seen in all measured indices of women’s health: haemoglobin , serum ferritin , anaemia ( Hb < 120 g/L ) , iron deficiency ( serum ferritin < 15 mcg/L ) and STH prevalence and intensity of infection . [15] As a result , the provincial authorities supported the expansion of the intervention to target reproductive-aged women in all districts of the province ( some 250 , 000 women ) . The community-based program was administered from the Yen Bai Centre for Malaria Control , through the District Centres for Preventive Medicine to Commune Health Stations and VHWs . The expanded program consisted of a weekly IFA tablet ( 200mg Ferrous Sulfate ) and one tablet of Albendazole ( 400mg ) given twice a year . Eligible women were encouraged to collect their supplements monthly from their VHW , who recorded the woman's name and date of distribution and advised about side-effects and safe storage of supplements . Albendazole was given as observed treatment on locally designated days either at the commune health station or supervised in the village by a commune health worker . National oversight of the program and support for training and educational material development and production was provided by the National Institute of Malariology , Parasitology and Entomology , which also has responsibility for the national hookworm control program . The Provincial Health Department provided salary support for distribution through the health system . WHO donated albendazole tablets but external donor funding was required for the IFA supplements , training and training materials and educational and promotional materials . Thus , the program was not fully supported by the national health system , remaining partly dependent on external financial and administrative support . The same cohort of women who participated in the baseline , three and 12 month surveys during the demonstration project ( as described above ) were invited to participate in the 30 month , 54 month , and 72 month follow-up surveys if they were still resident in the same villages . In all surveys participants were asked to provide a venous blood sample and a faecal sample . Haemoglobin was measured by HemoCue 201+ ( HemoCue AB , Angelholm , Sweden ) and the STH burden was determined using the Kato-Katz technique as described by Ash et . al . [20] Classification of helminth infection intensity ( eggs per gram , EPG ) was according to WHO cut offs: 1 ) Hookworm infections: light = 1–1999 , moderate = 2000–3999 , heavy ≥ 4000 2 ) Ascaris lumbricoides: light = 1–4999 , moderate = 5000–49999 , heavy ≥ 50000 , and 3 ) Trichuris trichiura light = 1–999 , moderate = 1000–9999 , heavy ≥ 10000 EPG . For previous surveys , serum ferritin was measured using a sandwich immunoenzymatic assay ( IEA; Beckman Coulter Access Reagents , Fullerton , CA ) . Due to a change in technology at the original laboratory and a subsequent change in the laboratory used , serum ferritin for the 72 month survey was analysed using a Cobas immunoturbidometric test kit ( Roche Diagnostics GmbH , D-68298 Mannheim ) . The structured interview administered during the 72 month survey sought information about whether women had taken the last deworming treatment and how regularly they had taken IFA supplements during the previous 10 weeks , and reasons for noncompliance . It also asked about other factors that may influence adherence such as access to , or provision of , supplements and direct or indirect costs related to travel or lost work time . Adherence with the supplementation regime was defined as taking at least 75% of the supplements provided by the VHW . Data of surveys collected before year 6 have been analysed and reported previously . [21] By pooling all available survey data we extended the former semi-cross-sectional , semi longitudinal panel design with data collected as part of the final survey . Data analysis was conducted using Stata/IC 11 . 2 ( College Station , TX ) . Cross-sectional summary data was obtained via linear ( haemoglobin , log transformed ferritin ) and logistic ( prevalence ) regression models that were fitted per survey time point , incorporating clustering at village level ( Huber-White Sandwich estimator ) to obtain robust standard errors . Ferritin data was right-skewed and therefore log transformed to normalise the data . Arithmetic mean haemoglobin , geometric mean ferritin , and prevalence ( anaemia , iron deficiency , IDA , hookworm , ascaris , trichuris , and total STH infection ) with 95% confidence intervals are presented . At each survey time point and for every outcome , the analysis sample consisted of the available outcome data of all eligible women . Data from women who became pregnant during the study were not excluded from the analysis sample . Longitudinal repeated measures mixed linear and logistic regression was used to investigate the change from baseline to year 6 , while accounting for clustering at individual and village level . Adherence data on weekly IFA supplementation and deworming treatment was summarized descriptively by survey as percentages with 95% confidence intervals . Differences in changes over time in anaemia prevalence were examined by ethnic group within district . The odds ratio of being iron deficient at each post-implementation time point compared to baseline was explored . At 72 months , prevalence of iron deficiency was compared between subgroups formed by district , ethnic group , and gender . Changes in prevalence over time by severity of hookworm , ascaris , and trichuris infection are presented graphically . Extensive consultation was undertaken between the project team , communities and community leaders , as well as liaison with village , district and provincial health staff . Village health workers provided participants with information regarding the surveys and written informed consent was documented at the time of enrolment in the surveys . The National Institute of Malariology , Parasitology and Entomology and the Walter and Eliza Hall Institute of Medical Research and Melbourne Health approved of these consent procedures , which were standard NIMPE consent procedures . Potential recruits received a printed plain language statement and oral consent and documented signatures were obtained prior to participation . Participants were informed that they could withdraw from the study at any time and that withdrawal would not affect their routine medical treatment . Participants were all adult women between the ages of sixteen and forty-five; there were no minors included . The project was approved by the Human Research Ethics Committees of the National Institute of Malariology , Parasitology and Entomology ( Hanoi , Vietnam ) , and the Walter and Eliza Hall Institute of Medical Research and Melbourne Health ( Melbourne , Australia ) and all ethics committees specifically approved the use of participants of reproductive age between sixteen and forty-five years of age . The timeline and participation rates for this and previous surveys are shown in Fig 1 . Of the 389 women originally surveyed in 2005 , 256 returned for the survey in 2012 . Two hundred and fifty two women provided blood samples , 216 provided stool samples and six women left before completing the interview . One hundred and seventy eight women ( 72 . 0% [95% C . I . 63 . 6% , 80 . 4%] ) were still taking at least 75% of the supplements they received . A further 29 women received the supplements but either only took them sometimes or gave them to someone else . The forty-three remaining women did not receive the supplements . These latter women were concentrated in six communes in Tran Yen district . Deworming treatment was received by 85 . 0% ( 95% C . I . 79 . 5 , 90 . 5 ) of women ( 212/249 ) during the last campaign but two of them did not take it . The adherence over time with taking IFA supplements and deworming treatment is shown in Table 1 . The change in mean haemoglobin and ferritin , and the prevalence of anaemia , iron deficiency anaemia and moderate/heavy STH infection over the 72 month period of program implementation are shown in Table 2 . By 2012 the overall mean haemoglobin level was 135g/L [95% C . I . 133 , 138] . Based on the mixed-effects model this represents an increase from baseline of 13g/L [95% C . I . 11 , 15] . The prevalence of anaemia reduced from 38% [95% C . I . 31 , 45] to 14% [95%C . I . 9 , 19] . Iron deficiency anaemia reduced from 14% [95%C . I . 10 , 19] to 3 . 9% [95% C . I . 2 , 6] . The relative change in anaemia over time by district and ethnic group of Kinh or non-Kinh is shown in Fig 2 . The anaemia prevalence dropped significantly from previous levels in all population groups , in the first 12 months of the intervention . In the following years anaemia prevalence continued to decrease significantly , in both the Kinh and non-Kinh ethnic groups , in Yen Binh district . However in Tran Yen district it remained static in the following 5 years , between 11 and 17% for the Kinh , and between 21 and 29% for the non-Kinh . The prevalence of iron deficiency in the 72 month survey was 43/252 ( 17% , [95%C . I . 10 , 24] ) . This was an increase on previous levels and corresponded to falling adherence rate ( Fig 3 ) . There was a higher prevalence of iron deficiency in Tran Yen , 30/140 ( 21% , [95% C . I . 15 , 28] ) compared to Yen Binh , 13/112 ( 12% , [95%C . I . 6 , 18] ) . Iron supplements were reportedly taken by 25/29 ( 86% , [95%C . I . 73 , 100] ) of iron deficient women in Tran Yen , and 9/13 ( 69% , [95% C . I . 40 , 98] ) in Yen Binh district . Iron deficiency was more prevalent among women from ethnic minority groups ( non-Kinh ) 23/95 ( 24% , [95% C . I . 16 , 33] ) than Kinh women , 20/157 ( 13% , [95%C . I . 7 , 18] ) . The main reason for not taking supplements was unavailability , but some women also reported not needing them because they felt well . The overall prevalence of STH infection fell from 83 . 7% [95% C . I . 77 . 2 , 90 . 2] to 13 . 9% [95% C . I . 8 . 7 , 19 . 1] , and hookworm infection from 75 . 9% [95% C . I . 68 . 1 , 83 . 8] to 10 . 2% [95% C . I . 5 . 4 , 15 . 0] , while moderate and heavy infections were essentially eliminated ( Fig 4 ) . We report the effectiveness of a community-wide weekly IFA supplementation and regular deworming program in a population of non-pregnant rural Vietnamese women after 72 months . It is one of the few studies to evaluate an intermittent iron supplementation and deworming program for nonpregnant women over a period of many years and offers unique insights into the effectiveness and sustainability of the WHO-recommended approach to prevention of iron-deficiency anaemia in this population . Haemoglobin levels remained well above the baseline mean and the prevalence of anaemia continued to fall in most sections of the population . Moderate and heavy intensity STH infections were virtually eliminated and only 10% of women still had light infections ( mostly hookworm ) . In 2011 , WHO recommended weekly IFA supplementation for non-pregnant women of reproductive age in populations with an anaemia prevalence of 20% or higher , given in three month cycles with a three month gap between each cycle . [8] In the Yen Bai setting , it took 12 months of weekly supplementation for the population prevalence of anaemia to drop below 20% and up to 6 years ( 72 months ) for the prevalence to be significantly below the 20% level . This slow rate of decline in anaemia levels has also been noted in other studies [22 , 23] and suggests greater benefits if weekly or biweekly supplementation is given continuously , at least in the first year , and continued for several years . The increased risk of iron deficiency in women of child bearing age is often compounded by hookworm infection in endemic areas . WHO has previously recommended that this group be included in preventive chemotherapy programs for STH ( except during the first trimester of pregnancy ) , and suggest achieving synergy by packaging this intervention with other interventions . [13] These guidelines are currently being updated and will provide more detail to assist program managers considering this intervention . [24] We observed reduced but still reasonable adherence with weekly IFA after 54 and 72 months ( 76% and 72% respectively ) , suggesting that the program remained popular with the target population . However , despite the relatively high reported adherence rates in Tran Yen district we noted a rise in the prevalence of iron deficiency since the 54 month survey . We found that the supply of IFA supplements had been interrupted in certain communes in Tran Yen district during this time , which may explain the rise in iron deficiency in this district . The failure to achieve a consistent supply of supplements in these communes may be due to inadequate training of new health staff over the 6 years of implementation as there was considerable turnover of commune staff and VHWs ( GC personal communication ) . It is important to note that STH infections of moderate and heavy intensity for any STH species remained at or less than 1% . As these heavier worm burdens infections are main cause of morbidity , we conclude that the deworming intervention virtually eliminated all STH-related morbidity . Limitations of the study included a reduced participation rate in the later surveys , in spite of the efforts of local village and commune health workers publicising the surveys . This was most likely due to the long follow-up period , and the movement of some families out of the area . The relatively high loss to follow-up may have biased our estimates of adherence and effectiveness , as non-adherent women may have chosen not to take part , while healthier adherent women may have remained engaged . As well , women who were feeling tired , or had illness , and those with poorer economic circumstances may have been less likely to adhere and/or to attend for surveys . This would have resulted in a healthier cohort at the end of the program , making the intervention appear more effective . Other limitations were that adherence data relied on self-reporting rather than objective documentation , and women who attended the survey may have exaggerated their adherence . Ferritin levels were measured using different methodology in this , compared to previous surveys . However both were commercial assays and we have no reason to believe that this change contributed to the lower mean ferritin observed in this survey . We did not include a control group as the research team and provincial authorities felt this would be unethical for a long term program . However , we are unaware of significant improvements in economic conditions during the six year period that may have accounted for the results presented here . Indeed , the global financial crisis commenced soon after program expansion and so deterioration in community living standards may have been expected . Improvements in main road infrastructure did occur but we did not observe a change in living conditions at village level ( Casey , personal communication ) . The project was conducted in a remote rural region of Vietnam and may not be generalizable to other areas or ethnic groups where the prevalence and causes of anaemia may differ . The sustainability of weekly IFA/deworming programs for the large populations for whom they are recommended is country-specific . The program in Yen Bai province was mainly externally funded , and so was never fully incorporated as a national or provincially-funded program . In post program debriefings , provincial health and finance officials emphasised that , while the program was well accepted by the population and effective and cheap on a per person basis ( 0 . 76USD/woman/year ) , the cost of supplying weekly supplements to the target population ( approx . $200 , 000 per annum ) was beyond the capacity of the province’s health budget . While they were prepared to cover the human resource distribution costs , they were not able to support purchase of IFA supplements , development and production of educational materials , and training ( G Casey , T Tinh , personal communication ) . Multiple micronutrient supplementation is even more expensive , even though it may be indicated in settings with higher rates of micronutrient deficiencies . [25] There are however encouraging signs for the long-term sustainability of community-based WIFS/deworming programs in some other countries , especially India . [17 , 26] Based on sound evidence from the field , [23 , 27] the Indian Government has produced an operational framework for universal weekly IFA supplementation for adolescents in school and adolescent females not attending school . [28] Responsibility for the national program , from policy formulation and resource allocation to monitoring and review , has been allocated to the Ministry of Health and Family Welfare [29] . Given that this program is projected to cover 130 million adolescents , it may encourage more countries with at-risk populations to provide resources for similar national programs . Likewise , in Cambodia , the use of weekly iron and folic acid supplementation has been progressively extended , over a 10 year period , to cover most schools ( LTC-S personal communication ) . A revolving fund approach can help sustain the program , by selling the supplements . This approach was successfully used in the weekly IFA supplementation programme of Hai Duong province , Viet Nam , where the supplements were sold to non-pregnant women through the Women’s Union network and provided free of charge when women were pregnant , according to the Vietnamese health policy [30] . Over a year , a non-pregnant woman would spend the equivalent of US$0 . 96 , which was acceptable for rural women in Vietnam . Funds gained from the sales of the supplements were used to pay for an incentive for Women’s Union collaborators to sell the supplement ( 20% ) , and for management costs and regular communication and promotion activities in the communes ( 30% ) . The remaining 50% was held in a local bank under the supervision of a district steering committee , and used to purchase new supplements , to continue the programme beyond its initial financing period . [30] A review of weekly IFA supplementation programs conducted in Cambodia , The Philippines and Viet Nam concluded that women are willing and able to purchase supplements when they are widely available and affordable , including in poor rural areas and schools . [31] In the Cambodian factories , where supplements had to be provided free of charge because local laws forbade their sale , WRA asked that the supplements be sold outside the factories so that they could continue taking them in the future . In each country , the programme’s success led to expanding weekly iron-folic acid supplementation through larger-scale programmes . The variety of social marketing and community mobilization strategies used in the three countries in schools , factories , and communities ( discussed in the above mentioned programme review ) provide valuable lessons for replicating this approach in other countries . [31] An analysis of 10 weekly IFA programmes for the prevention and control of anaemia in women , which took place in 6 countries , confirmed that high compliance in taking the supplements can be achieved , irrespective of supervision , provided recipients are convinced of the benefits through an effective communication strategy , with the participation of several stakeholders , and a system in place for monitoring consumption . [17] In conclusion , the program of free weekly IFA supplementation and regular deworming for women of reproductive age in Yen Bai province ran successfully for 6 years with external inputs of supplements , training and education . It was well received by the population , with good adherence , and resulted in major reductions in anaemia and STH infection . Sustainability will probably require full integration into Vietnam’s national health system . A complementary approach to be considered , successfully used both elsewhere in Viet Nam and in other countries , is to sell the supplements at an affordable price while promoting them through social marketing , thus creating and maintaining demand for the product .
Weekly iron-folic acid ( IFA ) supplementation combined with regular deworming for women of child bearing age is effective in the prevention of iron deficiency and anaemia . Following a baseline survey , a weekly IFA and regular deworming project was implemented in Yen Bai province , Vietnam in 2006 , and after 12 months expanded to the entire province . Haematological parameters , soil transmitted helminth ( STH ) burden and adherence to the program were monitored periodically until 2012 . We found anaemia prevalence fell from 37 . 8% to 14 . 3% during the six-year period , and haemoglobin levels increased from 122 g/L to 135g/L . STH infections were essentially eliminated as a public health risk . Seventy-two percent of participants continued to take at least 75% of the weekly supplements , and 85 . 0% took the most recent deworming treatment . These results show that prevention of anaemia in women of child-bearing age with weekly IFA and regular deworming is feasible and effective over a prolonged period . However , long-term sustainability may be a major challenge in some settings in the absence of ongoing external support .
You are an expert at summarizing long articles. Proceed to summarize the following text: Cryptococcus gattii causes life-threatening disease in otherwise healthy hosts and to a lesser extent in immunocompromised hosts . The highest incidence for this disease is on Vancouver Island , Canada , where an outbreak is expanding into neighboring regions including mainland British Columbia and the United States . This outbreak is caused predominantly by C . gattii molecular type VGII , specifically VGIIa/major . In addition , a novel genotype , VGIIc , has emerged in Oregon and is now a major source of illness in the region . Through molecular epidemiology and population analysis of MLST and VNTR markers , we show that the VGIIc group is clonal and hypothesize it arose recently . The VGIIa/IIc outbreak lineages are sexually fertile and studies support ongoing recombination in the global VGII population . This illustrates two hallmarks of emerging outbreaks: high clonality and the emergence of novel genotypes via recombination . In macrophage and murine infections , the novel VGIIc genotype and VGIIa/major isolates from the United States are highly virulent compared to similar non-outbreak VGIIa/major-related isolates . Combined MLST-VNTR analysis distinguishes clonal expansion of the VGIIa/major outbreak genotype from related but distinguishable less-virulent genotypes isolated from other geographic regions . Our evidence documents emerging hypervirulent genotypes in the United States that may expand further and provides insight into the possible molecular and geographic origins of the outbreak . Newly emerging and reemerging diseases have become a major focus of infectious disease research in the 21st century . Reemerging diseases are classified as those that have been previously documented , but are now rapidly increasing in incidence , geographic range , or both [1] . Emerging disease events have been occurring at higher than average rates in the United States due to several factors such as wildlife diversity , environmental change , international travel , and increases in host susceptibility [2] , [3] . An additional factor contributing to increases in morbidity and mortality for many infectious diseases involves genetic recombination events or gene/pathogenicity island acquisitions . These events can occur via either horizontal gene transfer or conjugation/introgression , leading to novel pathogenic genotypes . This form of virulence evolution has been well characterized in bacterial , viral , fungal , and parasitic human diseases [4] , [5] , [6] , [7] , [8] , [9] . The ability to cause damage to mammalian hosts is a common theme among all microbial pathogens , making it a key aspect of host-pathogen studies [10] . In the genomic era , it is now possible to combine conventional epidemiological approaches with newly developed molecular typing techniques to gain insight into the emergence and molecular epidemiology of pathogens . These approaches can improve understanding of population dynamics during an outbreak , and may lead to novel methods for the rapid identification , treatment , and diagnosis of emerging infections [11] . In addition , molecular typing serves as an initial approach to classify isolates into distinct genotypes for analysis . Further investigations may include the examination of virulence and phenotypic traits that may be common or distinct between genotypes [6] , [12] , [13] . Gaining insights into the molecular epidemiology and virulence of newly emerging diseases has considerable potential for the rapid assessment and management of newly emerging infections . Over the past decade , Cryptococcus gattii has emerged as a primary pathogen in northwestern North America , including both Canada and the United States [6] , [13] , [14] , [15] , [16] , [17] , [18] . In the past , C . gattii has often been associated with Eucalyptus trees in tropical and subtropical climates , causing disease in immunocompetent hosts at low incidences [19] , [20] , [21] . C . gattii is distinct from its sibling species Cryptococcus neoformans [22] , which more commonly infects immunosuppressed hosts and infects almost one million people annually with over 620 , 000 attributable mortalities [23] , [24] , [25] . C . gattii can be classified into four discrete molecular types ( VGI-VGIV ) , which represent cryptic species as no nuclear allelic exchange between groups has been observed [6] . This molecular classification is significant because VGII is responsible for approximately 95% of the Pacific Northwest infections in Canada and the United States [12] , [15] . The appearance of C . gattii in North America is alarming because this is the first major emergence in a temperate climate , indicating a possible expansion in the endemic ecology of this pathogen [26] , [27] . Several significant questions persist regarding the outbreak and its expansion within the United States . As the global collection of C . gattii isolates expands , the molecular epidemiology of the species has become increasingly informative , particularly through multilocus sequence typing ( MLST ) , which allows data to be readily compared between groups within the research community [6] , [15] , [28] , [29] , [30] . The increase in global and regional isolates that have been typed at the molecular level allows detailed analysis of C . gattii . The analysis of both conserved coding regions , and diverse noncoding regions provides insight into the genotypes responsible for the outbreak . A major finding in this study is a level of underlying diversity within the VGIIa/major genotype in the region of expansion and other geographic locales . Prior studies documented that the C . gattii VGIIa/major genotype isolates from Vancouver Island are highly virulent in experimental murine infection assays [6] . Here we expanded this analysis to examine clinical VGIIa genotype isolates from Vancouver Island , the United States , and Brazil , in addition to an environmental VGIIa isolate from California . Our findings are consistent with recent macrophage intracellular proliferation studies , demonstrating that United States isolates from the recent Pacific NW outbreak exhibit high virulence [31] . The enhanced virulence of isolates from the outbreak region , when compared with those from other regions , suggests that the genotypes circulating in the Pacific NW are inherently increased in their predilection to cause disease in mammalian hosts . In addition to the detailed examination of the VGIIa/major genotype clade , we report that the novel VGIIc genotype is highly virulent in a murine inhalation model . Moreover , the VGIIc genotype was found to have high intracellular proliferation rates in macrophages and a significantly increased percentage of mitochondria with tubular morphology after macrophage exposure , and thus VGIIc isolates share virulence attributes with the VGIIa/major genotype isolates from the Vancouver Island outbreak . These results extend the molecular and phenotypic understanding of the recently discovered VGIIc/novel genotype and help shed light into its possible geographic and molecular origins . These studies provide insights into both the evolutionary history and virulence characteristics of this unique and increasingly fatal fungal outbreak in the temperate climate of the North American Pacific Northwest and highlight the importance of a collaborative interdisciplinary approach to the analysis of emerging pathogens . Application of these approaches may increase awareness of disease risks in the expansion zone , lead to more rapid diagnoses and , as a result , accelerate the implementation of appropriate therapy . Human and veterinary cases of confirmed or suspected C . gattii infections in the states of Washington and Oregon were identified by referring physicians and veterinarians , and subsequently isolates were purified and examined . Melanin production was assayed by growth and dark pigmentation on Staib's niger seed medium , and urease activity was detected by growth and alkaline pH change on Christensen's agar . These tests established that isolates were Cryptococcus ( C . neoformans or C . gattii ) . Isolates were concomitantly examined for resistance to canavanine and utilization of glycine on L-canavanine , glycine , 2-bromothymol blue ( CGB ) agar . Growth on CGB agar indicates that isolates are canavanine resistant , and able to use glycine as a sole carbon source , triggering a bromothymol blue color reaction indicative of C . gattii , whereas C . neoformans is sensitive to canavanine , and cannot use glycine as a sole carbon source , resulting in no growth or coloration in this selective indicator medium . All CGB positive isolates were then grown under rich culture conditions prior to storage at −80°C in 25% glycerol and genomic DNA extraction . For genomic DNA isolation , a modified protocol of the MasterPure Yeast DNA purification kit from Epicentre Biotechnologies was used . Briefly , 500 µl of glass beads ( 425–600 nm ) were added into the combination of cells and 300 µl cell lysis solution . The rest of the method followed the protocol provided by the manufacturer . For multilocus sequence typing analysis ( MLST ) [32] , each isolate was analyzed with a minimum of eight and in some cases sixteen loci . For each isolate , genomic regions were PCR amplified ( Table S1 ) , purified ( ExoSAP-IT ) , and sequenced . All primers used for the analysis were designed specifically to amplify open reading frame ( ORF ) gene sequence regions including those with non-coding DNA regions to maximize discriminatory power . Sequences from both forward and reverse strands were assembled , and manually edited using Sequencher version 4 . 8 ( Gene Codes Corporations ) . Based on BLAST analysis of the GenBank database ( NCBI ) , each allele was assigned a corresponding number . GenBank accession numbers with corresponding allele numbers are listed in the supplementary information ( Table S2 ) . To determine that the nine VGIIc/novel isolates are clonally related , given the level of diversity in the loci and the number of isolates that have been examined , we applied an equation to measure the probability of a genotype occurring more than once in the dataset [33] , [34] . For the variable number of tandem repeat ( VNTR ) analysis , the Tandem Repeat Finder ( TRF ) version 4 . 00 software package was employed for marker development , using the genomic sequence of C . gattii isolate R265 ( http://www . broadinstitute . org/annotation/genome/cryptococcus_neoformans_b . 2/Home . html ) [35] . The identified tandem repeat sequences and 400 bp of the flanking region were extracted from the genomic sequence and ranked according to the number of total repeats and the size of repeat units using an in-house Perl script ( available upon request ) . Markers were examined for stability and those with high variability and stability were chosen for the analysis . Sequences were assembled and edited using Sequencher version 4 . 8 ( Gene Codes Corporations ) and aligned using the Clustal W web based software package ( http://www . ebi . ac . uk/Tools/clustalw2/index . html ) . Mating analysis was conducted on V8 media ( pH 5 ) . Isolates were incubated at room temperature in the dark for 2–4 weeks in dry conditions . All strains were crossed with the VGIII mating type a isolate B4546 and the VGIII mating type α isolate NIH312 , both of which are fertile and commonly used for mating studies [36] . Fertility was assessed by microscopic examination for hyphae , fused clamp cells , basidia , and basidiospore formation . For each VNTR marker , a sequence type was defined as a sequence exhibiting a unique mutation . Each sequence type was confirmed to be unique by BLAST analysis of the NCBI GenBank database [37] . A concatenated VNTR sequence type ( CVST ) was defined as unique combinations of sequence types from the VNTR markers . A multiple alignment of the sequences was carried out using Clustal W software [38] . Analysis of the sequences was conducted using the Neighbor-Joining and Maximum Parsimony methods within the MEGA 3 . 1 software [39] . In addition , the use of the maximum likelihood method ( PhyML 3 . 0 ) with SH-like approximate likelihood-ratio test and HKY85 substitution model was applied [40] , [41] . For this purpose , sequences of the selected VNTR markers were concatenated . We additionally concatenated all of the strain-typing markers including the housekeeping genes used in MLST and VNTR loci for clustering analysis . The haplotype mapping analysis was carried out using TCS software version 1 . 21 ( http://darwin . uvigo . es/software/tcs . html ) [42] . A proliferation assay was previously developed to monitor the intracellular proliferation rate ( IPR ) of individual strains for a 64-hour period following phagocytosis [31] . For this assay , J774 macrophage cells were exposed to cryptococcal cells that were opsonized with 18B7 antibody for 2 hr as described previously [43] . Each well was washed with phosphate-buffered saline ( PBS ) in quadruplicate to remove as many extracellular yeast cells as possible and 1 ml of fresh serum-free DMEM was then added . For time point T = 0 , the 1 ml of DMEM was discarded and 200 µl of sterile dH2O was added into wells to lyse macrophage cells . After 30 minutes , the intracellular yeast were released and collected . Another 200 µl dH2O was added to each well to collect the remaining yeast cells . The intracellular yeast were then mixed with Trypan Blue at a 1∶1 ratio and the live yeast cells were counted . For the subsequent five time points ( T = 16 hrs , T = 24 hrs , T = 40 hrs , T = 48 hrs and T = 64 hrs ) , intracellular cryptococcal cells were collected and independently counted with a hemocytometer . For each strain tested , the time course was repeated at least three independent times , using different batches of macrophages . The IPR value was calculated by dividing the maximum intracellular yeast number by the initial intracellular yeast number at T = 0 . We confirmed that Trypan Blue stains 100% of the cryptococcal cells in a heat-killed culture , but only approximately 5% of cells from a standard overnight culture . Compared to a conventional colony counting method , this method was shown to be more sensitive in detecting the clustered yeast population or yeast cells undergoing budding . IPR values were used to assess how consistent the different VGII genotype subgroups were . For this statistical analysis the medians of each population were compared with the non-parametric Mann-Whitney U-test and values of p<0 . 025 , after controlling for multiplicity , and were accepted as statistically significant ( http://elegans . swmed . edu/~leon/stats/utest . cgi ) . The mitochondrial morphology assays were conducted in a similar way to those in previous studies , with modifications [31] . C . gattii cells , grown overnight at 37°C in DMEM in a 5% CO2 incubator without shaking for 24 hr , or isolated from macrophages 24 hr after infection , were harvested , washed with PBS twice and re-suspended in PBS containing the Mito-Tracker Red CMXRos ( Invitrogen ) at a final concentration of 20 nM . Cells were incubated for 15 min at 37°C . After staining , cells were washed in triplicate and re-suspended in PBS . For each condition , more than 100 yeast cells per replicate for each of the tested strains were chosen randomly and analyzed . For quantifying different mitochondrial morphologies , images were collected using a Zeiss Axiovert 135 TV microscope with a 100× oil immersion Plan-Neofluar objective . Both fluorescence images and phase contrast images were collected simultaneously . Images were captured with identical settings on a QIcam Fast 1394 camera using the QCapture Pro51 version 5 . 1 . 1 software . All Images were processed identically in ImageJ and mitochondrial morphologies were analyzed and counted blindly . Three individual experiments were performed for each condition and the data were tested for normality using the Shapiro-Wilk test . For homogeneity of variances we used the Levene statistic . For statistically significant differences among the mean data we applied a One-Way ANOVA . Multi-comparisons using Tukey Honestly Significant Differences tests were performed to identify statistically significant differences between pairs . A p-value of p<0 . 05 , after controlling for multiplicity , was considered to be statistically significant . Regression analysis was used to measure the correlation between tubular mitochondrial morphology and IPR values; an F-value of P<0 . 05 was considered to be a significant correlation . To examine the virulence potential of global VGII isolates , with a specific emphasis on the Pacific NW VGII outbreak genotypes , two independent murine virulence experiments were conducted at two facilities ( Duke University Medical Center and the Wadsworth Center ) . The murine virulence assays at Duke University Medical Center and the Wadsworth Center used a similar protocol to previous C . gattii and C . neoformans experimental infections [6] , [44] , [45] . At the Duke University Medical Center Animal Facility , virulence was assessed using female A/Jcr mice ( NCI , 18–24 g ) . Strains were cultured in YPD broth for 18–20 h at 30°C , harvested , washed three times with sterile PBS and counted using a hemocytometer to determine cell concentrations . Inocula for both murine experiments were confirmed by plating on YPD and counting colony-forming units ( c . f . u . ) . Nine to ten A/Jcr mice per strain were anesthetized with pentobarbital and infected via intranasal instillation with 5×104 c . f . u . in 50 µl of sterile 1× PBS . Animals that displayed severe morbidity , based on twice-daily examinations , were euthanized . Time to mortality was evaluated for statistical significance using Kaplan–Meier survival curves within the Prism software package ( GraphPad Software ) , and P values were obtained from a log-rank test . Survival data was plotted for graphical analysis using the Prism software package . At the Wadsworth center animal facility , all assays were conducted using male BALB/c mice ( approximately 6 weeks old , 15–20 g , Charles River Laboratories , Inc . ) . Strains were grown overnight in YPD broth at 30°C with shaking . The cells were harvested , washed in PBS , and counted using a hemocytometer . Five mice per strain were anesthetized with a mixture of xylazine–ketamine , and allowed to inhale 105 ( 30 µl ) cryptococcal cells per mouse , via intranasal instillation . Mice were given food and water ad libitum and monitored twice daily . At the first sign of poor health or discomfort , infected animals were euthanized . Brain and lung tissues from the dead animals were cultured on Niger seed agar for C . gattii recovery to confirm infections were due to this pathogen . Time to mortality was evaluated for statistical significance as described above . Two animals from each strain assayed in the study conducted at Duke University were selected for histopathology analysis either at the time of sacrifice or at the conclusion of the experiment for the more attenuated isolates . For each animal , lung samples were collected and stored in 10% neutral buffered formalin . Samples were paraffin embedded and hematoxylin and eosin ( H&E ) stained at the Duke University Research Histology Laboratory . After staining and slide preparation , each sample was examined microscopically for analysis of cryptococcal cell burden and immune responses . Images were captured using an Olympus Vanox microscope ( Duke PhotoPath , Duke University Medical Center ) . The animal studies conducted at the Wadsworth Center were in full compliance with all of the guidelines set forth by the Wadsworth Center Institutional Animal Care and Use Committee ( IACUC ) and in full compliance with the United States Animal Welfare Act ( Public Law 98–198 ) . The Wadsworth Center IACUC approved all of the vertebrate studies . The studies were conducted in facilities accredited by the Association for Assessment and Accreditation of Laboratory Animal Care ( AAALAC ) . The animal studies at Duke University Medical Center were in full compliance with all of the guidelines of the Duke University Medical Center Institutional Animal Care and Use Committee ( IACUC ) and in full compliance with the United States Animal Welfare Act ( Public Law 98–198 ) . The Duke University Medical Center IACUC approved all of the vertebrate studies . The studies were conducted in Division of Laboratory Animal Resources ( DLAR ) facilities that are accredited by the Association for Assessment and Accreditation of Laboratory Animal Care ( AAALAC ) . To examine the C . gattii outbreak isolates collected from 2005 to 2009 ( Figure 1 ) , an in-depth stepwise molecular analysis was applied to each isolate , and the genotypes were compared with other global genotypes . In total , 20 markers were selected for analysis . These markers include both coding and noncoding genomic regions and range in size and allelic diversity ( Table 1 ) . Additionally , all of the markers are randomly distributed among the chromosomes in the most recent assembly of the reference C . gattii VGI genome , WM276 ( Figure 2 ) . Initially , all isolates were sequenced at a total of eight MLST markers , and four variable number of tandem repeats ( VNTR ) markers ( Figure 3 , Table 2 ) . Next , global isolates were selected for diversity , and several isolates from each of the primary genotypes in the expansion region were chosen for sequence analysis at eight additional MLST loci , bringing the total number of genetic markers analyzed for these isolates to 20 ( Figure 4A ) . As expected , the MLST markers were less variable and more conserved , while the VNTR markers allowed for higher-resolution differentiation between isolates that appeared identical by MLST analysis . The generated datasets were then concatenated both without and with VNTR data ( Figure 4B , Figure 4C ) . The combined analysis of the results presented here , and a 30 marker MLST analysis conducted previously [6] , [18] , reveal several findings of interest in relation to VGII genotypes in the region . From the analysis of 34 markers ( 30 MLST/4 VNTR ) , we show that the Vancouver Island VGIIa/major isolates are fully identical at all loci to several recent isolates from Washington and Oregon , as well as a historical clinical isolate ( 1970's ) , NIH444 , from Seattle . Additionally , the VGIIb/minor isolates from Australia and Vancouver Island are identical at 34 total loci , and also identical to VGIIb/minor isolates from Oregon at 20 loci ( 16 MLST/4 VNTR ) . Furthermore , all VGIIc isolates to date are identical across all 20 loci examined ( Figure 4A ) . However , we also are able to discriminate the outbreak VGIIa genotype from an environmental VGIIa isolate from California , CBS7750 , and clinical VGIIa isolates CA1014 and ICB107 from California and Brazil , respectively , at one or more MLST/VNTR loci . It is clear from prior studies that the VGIIa/major and VGIIb/minor isolates are clonal lineages [6] , [12] , [15] , [46] , and here we confirmed that this is the case for the nine VGIIc/novel isolates , based on 7-loci MLST analysis of the global VGII population ( Figure S1 ) ( p<0 . 0001 ) . The largest and most comprehensive dataset arose from the combined analysis of seven MLST and four VNTR loci , resulting in a total of 41 sequence types ( STs ) . This dataset was generated from clinical , veterinary , and environmental C . gattii isolates ( Figure 3 , Figure S1 , Table S3 ) . From the analysis , it is clear that the VGIIa/b/c clusters are all related to each other , but also distinct . In addition , the data show that the VGIIa/major clade is closely clustered to VGIIc , further validating prior reports that examined a more limited number of loci [13] , [47] . In addition , VGIIc ( ST21 ) shares high sequence identity to ST34 , represented by a mating type a clinical isolate from Colombia , suggesting that the VGIIc genotype may have resulted from a-α mating , even though all isolates related to the Pacific NW outbreak are exclusively α mating type . Additionally , Vancouver Island isolates from our collection that had not been fully typed by MLST were sequenced at two loci to determine if any were unrecognized VGIIc isolates ( n = 56 ) ( Figure S2 ) . Of these , 51 were found to be VGIIa , five were VGIIb , and none were VGIIc , consistent with previous data from the region . Thus , VGIIc appears to remain exclusive to the United States , specifically Oregon , and has never been reported from Vancouver Island , the mainland of Canada , Washington State , or elsewhere globally . Within the VGIIa/major cluster , based on the initial MLST analysis of 30 loci , only a single isolate ( ICB107 ) could be distinguished from the other VGIIa isolates , and this was at only one locus [18] . To further investigate this homogeneous population causing the vast majority of the outbreak-related morbidity and mortality , we expanded the molecular analysis to include highly variable regions of the genome . The application of these VNTR markers , in combination with the MLST markers , allowed us to generate five independent STs from within the VGIIa/major genotype and related isolates ( Figure 3 ) . These five sequence types ( ST1 , ST2 , ST3 , ST13 , ST30 ) contained a total of 44 isolates ( Figure 3 , Table S3 ) . The canonical VGIIa/major outbreak genotype , ST1 , contained the vast majority of the 44 isolates ( n = 38 ) . As expected based on previous models of the C . gattii outbreak expansion [13] , ST1 consisted of isolates exclusively from the initial outbreak and expansion zones , including British Columbia , Washington , and Oregon ( Table S3 ) . These results further validate the hypothesis that the epicenter of the outbreak was on Vancouver Island , beginning in the late 1990's , with a direct expansion into neighboring mainland British Columbia and subsequently into the United States [13] . The only exception in this dataset is isolate NIH444 , an older isolate from the region that was isolated from a patient sputum sample in Seattle in the early 1970's [18] , which is also identical at all 34 markers examined . This suggests that the VGIIa/major genotype responsible for most of the outbreak cases may have been circulating in the region prior to the outbreak . The possible travel history of this patient is unknown , and could therefore have involved exposure on Vancouver Island . Overall , this analysis provides increased evidence that the outbreak genotype is unique to the region thus far , and molecularly distinct from closely related isolates from both California and South America . While the homogeneous nature of the VGIIa/major isolates based on robust molecular typing validated previous models , an underlying diversity within this group was also discovered . First , we further validated that the isolate ICB107 ( ST13 ) , from Brazil , was indeed distinct from the ST1 VGIIa/major clade . This isolate differs at one MLST marker ( LAC1 ) , and three VNTR markers ( VNTR3 , VNTR15 , VNTR34 ) . Additionally , the high-resolution sequence analysis was able to discriminate other VGIIa isolates that were collected from California . These include isolate CBS7750 ( ST3 ) , collected from the environment in San Francisco in 1990 [48] , and isolate CA1014 ( ST2 ) , which was isolated from a patient with HIV infection in southern California . Each of these two isolates differs from ST1 due to unique mutations within the VNTR7 and VNTR34 loci , respectively . This shows that similar VGIIa genotype isolates have been found elsewhere , but that none are identical to those circulating as part of the ongoing Vancouver Island outbreak . Whether these isolates are a result of drift from ST1 , or if ST1 arose from one of these related genotypes is not known . In addition to discriminating VGIIa isolates that were not from the outbreak region , we also found a novel ST , ST30 , which is highly similar to ST1 , but divergent at a unique region of VNTR34 . Interestingly , all three of the ST30 isolates are exclusively from Oregon , including two human clinical cases and one marine mammal case ( Figure 1 , Figure 3 , Table S3 ) . These results are consistent with an expansion followed by genetic drift in the highly variable VNTR loci . Isolates of ST30 have not been detected on Vancouver Island , indicating that this divergence is recent , and likely occurred after the expansion of ST1 into the United States . Alternatively , both ST1 ( VGIIa/major ) and ST30 may have been present for a long period , with only ST1 having been transferred to Vancouver Island . To gain insights into the potential origins of the VGIIc genotype , and to assess its position within the overall VGII clade , clustering analysis was applied . Analysis of the combined dataset including 41 sequence types generated from 115 C . gattii isolates shows that the VGIIc genotype is independent , but similar to VGIIa ( Figure 3 ) . The closest relationship determined from the analysis was to ST34 , an isolate from Colombia , which is also of the opposite a mating type . Moving beyond the direct branch , it appears that the VGIIc genotype shares sequence similarities to global isolates from South America , Africa , and also European isolates with likely African origins based on collected clinical case histories . Additionally , the VGIIc group also shares the IGS1 allele with isolates from Australia , further obscuring the possible origins and necessitating a more thorough analysis ( Figure 4A ) . When the clustering analysis was expanded to include additional MLST loci ( Figure 4A ) , both with and without the VNTR markers , the relationships of VGIIc to other global genotypes was further elucidated , with close relationships observed with global isolates from South America , Africa , Europe ( Greece ) , and Australia ( Figure 4B , Figure 4C , Table S4 ) . These results increase the comprehensiveness of the analysis , and allow predictions of the relationship of this genotype to global isolates . Examination of alleles illustrates that , when the analysis is expanded , the VGIIc group appears to be more diverse from VGIIa and VGIIb . Each allele represented in green was initially denoted as an allele that was unique to the VGIIc genotype , with a total of seven such alleles ( Figure 4A ) . To further elucidate the possible origins of these alleles , isolates selected based on their global diversity were sequenced at these loci ( Figure 4A ) . Identical matches for four of the seven VGIIc-unique alleles were identified in isolates from Brazil , Australia , Europe , and European isolates with likely African origins , while three alleles ( SXI1α , HOG1 , and CRG1 ) remain unique to this novel genotype and only seen in Oregon thus far ( Figure 4A ) . To further characterize the genetic relationships among the global isolates in relation to the outbreak isolates , maximum likelihood ( ML ) analysis was applied . Initially , the isolates were characterized at 15 MLST loci , excluding the MAT locus so that both α and a isolates could be included . This analysis indicates that VGIIc may be more distantly related to the VGIIa/major genotype than initially observed . In addition , analysis of the 15 MLST loci shows a possible relation of VGIIc with isolates from South America , Africa , Europe , and Australia ( Figure 4B ) . When this analysis was expanded to also include the four VNTR loci , similar results for the global comparisons of all genotypes and the relation of VGIIc to global isolates were observed ( Figure 4C ) . For these reasons , additional sampling and analysis will be necessary to more precisely elucidate if this novel virulent genotype originated locally , or originated in an under-sampled region . In addition to clustering analyses , TCS haplotype-mapping software was applied to establish the evolutionary histories of the MLST alleles examined during the analysis ( Figure 5 , Figure 6 , Figure S3 ) . From the sequence results , all of the VGIIc isolates were determined to be 100% identical , indicating that there was likely a recent emergence in which all of the isolates are clonally derived . To test this hypothesis , the TCS analysis allowed for the examination of individual loci to determine which alleles are likely ancestral , intermediate , or recently derived . Of the sixteen loci examined , eight were consistent with VGIIc possessing the ancestral allele , six of the alleles were distal nodes at the terminal end of the respective haplotype networks , and two loci were of intermediate allele positions . Alleles with ancestral genotypes are less informative because these alleles may not have diversified over time in the VGIIc lineage for various reasons , including selection pressures and overall lack of diversity at the allele . When only non-ancestral alleles were examined , 75% lay at the distal ends of their haplotype maps . Intriguingly , the three VGIIc alleles unique to the genotype ( SXI1α , HOG1 , and CRG1 ) all have distal placements ( Figure 5A–C ) . Additionally , the most recent ancestor to VGIIc in all three cases can be shown to derive from isolates that are from South America and Australia , indicating that VGIIc may have emerged out of one of these regions ( Figure 5 ) . While other regions including Europe and North America can be seen , no other regions are observed for all three of these alleles . These distal placements are consistent with a recent divergence of the unique VGIIc lineage . The haplotype analysis , in combination with the lack of any underlying diversity within the nine VGIIc isolates analyzed , indicates a recent emergence of this novel virulent genotype in Oregon . To examine the role that recombination may have played in the population structure of the VGII molecular type , we conducted paired allele analysis for 25 representative global isolates ( Figure 6 , Figure S4 ) . The discovery of all four possible allele combinations between two unlinked loci ( AB , ab , Ab , aB ) serves as evidence for likely recombination [49] . From this analysis , we show that isolates collected from South America , Africa , and Australia appear to be involved in recombination events . Representative VGIIa/major , VGIIb/minor , and VGIIc/novel isolates were found among groups of recombinant isolates . A group of ten isolates , all α , from South America and Africa ( Figure S4 ) appeared most commonly as recombinant partners , although several a mating type isolates were also less frequently involved . In further support , when we examined the number of genotypes present by region and compared this data to the total number of genotypes represented ( Figure S1 ) , it is clear that South America and Africa populations are more diverse when compared with isolates from North America , which are more clonal . Additionally , while the observed diversity in Australia was lower than South America and Africa , this may be attributable to sampling bias of clonal regions as prior studies have shown that this continent is a region with high levels of recombination due to both same-sex and opposite-sex mating events [50] . In addition to the paired allele analysis , allele diagrams were constructed to observe possible recombination within individual MLST loci ( Figure S5 ) . The most parsimonious explanation for allelic diversity in 11 of the MLST loci analyzed is as a result of consecutive and/or independent mutations within the population . Within the four remaining loci , there exists at least one hybrid allele that may be the result of a recombination event between two hypothesized parental alleles in the global VGII population ( Table 3 , Figure S5 ) . Phenotypic mating results were conducted and illustrate that the VGIIa/major ( α ) , VGIIc/novel ( α ) , VGII mating type a genotypes , as well as several of the proposed parental contributors from the allelic and genotypic recombination analysis show fertility with the production of spores when mated with fertile VGIII isolates ( Table S5 ) . Taken together , this suggests that both α-α and a-α mating events may be contributing to the formation of recombinant genotypes as well as the production of infectious spores . There were no examples of alleles introgressed into VGII from VGI , VGIII , or VGIV , in accord with findings that the four VG molecular types likely represent cryptic species [6] , [29] . In summary , these results suggest that recombination events may be critical driving forces in the evolution of C . gattii VGII diversity , which may in part contribute to the generation of genotypes displaying increased virulence . It has recently been shown that intracellular proliferation rate ( IPR ) values for cryptococcal cells within macrophages are positively correlated with virulence in the murine model for cryptococcosis [31] . To further elucidate the potential virulence of outbreak isolates collected from the United States , proliferation rates of selected isolates were tested and compared to other isolates for which proliferation data had been previously obtained . In total , IPR values for eight of the nine VGIIc isolates were measured ( Figure 7A ) . In addition , the type strains for VGIIa/major ( R265 ) and VGIIb/minor ( R272 ) were included as controls , and previously published data for other VGIIa and VGIIb isolates were included for comparisons [31] . On the basis of individual strains , seven of the eight VGIIc/novel isolates showed high IPR levels , with only a single outlier ( EJB52 ) that had a low IPR value ( 0 . 97 ) . Taken together , the median IPR value for VGIIc is significantly closer to that of VGIIa/major than to VGIIb/minor ( Figure 7A ) . These results indicate that the VGIIc genotype has a similar intracellular phenotype , and thus virulence profile to the VGIIa/major genotype . This is noteworthy because previous analysis showed that the VGIIa/major genotype isolates from the outbreak had unusually high IPR values , and the VGIIc isolates from the same outbreak are here shown to have similarly high IPR values . Another unique feature of the outbreak VGIIa/major isolates is the ability to form highly tubular mitochondria after intracellular parasitism , a characteristic that correlates with both IPR and murine virulence [31] . To explore the morphology of VGIIc isolates , we examined selected isolates in DMEM media and after exposure to macrophages . This analysis included two VGII environmental isolates ( CBS8684 , CBS7750 ) and four of the VGIIc/novel isolates . As expected , the vast majority of the mitochondria for all six isolates were non-tubular after exposure to DMEM media alone ( Figure 7B ) . However , after exposure to macrophages , three of the four VGIIc isolates tested showed significantly higher percentages of tubular morphology ( Figure 7C ) . The lone VGIIc isolate that did not exhibit this morphology ( EJB52 ) was the same isolate that also had a low IPR value , and is thus an overall outlier for the VGIIc genotype . When the results of IPR versus percentage of cells exhibiting tubular morphology were plotted , the graph showed a statistically significant correlation of the two measures with an R2 value of 0 . 85 ( Figure 7D ) . These results further indicate that the VGIIc genotype is phenotypically similar to the Vancouver Island VGIIa/major outbreak strains . Our results also support evidence for similar mechanisms regulating the increased virulence seen in the novel VGIIc genotype . The exact roles that the mitochondrial tubular morphology might play in virulence are not yet known . However , the distinct phenotype is clearly unique to the outbreak isolates and is correlated with an increased ability to grow and divide within host innate immune cells . The VGIIc isolates were found to be highly virulent in the murine inhalation model of infection . Two studies were conducted to examine virulence . In the first murine experiment a total of six isolates ( n = 5 animals/isolate ) , were examined including two VGIIc isolates ( Figure 8A ) . The VGIIa/major isolate R265 served as a positive control for high virulence , based on prior studies [6] , and the VGIIc isolates EJB15 and EJB18 showed similar virulence with this well characterized virulent isolate . Additionally , two VGIIa isolates that are not hypothesized to be from the current Vancouver Island outbreak , including NIH444 , which is fully identical across 34 markers , and isolate CA1014 , which differs from R265 at VNTR34 , show a significant reduction in virulence compared to the high virulence isolates ( P<0 . 05 ) . Finally , in accordance with previous studies , the VGIIb/minor type strain R272 from Vancouver Island was avirulent in this model . The analysis of virulence within the VGII genotype was extended in a second experiment , in which 12 isolates ( n = 9–10 animals/isolate ) were examined . This study included two VGIIa/major isolates from the outbreak zone , two VGIIb/minor isolates from the outbreak zone , five of the novel VGIIc isolates , two VGIIa-related isolates that are not part of the outbreak , and the C . neoformans var . grubii type strain , H99 . The H99 isolate used ( H99S ) has been shown to be highly virulent in the murine model of infection [44] , [51] . As expected , all five of the VGIIc isolates from Oregon as well as the VGIIa/major isolates from Vancouver Island and Oregon , and the highly virulent H99 isolate exhibited a high level of virulence ( median survival = 20 . 6 days ) . The VGIIb/minor isolates tested were significantly decreased in virulence compared to the more virulent VGIIa and VGIIc genotypes ( P<0 . 005 ) . The VGIIb isolate R272 was avirulent whereas the VGIIb isolate EJB53 from Oregon exhibited significantly less virulence compared to the VGIIa/major and VGIIc isolates ( P<0 . 005 , median survival = 46 days ) . Similar to the first animal study , two VGIIa isolates that differ at one or more molecular markers from the major VGIIa outbreak genotypes were also tested . The environmental isolate CBS7750 and a clinical isolate from South America ICB107 were significantly attenuated ( P<0 . 005 ) ( Figure 8B ) . These results provide further evidence that these are related to but distinguishable from isolates that are specific to the Vancouver Island outbreak , and subsequent United States expansion , and are decreased in ability to mount fatal infections in a mouse intranasal instillation model of infection . The cause of infection was further evaluated by histopathological analysis of lung sections recovered from two infected animals per isolate at sacrifice . Harvested organs were processed and sectioned for slides with H&E staining . The lungs from the virulent isolates showed significant inflammation and numerous cryptococcal cells dispersed throughout the alveoli , in accordance with severe pulmonary infection . Our findings show that there are no major clinical differences between pulmonary infections with the infectious genotypes VGIIa/major ( Figure 8C ) , and the novel VGIIc genotype ( Figure 8D ) . These results further support similar disease progression caused by these two highly virulent outbreak genotypes . The findings presented here document that the outbreak of C . gattii in Western North America is continuing to expand throughout this temperate region , and that the outbreak isolates in the United States of both the VGIIa/major genotype and the novel VGIIc genotype are clonally derived and highly virulent in host models of infection . These conclusions are based on an extensive molecular analysis of isolates collected from the United States ( Table 2 ) and a comprehensive global collection of VGII isolates of diverse geographic origin ( Figure S1 ) , examining both conserved and divergent regions of the genome . The virulence analysis is based on assays in both murine derived macrophages and mice . These findings demonstrate that this emerging and fatal outbreak is continuing to expand , and that the virulence of these isolates is unusually high when compared to isolates of closely related but distinguishable genotypes found in other non-outbreak regions . The continued expansion of C . gattii in the United States is ongoing , and the diversity of hosts increasing . Cases have been observed in urban and rural areas , and have occurred in a range of mammals [16] , [52] . On Vancouver Island and the mainland of British Columbia , cases have been documented in marine and terrestrial mammals including cats , dogs , porpoises , ferrets , and llamas [15] , [52] , [53] . This trend has continued in the United States , with several cases in agrarian , domestic , and wild terrestrial mammals , as well as marine mammals , adding elk , alpacas , and sheep to the aforementioned list ( Table S1 ) [13] , [14] , [17] . The co-expansion of the outbreak among mammals and humans is significant for several reasons . Non-migratory mammals serve as sentinels for disease expansion , particularly given that isolation of C . gattii from the environment is difficult , and not yet successful at all in Oregon . Additionally , the threat to agricultural and domestic animals is significant and thus the need for cooperation among health officials is critical . Finally , the widespread spectrum of disease illustrates that the organism is likely to be pervasive in the environment , and that physicians and veterinarians should be well informed of symptoms to facilitate early diagnoses , and successful isolate collection and tracking . A major question in the study of this outbreak is whether sexual recombination , either within or between mating types , is occurring or has occurred in the region . The possibility of meiosis is important for two reasons . The first is that sexual recombination is postulated to be a driving force for the increased virulence of the VGIIa/major genotype , supported by the discovery of a diploid VGIIa/major isolate , an intermediate in unisexual mating ( all nine VGIIc/novel isolates are haploid ) [6] , [36] . C . gattii has also been shown to undergo opposite sex mating in the laboratory , although this has not yet been observed to occur between two isolates of the VGII molecular type [36] , [54] , [55] . Studies in C . neoformans have shown that this related pathogen completes a full a-α sexual cycle in association with plants [56] . Additionally , a recent study of environmentally sampled Australian VGI isolates demonstrated evidence for recombination via both opposite and same-sex mating [50] . Taken together , available evidence indicates that both opposite and same-sex mating are naturally occurring in populations . This evidence lends support to the hypothesis that meiosis might be a factor in the forces that are driving high virulence in the outbreak region . The second major event that results from sexual processes in the pathogenic Cryptococcus species is the formation of spores . Small spores ranging from 1–2 µm in diameter have been observed to be produced in large numbers as the result of opposite sex mating in both C . neoformans and C . gattii [57] , [58] . Studies by Lin and colleagues showed that sexual spores can be produced as the result of a meiotic process occurring between cells of the same mating type , a process referred to as unisexual or same-sex mating [59] . Several studies have shown spores to be pathogenic in animal models of infection . Two previous studies both showed evidence for virulence of Cryptococcus spores , and in one case provided evidence for enhanced virulence compared to yeast cells [60] , [61] . More recently , studies have shown that Cryptococcus neoformans spores are indeed virulent in the murine intranasal instillation model of infection [44] , [62] , providing evidence that spores should be considered as infectious propagules in models examining infections , expansion , and emergence of both C . neoformans and C . gattii . Given that all of the Pacific NW isolates are α mating type , and particles small enough to be spores are present in the air [26] , [63] , the most parsimonious model is that if these are spores , they are produced via α-α unisexual reproduction . Our findings further indicate that mitochondria may play a significant role in the increased virulence seen in the outbreak isolates [31] . Tubular morphology and the increased ability to proliferate within immune cells indicate that the ability to proliferate and survive within host cells is fundamental to virulence . The possible role of mitochondrial involvement is intriguing and also increasingly relevant based on studies that have shown mitochondrial inheritance and recombination may impact C . gattii evolution , with the inheritance of the mitochondrial genome from the a mating type parent in opposite-sex mating [64] , [65] . Future studies in this area should address the roles that mitochondrial genes , or nuclear genes that regulate mitochondria may play in the hypervirulence observed in the outbreak isolates . Furthermore , it may be that cell-cell fusion events via mating and mitochondrial exchange without meiosis or nuclear genetic exchange have played roles in recombination and virulence acquisition in naturally occurring C . gattii populations [64] , [65] . A central question in the field lies in the possible origins of the virulent genotypes . For the VGIIa and VGIIc lineages , it is clear that those are unique to the Pacific NW , and either arose there locally , or were transferred from an under-sampled region ( Australia , South America , Africa ) . Isolates that are related to , but distinct at one or more molecular marker from VGIIa have been identified in San Francisco ( CBS7750 ) , southern California ( CA1014 ) , and South America ( ICB107 ) . However , in each of these cases , the isolates are not identical with the VGIIa/major isolates from the Pacific NW . Whether the outbreak isolates are derived from these isolates , or alternatively that these isolates are derived from the outbreak lineage is at present unclear . In the VGIIb/minor outbreak lineage , isolates from Australia are identical at all 30 MLST loci and four VNTRs analyzed , and the most parsimonious model is that the two are directly related . While it is conceivable that both the Australian and the Vancouver Island VGIIb/minor genotype isolates were dispersed independently from another geographic locale , until isolates are identified conclusively from another locale the most parsimonious model is transfer from Australia to the Pacific NW . We note that a single isolate with a related but distinct genotype ( isolate 99/473 ) from the Caribbean has been identified; and other isolates have been reported to share the VGIIb genotype but have been analyzed at a limited number of MLST markers ( n = 7 ) which is insufficient to establish how closely related these isolates are to the outbreak VGIIb/minor genotype strains [29] . The origins of VGIIc are unclear , with the genotype possibly arriving in the Pacific NW from South America , Africa , Europe , or Australia . Alternatively , this novel unique genotype may have arisen locally . As for the geographic origins of VGII diversity , this also remains to be established and may involve populations in Australia , South America , and Africa . It is clear that there is considerable diversity among isolates from South America . As we originally proposed as an alternative model [6] , and has been independently presented by other investigators ( W . Meyer , T . Boekhout , JP Xu , pers . comm . ) , South America may represent a source of diversity and ongoing generation of novel isolates . Analysis of 8 MLST loci in this study indicates that in South America and the Caribbean there are 14 genotypes seen in 21 isolates , while in North America only 3 genotypes have been observed through the analysis of 64 isolates ( Figure S1 ) . Additionally , there is accumulating evidence that fertile isolates of both a and α mating type are present in South America [29] , and thus ongoing a-α opposite sex mating may be occurring there . It is also clear that a unique set of VGII isolates are circulating in Australia , and there is evidence for ongoing recombination in α only and a-α populations , suggesting that mating contributes to the generation of diversity in Australia [36] , [49] , [54] , [55] , [66] , [67] . Finally , the analysis of global VGII isolates reveals genetic diversity in Africa , and given the recent findings that C . neoformans likely originated in sub-Saharan Africa ( A . Litvintseva and T . Mitchell , pers . Comm . ) , further analysis of African C . gattii isolates is clearly warranted . It remains possible that South America , Africa , or both represent the ancestral populations of C . gattii , and that more recent dispersal events from other established populations ( for example , from Australia to the Pacific Northwest ) have occurred to contribute to the outbreak . As yet , all of the isolates found in the Pacific Northwest are α mating type . Thus , if sexual reproduction is occurring in the Pacific Northwest , it would appear to involve same-sex mating occurring under environmental conditions . Recent studies have documented that C . neoformans and C . gattii are stimulated to undergo opposite-sex mating in laboratory conditions that simulate environmental niches ( pigeon guano medium , co-culture with plants ) and thus similar conditions may be necessary in nature [56] , [68] . Overall , both the VGIIa/major and the VGIIc/novel genotypes contain a number of MLST loci that are thus far restricted to these lineages , and their origins remain to be identified . Independently of the variables leading up to and influencing this outbreak , the major concern is and continues to be the inexorable expansion throughout the region . From 1999 through 2003 , the cases were largely restricted to Vancouver Island . Between 2003 and 2006 , the outbreak expanded into neighboring mainland British Columbia and then into Washington and Oregon from 2005 to 2009 . Based on this historical trajectory of expansion , the outbreak may continue to expand into the neighboring region of Northern California , and possibly further . The rising incidence of cryptococcosis cases in humans and animals highlights the need for enhanced awareness in the region , and those regions that may potentially become involved . While rare , little is currently known about how or why specific humans and animals become infected . Increased vigilance may decrease the time from infection to diagnosis , and thus lead to more effective treatment and a reduction in mortality rates . The potential dangers of travel-associated risks should be noted , as a growing number of cases attributable to travel within the Pacific NW region have been documented [69] , [70] . Northern California has similar temperate climates to endemic regions within Oregon , leading to the hypothesis that the emergence may expand there , while expansion eastward may be limited by winters with average temperatures often below freezing [17] . The expansion of the outbreak into California is plausible based on several studies documenting the presence of C . gattii throughout the state and in Mexico . C . gattii molecular type VGII was environmentally isolated in the San Francisco area in 1990 ( isolate CBS7750 ) [48] , and there have also been two confirmed and one travel-associated case of C . gattii molecular type VGI in California . Of the VGI cases , one occurred in a male Atlantic bottlenose dolphin in San Diego , one was isolated from a liver transplant recipient in San Francisco , and the other from an otherwise healthy patient in North Carolina with travel history to the San Francisco region [71] , [72] , [73] . In addition C . gattii has been reported in southern California among a cohort of HIV/AIDS patients [74] . Recently , studies of clinical isolates from Mexico revealed all four molecular types of C . gattii to be present [75] . Taken together , the hypothesis that the virulent isolates from the Pacific NW will expand into California must be considered by both physicians and public health officials . During the coming years , monitoring and researching the outbreak expansion as a multidisciplinary effort will be critical . The ability to bring diverse groups of professionals interested in C . gattii expansion has been greatly facilitated through the formation of the Cryptococcus gattii working group of the Pacific Northwest [17] . From a research standpoint , further examination of the molecular mechanisms underlying the increased virulence in both VGIIa/major and VGIIc/novel will be useful for the development of aggressive treatments that may be needed . Furthermore , increased efforts to determine the ecology and population dynamics of C . gattii in the region , and elucidating the evolutionary history of the VGIIc genotype will be critical to gain further insights into the origins of this unprecedented and frequently fatal fungal outbreak .
Emerging and reemerging infectious diseases are increasing worldwide and represent a major public health concern . One class of emerging human and animal diseases is caused by fungi . In this study , we examine the expansion on an outbreak of a fungus , Cryptococcus gattii , in the Pacific Northwest of the United States . This fungus has been considered a tropical fungus , but emerged to cause an outbreak in the temperate climes of Vancouver Island in 1999 that is now causing disease in humans and animals in the United States . In this study we applied a method of sequence bar-coding to determine how the isolates causing disease are related to those on Vancouver Island and elsewhere globally . We also expand on the discovery of a new pathogenic strain recently identified only in Oregon and show that it is highly virulent in immune cell and whole animal virulence experiments . These studies extend our understanding of how diseases emerge in new climates and how they adapt to these regions to cause disease . Our findings suggest further expansion into neighboring regions is likely to occur and aim to increase disease awareness in the region .
You are an expert at summarizing long articles. Proceed to summarize the following text: Ribosomal protein L3 is an evolutionarily conserved protein that participates in the assembly of early pre-60S particles . We report that the rpl3[W255C] allele , which affects the affinity and function of translation elongation factors , impairs cytoplasmic maturation of 20S pre-rRNA . This was not seen for other mutations in or depletion of L3 or other 60S ribosomal proteins . Surprisingly , pre-40S particles containing 20S pre-rRNA form translation-competent 80S ribosomes , and translation inhibition partially suppresses 20S pre-rRNA accumulation . The GTP-dependent translation initiation factor Fun12 ( yeast eIF5B ) shows similar in vivo binding to ribosomal particles from wild-type and rpl3[W255C] cells . However , the GTPase activity of eIF5B failed to stimulate processing of 20S pre-rRNA when assayed with ribosomal particles purified from rpl3[W255C] cells . We conclude that L3 plays an important role in the function of eIF5B in stimulating 3′ end processing of 18S rRNA in the context of 80S ribosomes that have not yet engaged in translation . These findings indicate that the correct conformation of the GTPase activation region is assessed in a quality control step during maturation of cytoplasmic pre-ribosomal particles . Ribosomes are very intricate ribonucleoprotein particles that catalyse protein synthesis . In all organisms , ribosomes are composed of two ribosomal subunits ( r-subunits ) , the large one ( 60S , LSU ) being about twice the size of the small one ( 40S , SSU ) [1] , [2] . In eukaryotes , synthesis of ribosomes is a multicomponent , multistep process that is highly compartmentalised ( for reviews , see [3]–[5] ) . Most ribosome maturation reactions take place in the nucleolus , but later steps occur in the nucleoplasm and cytoplasm [6]–[8] . Although evolutionary conserved throughout eukaryotes , ribosome biogenesis has been best studied in the yeast Saccharomyces cerevisiae . In the yeast nucleolus , the mature 18S , 5 . 8S and 25S rRNAs are transcribed as a single large precursor rRNA ( pre-rRNA ) that undergoes both co-transcriptional and post-transcriptional processing [9] . Concomitant with processing , the pre-RNAs undergo RNA modification and folding , association with trans-acting factors , and assembly with 5S rRNA and most ribosomal proteins ( r-proteins ) to form pre-ribosomal particles . The yeast pre-rRNA processing pathway is well-characterised [10] ( see Figure S1 ) . Among the pre-rRNA processing reactions , cleavage at site A2 is special since it separates the intermediates on the LSU and SSU synthesis pathway , which apparently follow independent nuclear maturation . Correct nuclear maturation of pre-ribosomal particles leads to the recruitment of export factors and acquisition of export competence . Incorrectly assembled pre-ribosomal particles are strongly retained in the nucle ( ol ) us and are targeted to degradation ( for examples , see [11] , [12] and references therein ) . Cytoplasmic pre-ribosomal particles undergo final maturation before becoming translationally active [8] , [13] . Cytoplasmic maturation of pre-60S particles involves pre-rRNA processing of the 6S pre-rRNA to mature 5 . 8S rRNA [6] and the dissociation and recycling of several export and assembly factors by an ordered series of linked ATPase- and GTPase-dependent steps [8] , [14] . Among these factors are Tif6 and Nmd3 , which are proposed to impede joining of pre-60S with mature 40S r-subunits [15] , [16]; thus , they should be removed before mature 60S r-subunits enter translation . Concomitant with this , the assembly of several r-proteins occurs , amongst them P0 ( also P0 in the new proposed nomenclature of r-proteins [17] ) , L10 ( L16 ) , L24 ( L24e ) and L40 ( L40e ) . During cytoplasmic maturation of pre-40S particles , Dim1 dimethylates two consecutive , conserved adenines at the 3′ end of the 18S rRNA [18] , followed by Nob1-dependent cleavage of the 20S pre-rRNA at site D to produce the mature 18S rRNA 3′ end [7] , [19] . Late-acting factors associated with the cytoplasmic pre-40S particles may prevent premature association with translation initiation factors , mRNA , initiator tRNA , and mature 60S r-subunits [20] . Only a few 40S r-proteins are thought to stably assemble in the cytoplasm , and these are likely to include S3 ( S3 ) , S10 ( S10e ) and S26 ( S26e ) [21] . We are interested in understanding the contribution of specific 60S r-proteins to ribosome biogenesis . L3 is an evolutionarily conserved protein that contains two tightly packed globular domains bound on the solvent side of the LSU , close to the binding region for GTP-dependent translation factors . Moreover , L3 contains two extensions that enter deep into the central core of the LSU and are very close to the peptidyl transferase center ( PTC ) ( Figure S2 ) [2] , [17] . Dinman and coworkers have extensively studied the role of yeast L3 in ribosome function and revealed that it modulates translation elongation by coordinating both the accommodation of charged tRNAs and the binding of elongation factor 2 ( eEF2 ) ( e . g . [22] , [23] ) . We have previously undertaken the analysis of L3 in yeast ribosome synthesis . Our results indicate that L3 has an essential role in the formation of early pre-60S r-particles [24] . To further study the role of L3 in ribosome synthesis , we have analysed the phenotypic effects of a collection of viable rpl3 point mutants . Herein , we show that , unexpectedly , the rpl3[W255C] mutation leads to the accumulation of translation-competent cytoplasmic pre-40S r-particles containing the 20S pre-rRNA . These in vivo results unequivocally demonstrate the requirement of the 60S r-subunit for efficient 20S pre-rRNA processing . Two recent studies have revealed that 20S pre-rRNA cleavage to mature 18S rRNA might require the association of pre-40S r-particles with the yeast translation initiation factor eIF5B/Fun12 and the 60S r-subunit to form an 80S-like complex [25] , [26] . In agreement with these reports , our results demonstrate that despite the fact that in vivo yeast eIF5B associates with similar efficiency to wild-type and L3[W255C] containing ribosomes , its GTPase activity is unable to stimulate processing of 20S pre-rRNA in rpl3[W255C] cells . Taking into account that the L3[W255C] mutant protein alters the structure of the 60S r-subunits [27] and the in vitro affinity of ribosomes for the elongation factors eEF1 and eEF2 [23] , we postulate that the correct conformation of the binding site of ribosome-dependent GTPases is used as a quality control step to ensure proper maturation of cytoplasmic pre-ribosomal particles . To define better the role of L3 in the normal accumulation of 60S r-subunits , we studied the phenotypes of selected rpl3 point mutations ( Figure S2A ) . The rpl3[K30E] and rpl3[Q371H] mutations were found to be synthetically lethal with mutants of genes encoding components of the Dpb6-containing subcomplex [28] , [29] . The rpl3[W255C] , rpl3[P257T] , rpl3[I282T] and rpl3[W255C , P257T] mutations have been reported to affect different translation properties [22] , [23] , [30] . All these mutant proteins support growth as the sole source of L3 , although not at wild-type levels , and are recessive ( Figure S3 , and data not shown ) . We next examined the polysome profiles of the different mutants grown at 23°C relative to an isogenic wild-type strain . As shown in Figure 1 , the rpl3[K30E] , rpl3[Q371H] and rpl3[P257T] mutants clearly displayed profiles consistent with a deficit of 60S r-subunits . Notably is the appearance of polysome halfmers ( indicated with arrows in Figure 1 ) , which reflect formation of 43S pre-initiation complexes that are not bound by 60S r-subunits . Moreover , the rpl3[I282T] mutant apparently has a mild translation initiation defect . Unexpectedly , both the single rpl3[W255C] and the double rpl3[W255C , P257T] mutants displayed a clear deficit in free 40S relative to 60S r-subunits . This finding was not previously reported for the original mak8-1 mutant , which consists of the double rpl3 mutation W255C P257T [31] . Northern analyses were used to determine whether the polysome profiles obtained for the rpl3[W255C] and the rpl3[W255C , P257T] mutants correlated with defects in pre-rRNA processing or rRNA accumulation . Comparison of total RNA isolated from the rpl3 mutants and the isogenic wild-type strain revealed only slight differences in the levels of most pre-rRNAs in rpl3 mutants ( Figure 2 ) . The exception was a dramatic accumulation of 20S pre-rRNA in the rpl3[W255C] and rpl3[W255C , P257T] mutants , accompanied by modest reductions in mature 18S rRNA accumulation . These phenotypes were similar to those observed in the previously characterised rps14A[R136A] mutant , which served as a positive control for 20S pre-rRNA accumulation [32] . We conclude that , unexpectedly for a specific mutation in a 60S r-subunit protein , the mutation rpl3[W255C] leads to a 40S r-subunit biogenesis deficit due to a defect in 20S pre-rRNA processing . Processing of the 20S pre-rRNA occurs in the cytoplasm [7] , so a defect in 20S pre-rRNA processing might result from either reduced export of pre-40S particles or impaired cleavage of cytoplasmic 20S pre-rRNA . To assess pre-40S export , we analysed the subcellular localisation of the 40S r-subunit reporter S2-eGFP in wild-type and rpl3[W255C] cells . As shown in Figure 3A and Figure S4 , both S2-eGFP and the 60S r-subunit reporter L25-eGFP were almost exclusively cytoplasmic in both wild-type and rpl3[W255C] cells . We also visualised the 20S pre-rRNA and its precursors by FISH using a probe complementary to the 5′ region of ITS1 . In the wild-type strain , the FISH signal was predominantly nucleolar with a faint cytoplasmic signal ( Figure 3B ) . This was expected , since the 20S pre-rRNA is rapidly converted to mature 18S rRNA following export of pre-40S particles to the cytoplasm . However , in the rpl3[W255C] mutant , the signal was substantially stronger and predominantly cytoplasmic , indicating that the unprocessed 20S pre-rRNA accumulated in the cytoplasm of rpl3[W255C] cells . The 20S pre-rRNA is dimethylated at the 3′ end of 18S rRNA by Dim1 following export and prior to cleavage [33] . Primer-extension is blocked by the presence of the dimethylation , which was clearly present in 20S pre-rRNA of rpl3[W255C] cells ( Figure 3C ) , confirming that the block in maturation occurs following export . We conclude that the 20S pre-rRNA is exported from the nucleus but fails to be efficiently processed in the cytoplasm in rpl3[W255C] cells . Identical results were obtained in analyses of rpl3[W255C] yeast strains derived from W303 or BY4741 , showing our findings to be independent of genetic background and any secondary mutation ( s ) ( data not shown ) . We previously reported that pre-40S r-particles containing the 20S pre-rRNA could be efficiently incorporated into translating ribosomes in ubi3Δub mutant cells [34] . In contrast , pre-40S r-particles are not found in polysomes in wild-type cells or in most mutants that accumulate cytoplasmic 20S pre-rRNA [25] , [32] , [35] , [36] . Interestingly , pre-40S r-particles can engage with mRNAs and 60S subunits but are unable to efficiently elongate in cells depleted of Rio1 or Nob1 , or expressing S14A[R136A] [25] , [36] , [37] . To assess whether the pre-40S r-particles accumulated in rpl3[W255C] cells engage in translation , the distribution of the 20S pre-rRNA in polysome gradients was determined by northern blotting and compared to the wild type and cells expressing L3[Q371H] or S14A[R136A] ( Figure 4 ) . In wild-type and rpl3[Q371H] mutant cells , 20S pre-rRNA co-migrated with the 40S r-subunit peak . In rps14A[R136A] cells , the 20S pre-rRNA accumulated in the 80S peak , whereas the rpl3[W255C] mutant showed 20S pre-rRNA in complexes of high molecular weight that co-sedimented with polysomes . To confirm that the slowly sedimenting 20S pre-rRNA containing particles were not simply aggregates , cell extracts were prepared under polysome run-off conditions ( omission of cycloheximide ) either in standard buffer or in a buffer lacking MgCl2 ( which causes dissociation of 80S couples into 40S and 60S r-subunits ) . In the absence of cycloheximide , the 20S pre-rRNA was shifted from the high molecular weight fractions to the 80S fractions in the presence of MgCl2 or to 40S fractions in the absence of MgCl2 ( Figure S5 ) . Moreover , quantification of the 20S/18S and 20S/25S ratios showed similar values for each polysomal fraction in Figure 4 , indicating that the accumulated , 20S pre-rRNA containing pre-40S r-particles are competent for both translation initiation and elongation ( data not shown ) . We conclude that the presence of L3[W255C] in the 60S r-subunits leads to the accumulation of pre-40S particles that assemble into 80S ribosomes and are competent for translation elongation . We assessed whether translation influences the accumulation of pre-40S r-particles in the rpl3[W255C] mutant ( Figure 5 ) . Protein synthesis was inhibited by treatment of wild-type and rpl3[W255C] strains with 0 . 8 µg/ml cycloheximide ( the lowest concentration that arrested growth ) . As shown in Figure 5A , cycloheximide treatment for 6 h did not significantly affect steady-state levels of mature 25S and 18S rRNA in the wild-type or the rpl3[W255C] strain and resulted in only a minor accumulation of 35S pre-rRNA in wild-type cells . Cycloheximide also had little effect on 20S pre-rRNA levels in the wild-type strain , whereas a 2-fold reduction was already observed 1 h after cycloheximide addition to rpl3[W255C] cells . To discard any indirect effect of the cycloheximide treatment , we blocked translation initiation by using a cdc33–42 mutant , in which Cdc33/eIF4E is defective in recognition of the cap structure of mRNAs during translation initiation [38] . As shown in Figure 5B , in the cdc33–42 rpl3[W255C] double mutant , the 20S pre-rRNA levels again decreased about 3-fold in comparison to those from an isogenic rpl3[W255C] single mutant , while the 20S pre-rRNA levels in the cdc33–42 single mutant were similar to those of the wild type strain . The fraction of ribosomes engaged in translation is much lower in slow-growing than in fast-growing cells [39] . Consistently , when wild-type and rpl3[W255C] cells were cultivated in different media , we found a clear correlation between the measured doubling times and the levels of accumulation of 20S pre-rRNA in the rpl3[W255C] strain ( Figure 5C and Table S4 ) . Thus , fast-growing cells accumulated about 4-fold more 20S pre-rRNA than slow-growing cells . These data indicate that 20S pre-rRNA accumulation in rpl3[W255C] cells is promoted by active translation , suggesting that 20S pre-rRNA processing and/or decay is prevented in pre-40S r-particles engaged in translation . Fun12 ( the yeast homologue of eIF5B ) is a GTPase required for binding of initiator tRNA and r-subunit joining during translation initiation [40] . In addition , Fun12/eIF5B is required for efficient 20S pre-rRNA processing [26] , [41] , which requires binding of Fun12/eIF5B to pre-40S r-particles and mature 60S r-subunits [25] , [26] . To assess binding of Fun12 to 60S r-subunits containing L3[W255C] , we expressed a fully functional genomically integrated Fun12-TAP construct [42] in wild-type and rpl3[W255C] cells and performed immunoprecipitation experiments with IgG-Sepharose . As shown in Figure 6A , western blot analysis indicated that Fun12-TAP co-precipitates Nob1 and r-proteins from both r-subunits to the same extent in both strains . Furthermore , Northern hybridisation showed that Fun12-TAP co-precipitated similar levels of 20S pre-rRNA and mature 25S rRNAs relative to the levels of their respective inputs in cells of both strains ( Figure 6B ) . As previously reported [26] , Fun12 also co-precipitated nuclear 35S , 32S and 27S pre-rRNAs . The significance of this is unclear , but more efficient association with these species was observed in wild-type compared to rpl3[W255C] cells . Since Fun12/eIF5B co-precipitates several pre-rRNAs , we studied the association of TAP-tagged Fun12/eIF5B with pre-ribosomal particles by sucrose gradient analysis . As shown in Figure S6A , Fun12-TAP is enriched in the low-molecular-mass fractions , in free 40S r-subunits , 80S and polysomes . In agreement with our previous results , the sedimentation pattern of Fun12-TAP was similar in cell extracts of wild-type and rpl3[W255C] cells . Likewise , analysis of the sedimentation pattern of fully functional N-terminal PTH-tagged Nob1 in sucrose gradients showed that PTH-Nob1 is enriched in the low-molecular-mass region and free 40S r-subunit fractions of the gradient with a weaker peak around 80S to 90S in wild-type cells . This sedimentation pattern was also similar for wild-type and rpl3[W255C] cells ( Figure S6B ) . We conclude that the binding of Fun12/eIF5B and Nob1 to 80S-like r-particles is not significantly altered in rpl3[W255C] cells . In vitro cleavage of 20S pre-rRNA by the endonuclease Nob1 is stimulated by addition of ATP or GTP , and Fun12/eIF5B was identified as the relevant GTPase [26] . We used this assay to determine whether L3 directly contributes to 20S pre-rRNA cleavage . To this end , we purified pre-ribosomal particles from cells expressing L3 or L3[W255C] via N-terminally PTH-tagged Nob1 , which co-purifies both free pre-40S r-particles and pre-40S-60S complexes [26] . The stimulation of 20S pre-rRNA processing upon addition of ATP or GTP was assessed by primer extension ( Figure 7 ) . As controls , pre-ribosomes were also purified from cells expressing L3[K30E] and rsa3Δ cells; both mutations reduce 60S r-subunit accumulation to a similar extent , but do not lead to 20S pre-rRNA accumulation ( [29] , and Figure 2 ) . Nob1 , like other PIN-domain nucleases , requires Mn2+ for efficient in vitro cleavage ( see ref . [19] and references therein ) . During the incubations required for purification of the pre-ribosomes , cleavage is inhibited by the use of buffers containing only Mg2+ . Cleavage is then activated at time 0 by addition of Mn2+ plus the relevant nucleotide . However , Nob1 inhibition in the absence of added Mn2+ is not complete , so the 0 min time point contains some level of pre-rRNA that has been cleaved at site D [26] . Thus , in our assays , the efficiency of cleavage was quantified relative to the signal at time 0 . Moreover , the amount of 20S pre-rRNA that is recovered and available for cleavage is not the same for different mutants . In particular , the in vivo 20S pre-rRNA processing defect shown by rpl3[W255C] strains results in substantially higher recovery , as shown by the stronger primer extension stop at the 18S rRNA dimethylation sites at A1781/1782 and the increased signal at site D at time 0 . Since only a small fraction of the total 20S pre-rRNA is cleaved , even under optimal conditions , the primer extension stop at A1781/1782 was used as a control for input to normalize between the different time points for each strain . Comparison of primer extension stops at site D and at A1781/1782 in the 0 min samples , indicated that the fraction of the 20S pre-rRNA that was cleaved during pre-ribosome purification was similar in each sample ( Figure S7 ) . As shown in Figures 7A and 7B , addition of Mn2+ plus ATP to pre-ribosomes purified from the wild-type cells increased the level of cleaved 20S pre-rRNA about 3 . 5-fold after 30 min incubation . Cleavage of 20S pre-rRNA in the presence of ATP was mildly reduced when r-particles were purified from rpl3[K30E] , rpl3[W255C] or rsa3Δ cells ( only 2 . 5-fold stimulation at 30 min ) probably reflecting the deficit in 60S r-subunit levels . In contrast , when cleavage was activated by addition of Mn2+ plus GTP , the level of 20S pre-rRNA cleaved at site D was elevated around 2 . 5 fold in pre-ribosomes purified from the wild-type , rpl3[K30E] , or rsa3Δ strains , whereas substantially less cleavage was observed for pre-ribosomes recovered form rpl3[W255C] cells ( less than 1 . 5-fold stimulation at 30 min ) ( Figures 7C and 7D ) . We conclude that impairment of 20S pre-rRNA processing in rpl3[W255C] cells is , at least , partially due to the inability of the GTP-dependent activity of Fun12/eIF5B to stimulate the Nob1 cleavage activity at site D . Since L3[W255C] protein is a component of 60S r-subunits , these data demonstrate that 20S pre-rRNA processing could occur in particles formed by pre-40S and pre-60S or mature 60S r-subunits . To test for functional interactions between L3 and Nob1 , we combined the rpl3[W255C] mutation with the NOB1-TAP allele , which expresses Nob1 fused at its C-terminus with a TAP-tag . This nob1 allele also leads to a mild 20S pre-rRNA accumulation , in contrast to the PTH-NOB1 construct , which behaves like the wild type protein ( [26] , and data not shown ) . As shown in Figure 8 , the NOB1-TAP allele specifically exacerbated the growth defect of the rpl3[W255C] mutant at both 23°C or 30°C . Taken together with the results of the previous section , these data strongly suggest that the conformational changes of 60S r-subunits caused by the W255C mutation in L3 negatively affect the functionality of the D-site endonuclease Nob1 . Multiple steps in the translation cycle are mediated by ribosome-associated GTPases , including eIF5B/Fun12 ( r-subunit joining ) , eEF1 and eEF2 ( translation elongation ) , eEF3 ( translation termination ) and even Hbs1 ( release of stalled ribosomes and NGD ) ( reviewed in [43] ) . Each of these associates with a common binding site in the 60S r-subunit , which is referred to as the GTPase-associated center . Recent reports have proposed that final maturation of cytoplasmic pre-40S r-particles is stimulated by association with Fun12 and mature 60S r-subunits [25] , [26] . Here , we demonstrate a functional link between formation of the correct structure in the GTPase-associated center region of 60S r-subunits and the stimulation of 20S pre-rRNA cleavage . L3 has been described as the “gatekeeper to the A-site” [23] and the L3[W255C] protein alters the structure of the 60S r-subunits [27] and the binding in vitro of elongation factors [23] . These results strongly suggest that the correct conformation of the domain forming the binding site for the ribosome-dependent GTPases is a prerequisite for final 40S r-subunit maturation . This model is outlined in Figure 9 . Examination of the L3 structure within the 60S r-subunit ( see Figure S2B ) reveals that W255 is located at the tip of the internal “finger” that extends through the A-site to the PTC . Indeed , this residue makes the closest approach of any amino acid to the PTC site . Residue P257 induces a bend in the finger that helps position W255 [17] , [22] , [23] , [27] . Biochemical and molecular analyses show that L3 functions in binding of aminoacylated tRNAs and eEF2 . Moreover , mutations in L3 affect peptidyl-transferase activity , antibiotic sensitivity and translation of RNA derived from the “killer” dsRNA virus ( see [22] , [23] and references therein ) . The rpl3[W255C] allele was found to be functionally important as this mutation conferred resistance to anisomycin , decreased peptidyltransfer rate and increased programmed −1 r-frameshifting ( −1 PRF ) , leading to loss of the killer virus . All these phenotypes appear to result from increased affinity of ribosomes containing L3[W255C] for the eEF1-GTP-aminoacylated tRNA ternary complex and decreased affinity for eEF2 [22] , [23] . In the 80S ribosome structure , the W255 residue is about 12 nm away from the 3′ end of the 18S rRNA , making it unlikely to directly contact the 20S pre-rRNA processing machinery ( see Figure S2B ) . It also appears unlikely that the reduced 20S cleavage in rpl3[W255C] strains is an indirect effect of reduced translation of ( a ) 20S pre-rRNA processing factor ( s ) , since other rpl3 alleles ( e . g . rpl3[P257T] and rpl3[I282T] ) also result in strong anisomycin resistance , peptidyl-transferase inhibition and stimulation of −1 PRF [30] but do not impair 20S pre-rRNA processing or turnover ( Figures 1 and 2 ) . Therefore , the observed 20S pre-rRNA processing impairment in rpl3[W255C] cells is likely caused by the loss of proper interaction and/or function of a distinct trans-acting factor that stimulates the activity of the D-site endonuclease Nob1 . In line with such a scenario , we observed that only the rpl3[W255C] mutation exacerbates the mild slow-growth phenotype of a NOB1-TAP allele , which expresses a C-terminally TAP-tagged Nob1 protein ( Figure 8 ) . The observation that ribosomes containing L3[W255C] show alterations in the affinity and function of elongation factors eEF1 and eEF2 [22] , [23] , suggested that functional interactions with Fun12/eIF5B might also be impaired . The structural homology between the eIF5B G-domains of Fun12/eIF5B , eEF1 and eEF2 strongly indicates that these proteins interact similarly with the ribosome ( [44] , reviewed in [43] , [45] ) . The GTPase activity of Fun12 promotes r-subunit joining [40] , [46] and stimulates in vitro Nob1-dependent 20S pre-rRNA cleavage in purified pre-40S r-particles in conjunction with mature 60S r-subunits [26] . Stimulation of 20S pre-rRNA cleavage by GTP is lost in pre-40S r-particles that were associated with 60S particles containing L3[W255C] ( Figures 7C and 7D ) . Since Fun12 is responsible for GTP-mediated stimulation of 20S pre-rRNA cleavage in vitro [26] , we conclude that Fun12 function ( i . e . its GTP-hydrolysis dependent conformational change ) is practically impaired in ribosomes containing L3[W255C] . This does not appear to be due to strongly reduced binding of Fun12 to 80S particles , since Fun12-TAP co-precipitated in vivo particles containing 20S pre-rRNA and 25S rRNA with similar efficiencies from wild-type and rpl3[W255C] cells ( Figure 6 ) . Fun12-TAP also co-precipitated 35S , 32S and 27S pre-rRNA species , and maturation of both 35S and 27S pre-rRNAs is delayed in a fun12Δ strain [26] , [41] . The rpl3[W255C] allele did not clearly alter 35S or 27S pre-rRNA processing ( see Figure 2 ) , but strongly reduced association of these pre-rRNA species with Fun12-TAP ( Figure 6 ) . The significance of the association of Fun12 with nuclear and nucleolar pre-ribosomes remains to be determined . In vitro , cleavage of 20S pre-rRNA in purified pre-40S r-particles is also activated by an ATP-binding factor that remains to be identified [26] . The stimulation of 20S pre-rRNA processing by ATP is reduced , to slightly different extents , for r-particles purified from rpl3[K30E] , rsa3Δ or rpl3[W255C] cells ( Figures 7A and 7B ) . This indicates that the factor responsible for ATP-stimulated cleavage is also dependent on 60S r-subunits , but with a specificity that is different from Fun12 . Analysis of the presence of 20S pre-rRNA in polysome fractions clearly indicated that pre-40S particles accumulated in rpl3[W255C] cells are competent for elongation ( Figures 4 and S4 ) . This was unexpected , since late-acting pre-40S synthesis factors are expected to block association with translation factors , 60S r-subunits and the mRNA [20] , [47] . This indicates that the block induced by L3[W255C] allows these factors to dissociate from the late pre-40S r-particles . Supporting this model , Nob1 , which should impair binding of translation initiation factors , was not detected in polysomal fractions of either wild-type or rpl3[W255C] cells ( [25] , Figure S6B ) . Consistent with this , pre-40S r-particles that are engaged in translation were unable to undergo 20S pre-rRNA processing . The accumulation of 20S pre-rRNA in rpl3[W255C] cells was partially suppressed by reduced translation ( Figure 5 ) , suggesting that the loss of Nob1 , and therefore loss of cleavage competence , from pre-40S particles might be stimulated by translation . In Dictyostelium discoideum immature r-particles efficiently enter polysomes and require active translation for final maturation [48] . In contrast , yeast 80S complexes formed during 40S r-subunit maturation are unable to initiate translation [25] and 20S pre-rRNA maturation is opposed by the engagement of the pre-ribosomal particles in protein synthesis . During late maturation of pre-60S r-particles , release of the nucleolar shuttling factor Tif6 is dependent on the GTPase Efl1/Ria1 , which is also homologous to eEF2 [49] , [50] and apparently binds to the same sites as eEF2 in 60S r-subunits [51] . Tif6 prevents the association between 40S and 60S r-subunits [15] , [52] , [53] but mutations that trap Tif6 on cytoplasmic pre-60S r-particles , including the recently described P-site loop mutations of L10 [54] , do not lead to 20S pre-rRNA accumulation [50] , [55]–[59] ( see also Figure S8 ) . These results imply that 20S pre-rRNA processing is not exclusively performed in 80S-like particles or that Tif6 does not fully prevent association of pre-ribosomal subunits . Characterization of the L10 P-site loop mutants led to the conclusion that cytoplasmic maturation of pre-60S r-subunits also involves verification of the correct structure in the binding site of ribosome-stimulated GTPases ( [54] , reviewed in [60] ) . Our results unequivocally indicate that cytoplasmic maturation of pre-40S to translation competent 40S r-subunits also relies on the proper conformation of this binding site within pre-60S r-particles via Fun12 . The common binding site for the ribosome-dependent GTPases is a key structural feature for most steps in translation . Together the data indicate that the correct structure in this domain is required for the final maturation steps for both r-subunits prior to their entry into the translating pool . All yeast strains used in this study are listed in Table S1 , plasmids in Table S2 and oligonucleotides in Table S3 . Unless otherwise indicated , experiments were conducted in the W303 [61] or BY4741 [62] genetic backgrounds . Strain CDK35-4A [63] was crossed to JDY318 [YCplac111-rpl3-W255C] , the resulting diploid was sporulated , tetrads dissected and the progeny examined . JDY945 is a segregant of the resulting diploid , which contains the cdc33::TRP1 and rpl3::HIS3MX6 alleles and harbours the YCplac33-cdc33–42 and the YCplac111-rpl3[W255C] plasmid . Strain JDY318 [YCplac111-rpl3-W255C] was crossed to DY121 , the resulting diploid was sporulated , tetrads dissected and the progeny examined . JDY1025 is a segregant of the resulting diploid , which contains the FUN12-TAP::TRP1 and rpl3::HIS3MX6 alleles and harbours the YCplac111-rpl3-W255C plasmid . Strain DY121 was a generous gift from R . H . Singer [42] . Growth and handling of yeast and standard media were done following established procedures [64] . Plasmids YCplac111-RPL3 , YCplac111-rpl3-Q371H ( also known as YCplac111-rpl3-101 ) , YCplac111-rpl3-K30E ( also known as YCplac111-rpl3-102 ) , YCplac22-RPL3 , YCplac22- rpl3-Q371H and YCplac22- rpl3-K30E have been previously described [29] . To generate YCplac111-rpl3-W25C and YCplac22-rpl3-W255C , site directed mutagenesis was performed on wild-type RPL3 cloned into YCplac111or YCplac22 , respectively [65] . All inserts were fully sequenced . Plasmid YCplac22-rps14A-R136A was generated by a similar strategy . Plasmids pRS316-RPL25-eGFP , pRS316-RPS2-eGFP and pRS314-DsRed-NOP1 ( generous gift from J . Bassler and E . Hurt ) have been previously described [66]–[68] . Plasmid pRS415-PTH-NOB1 has also been previously described [26] . Other plasmids used in this study are described in Table S2 . Cell extracts for polysome and r-subunit analyses were prepared and analysed as previously described [69] using an ISCO UA-6 system equipped to continuously monitor A254 . When needed , fractions of 0 . 5 ml were collected from the gradients; protein and RNA were extracted from the different fractions as exactly described [70] , and analysed as described below by northern or western blot analyses . RNA extraction , northern hybridisation and primer extension analyses were carried out according to standard procedures [71] , [72] . In all experiments , RNA was extracted from samples corresponding to 10 OD600 units of exponentially grown cells . Equal amounts of total RNA ( 5 µg ) were loaded on gels or used for primer extension reactions [72] . For primer extensions , Superscript III ( Invitrogen ) was used . The sequences of oligonucleotides used for northern hybridisation and primer extension analyses are listed in Table S3 . Phosphorimager analysis was performed with a FLA-5100 imaging system ( Fujifilm ) . To test pre-40S export , the wild-type strain and the rpl3[W255C] mutant were transformed with pRS316 plasmids harbouring the L25-eGFP [66] or S2-eGFP [67] reporters ( gifts from J . Bassler ) and inspected by fluorescence microscopy as previously described [12] , [73] . To examine the localization of the 20S pre-rRNA , fluorescence in situ hybridisation ( FISH ) was carried out as previously described [34] , [74] , using a Cy3-labelled ITS1-specific probe ( see Table S3 ) . The 20S pre-rRNA in vitro cleavage assays were performed with pre-ribosomal particles purified via N-terminally PTH-tagged Nob1 as previously described [26] . Briefly , pre-ribosomal particles were immunoprecipitated using immunoglobulin G ( IgG ) -Sepharose beads . Nucleotides were added to a final concentration of 1 mM . Reactions were incubated at 20°C for 0 , 2 , 5 , 10 and 30 min; after these incubation times , RNA was extracted as previously described [75] and analysed by primer extension , as described above , using oligonucleotide ITS1RT . Extracts from wild-type or rpl3[W255C] cells expressing TAP-tagged Fun12 were immunoprecipitated using IgG-Sepharose beads as previously described [75] . RNA was recovered from the beads and total cell extracts with phenol-chloroform exactly as previously described [75] and analysed by Northern blotting as described above .
Recent progress has provided us with detailed knowledge of the structure and function of eukaryotic ribosomes . However , our understanding of the intricate processes of pre-ribosome assembly and the transition to translation-competent ribosomal subunits remains incomplete . The early and intermediate steps of ribosome assembly occur successively in the nucleolus and nucleoplasm . The pre-ribosomal subunits are then exported to the cytoplasm where final maturation steps , notably including D site cleavage of the 20S pre-rRNA to mature 18S rRNA , confer subunit joining and translation competence . Recent evidence indicates that pre-40S subunits are subject to a quality control step involving the GTP-dependent translation initiation factor eIF5B/Fun12 , in the context of 80S-like ribosomes . Here , we demonstrate the involvement of 60S subunits in promoting 20S pre-rRNA cleavage . In particular , we show that a specific point mutation in the 60S subunit ribosomal protein L3 ( rpl3[W255C] ) leads to the accumulation of pre-40S particles that contain the 20S pre-rRNA but are translation-competent . Notably , this mutation prevents the stimulation of the GTPase activity of eIF5B/Fun12 , which is also required for site D cleavage . We conclude that L3 plays an important role in regulating the function of eIF5B/Fun12 during 3′ end processing of 18S rRNA at site D , in the context of 80S ribosomes that have not yet engaged in translation .
You are an expert at summarizing long articles. Proceed to summarize the following text: Dengue virus ( DENV ) is a re-emerging arthropod borne flavivirus that infects more than 300 million people worldwide , leading to 50 , 000 deaths annually . Because dendritic cells ( DC ) in the skin and blood are the first target cells for DENV , we sought to investigate the early molecular events involved in the host response to the virus in primary human monocyte-derived dendritic cells ( Mo-DC ) . Using a genome-wide transcriptome analysis of DENV2-infected human Mo-DC , three major responses were identified within hours of infection - the activation of IRF3/7/STAT1 and NF-κB-driven antiviral and inflammatory networks , as well as the stimulation of an oxidative stress response that included the stimulation of an Nrf2-dependent antioxidant gene transcriptional program . DENV2 infection resulted in the intracellular accumulation of reactive oxygen species ( ROS ) that was dependent on NADPH-oxidase ( NOX ) . A decrease in ROS levels through chemical or genetic inhibition of the NOX-complex dampened the innate immune responses to DENV infection and facilitated DENV replication; ROS were also essential in driving mitochondrial apoptosis in infected Mo-DC . In addition to stimulating innate immune responses to DENV , increased ROS led to the activation of bystander Mo-DC which up-regulated maturation/activation markers and were less susceptible to viral replication . We have identified a critical role for the transcription factor Nrf2 in limiting both antiviral and cell death responses to the virus by feedback modulation of oxidative stress . Silencing of Nrf2 by RNA interference increased DENV-associated immune and apoptotic responses . Taken together , these data demonstrate that the level of oxidative stress is critical to the control of both antiviral and apoptotic programs in DENV-infected human Mo-DC and highlight the importance of redox homeostasis in the outcome of DENV infection . Dengue virus ( DENV ) is the leading arthropod-borne viral infection in the world , and represents a major global human health concern . DENV is endemic in more than 100 countries with up to 3 billion people in tropical regions of the world at risk of infection [1]–[3] . Recently , DENV has expanded its global range , with long-term outbreaks in South America and reintroduction into North America through Florida and Texas , with each of these outbreaks accompanied by increased disease severity . Of the estimated 50–100 million annual cases , the majority of infected individuals develop a self-limiting febrile illness , but approximately 500 , 000 clinical cases result in more severe manifestations , such as DENV-induced hemorrhagic fever and shock syndrome [1] , leading to 25–50 , 000 deaths per year [4] . The pathogenesis of dengue is incompletely understood and the factors that determine whether infection manifests as self-limiting dengue fever or progresses to life-threatening illness remains unanswered . Dengue is an RNA virus of the Flaviviridae family with 4 closely related serotypes that exhibit inter- and intra-serotypic genetic diversity [5]–[9] . Innate recognition of DENV involves a spectrum of pattern recognition receptors ( PRR ) that sense conserved molecular components termed pathogen associated molecular patterns ( PAMP ) , and together orchestrate antiviral responses to the viral infection . The cytoplasmic helicases RIG-I and MDA-5 have a central role in the host response to DENV by contributing to DENV protection in hepatocytes [10] . Additionally , TLR3 and TLR7 recognize DENV RNA and mount a rapid protective immune response in human monocytic cells and plasmacytoid dendritic cells , respectively [11] , [12] . Signaling through these different cellular sensors leads to the activation of the interferon pathway that restricts viral proliferation and contributes to the establishment of adaptive immune responses via NF-κB-mediated cytokine and chemokine release [13]–[16] . Interestingly , the host immune response , activated in response to DENV infection , not only mediates protection against disease , but also contributes to disease severity [1] . For example , high levels of circulating pro-inflammatory cytokines such as IL-1β or TNF-α in DENV-infected patients correlates with severe dengue fever , compared to patients suffering with mild dengue fever [17] . Reactive oxygen species ( ROS ) production , generated as a consequence of microbial invasion , has long been known to exert an antimicrobial effect in phagocytes [18] . The activation of the antiviral and inflammatory signaling pathways has also been linked with the production of ROS [19]–[23] , which include oxygen ions and peroxides that are produced as byproducts of aerobic metabolism . Because of the high chemical reactivity of ROS , cells possess scavenger antioxidant mechanisms that maintain redox homeostasis [24]–[26] . Signaling pathways downstream of ROS detection activate the transcription factor nuclear factor-erythroid 2-related factor 2 ( Nrf2 ) [24]–[26] , which binds antioxidant response elements ( ARE ) within the promoters of genes encoding antioxidant and detoxifying enzymes . Nrf2-dependent antioxidant genes act synergistically to reduce oxidative stress by quenching ROS [24]–[26] . Increased generation of ROS and changes in redox homeostasis have been described in the context of many viral infections [23] , [27]–[33] and the failure to maintain an appropriate redox balance contributes to viral pathogenesis through alterations of biological structures and the massive induction of cell death [34]–[36] . In the flavivirus family , hepatitis C virus ( HCV ) has been shown to promote oxidative stress and manipulate antioxidant systems , leading to chronic disease [31] , [37] , [38] . As well , DENV was shown to stimulate oxidative stress in hepatocytes leading to production of the chemokine CCL5 and to activation of the transcriptional regulator C/EBP beta [39] . Furthermore , HepG2 xenografted SCID mice presented alterations in oxidative stress status and increased inflammatory cytokines following DENV infection [40] . More recently , oxidative stress-induced damage and alterations in redox status have been associated with increased disease severity in DENV-infected patients , suggesting a possible role for oxidative stress in DENV-induced pathogenesis [41]–[44] . Interestingly , circulating monocytes from glucose-6-phosphate dehydrogenase ( G6PD ) -deficient patients , displayed an increased susceptibility to DENV infection and replication [45] . The G6PD deletion affects ROS production , thus linking cellular oxidative state and susceptibility to DENV infection . Altogether , these observations underline the importance of the redox homeostasis in DENV infection and suggest an important interplay between the generation of oxidative stress and the immunopathology of dengue disease . Initial contact between DENV and innate immune cells plays an essential role in the outcome of the infection . Indeed , DENV infection pushes monocytes towards a CD16+ inflammatory phenotype that facilitates plasmablast differentiation and induction of anti-DENV antibody responses [46] . Given the importance of DC in bridging the innate and adaptive immune response , and since DC in the skin and peripheral blood are the first target cells for DENV after transmission via a mosquito bite [47]–[49] , evaluation of the early molecular events in DC is crucial to the understanding of DENV pathogenesis . In the present study , we generated in-depth transcriptome analysis , coupled with biochemical and functional analyses of the early host response to DENV infection in primary Mo-DC . DENV infection triggered an NADPH-oxidase ( NOX ) -dependent oxidative stress response that was required for the activation of IRF3/7/STAT1 and NF-κB-mediated antiviral responses and for mitochondrial-dependent apoptosis . Furthermore , we have identified a critical role for the transcription factor Nrf2 in regulating both antiviral and inflammatory gene response to the virus by feedback modulation of oxidative stress . Overall , these studies highlight the importance of redox homeostasis in the outcome of DENV infection . An in vitro model of de novo DENV infection was established using primary human monocytes differentiated in vitro with Mo-DC-differentiation medium containing GM-CSF and IL-4 . Primary CD14+ CD1a− monocytes were less permissive to DENV2 infection , whereas infectivity increased progressively as the cells differentiated toward the Mo-DC ( CD14− CD1a+ ) phenotype ( 4 . 66±0 . 45% of DENV+ cells in monocytes at day 0 vs 79 . 6±0 . 47% in Mo-DC at day 7 ) ( Fig . 1A ) . A strong statistical correlation between a CD14−CD1a+ phenotype and DENV infection was confirmed by the nonparametric Spearman test ( r = 0 . 9829; p<0 . 0001; n = 15 ) . DENV2 viral RNA accumulation was detected after a lag period of 6 h and increased exponentially thereafter ( Fig . 1B ) , which corroborates a previous report demonstrating release of infectious particles [50] . Prior to the onset of detectable DENV replication , an antiviral response was mounted by the infected Mo-DC population , as demonstrated by the increase in IFN-β , IFIT1 and CCL5 gene expression ( Fig . 1B ) . DENV infected Mo-DC in a dose dependent manner to a maximum of ∼80% infectivity at a MOI of 20 ( Fig . 1C ) . As a consequence of early virus sensing , a broad antiviral and inflammatory response was generated as shown by the phosphorylation of IRF3 and STAT1 ( Fig . 1D ) and significant release of IFN-α , TNF-α and IL-6 ( Fig . 1E ) by the infected cells . Previous studies reported cleavage of the endoplasmic reticulum adaptor STING upon DENV infection in Mo-DC [51] . However , in our experimental model and with the viral strain used , a modest 20% decrease in STING expression was observed at 48 h after infection ( Fig . 1D ) . Altogether these data demonstrate that DENV-infected Mo-DC generate a broad host response and secrete an array of antiviral and inflammatory cytokines in response to the virus . To characterize signaling pathways involved in the host intrinsic response to DENV2 infection , a transcriptome analysis of DENV-infected Mo-DC was performed; Fig . 2A represents a waterfall plot of differentially expressed genes ( DEG; selected based on fold change >±1 . 3 , p value <0 . 05 ) after DENV2 infection . Most changes in gene expression appeared early , with over 7000 genes either up- or down-regulated by 6 h after infection ( Fig . 2A ) . Pathway analysis identified multiple canonical networks coordinately regulated at all times after infection; the expected IFN/IRF antiviral pathways as well as the NF-κB-dependent pro-inflammatory pathways were all highly enriched after de novo DENV2 infection ( Fig . 2B ) . We also noticed an enrichment of networks associated with the generation of a pro- and anti-oxidant stress response ( Fig . 2B ) . Further gene analysis represents the top 50 DEG over time following DENV infection ( Fig . 2C ) ; among the top up-regulated genes , two subclasses predominated – interferon-stimulated genes ( ISGs ) such as ISG15 , IFIT1 , IFIT2 , IFIT3 , OASL , OAS2 , CCL5 , HES4 ( presented in black ) and more surprisingly a large set of antioxidant genes belonging to the metallothionein family including MT1A , MT2A , MT1E , MT1X , MT1G , MT1H , and MT1F ( presented in red ) ( Fig . 2C ) . Based on the regulation of gene networks activated or repressed after DENV2 infection , Fig . 2D illustrates a word cloud map of possibly activated ( red ) or inhibited ( green ) transcription factors controlling gene networks at 6 h and 24 h after DENV2 challenge ( Fig . 2D ) . At 6 h after infection , two subclasses of transcription factors predominated: 1 ) transcription factors associated with cellular stress-responses including TP53 ( p53 ) , EPAS1 , HIF1A and NFE2L2 ( Nrf2 ) ; and 2 ) transcriptional regulators associated with the antiviral program including IRF1/3/7 , STAT1/ISGF3 and NF-κB complex ( Fig . 2D ) . By 24 h post-infection , the activity of stress-related transcription factors decreased , with the exception of TP53 , while transcription factors driving the antiviral response - predominantly IRF7 and NF-κB - were highly active ( Fig . 2D ) . A Fluidigm BioMark high throughput qPCR assay encompassing a cross-section of genes identified in the genomic analysis ( S1 Table ) was used to validate the transcriptome data; the pattern of gene expression at various times after DENV2 infection was similar for three different donors ( S1A Figure ) . Computational analysis identified different kinetics of IFN induction , as well as sustained up-regulation of chemokines , Th1 cytokines , ISGs and antiviral transcription factors ( S1B Figure ) . A strong statistical correlation between the log fold change for the microarray values and the log fold change for the BioMark values was confirmed by a Spearman correlation test ( S1C Figure ) ( r = 0 . 8399194; p = 4 . 576e-14; n = 49 ) . In order to gain systems-wide insight into DENV-modulated transcriptome , a functional clustering ( node analysis ) ( Fig . 3 ) , as well as gene-pathway checkerboard analysis ( S2 Figure ) of DENV-induced DEGs was performed . This functional clustering identified at 6 h ( Fig . 3A and S2A Figure ) and 24 h ( Fig . 3B and S2B Figure ) a variety of transcriptional sub-networks and biological processes regulated by DENV . The Nrf2-mediated oxidative stress response pathway , the top differentially regulated pathway in DENV-infected Mo-DC at 6 h ( S2A Figure ) , was triggered prior to the onset of viral replication and intersected with other pathways such as NF-κB , IRF and STAT signaling ( Fig . 3A and S2A Figure ) . At the same time , hypoxia pathway controlled by the transcription factor HIF1-α was predominantly down regulated ( Fig . 3A and S2A Figure ) . By 24 h the activity of the Nrf2-driven pathway decreased , whereas the expansion and increased interaction among the antiviral , inflammatory and death response networks predominated ( Fig . 3B and S2B Figure ) . Concomitantly , genes related to mitochondrial function were all significantly down regulated and presumably associated with an increase in the apoptotic response ( Fig . 3B and S2B Figure ) . The role of reactive oxygen species ( ROS ) as specific second messengers in signaling cascades involved in cell proliferation , differentiation and immune activation has been well documented [52] . In light of the array data and to evaluate if ROS are involved in the recognition of DENV , Mo-DC were infected and ROS formation was monitored by flow cytometry using the oxidant-sensitive fluorescent detection probe CM-H2DCFDA . ROS production was induced in DENV-infected Mo-DC , as reflected in the 2-fold increase in DCF fluorescence detected by FACS at 18 h after infection ( p = 0 . 0405 ) ( Fig . 4A ) . Also , DENV infection increased intracellular ROS accumulation in a dose dependent manner ( Fig . 4B ) . A strong statistical correlation between DENV infection and the accumulation of ROS was confirmed by the nonparametric Spearman test ( r = 0 . 7635; p<0 . 0001; n = 15 ) ( Fig . 4C ) . Although ROS are generated intracellularly , the primary sources of ROS are plasma membrane oxidases , particularly NADPH oxidases . ROS were detected as early as 3 h after infection , and ROS production was suppressed by pre-treatment with the antioxidant diphenyleneidonium chloride ( DPI ) , an NADPH-oxidase ( NOX ) inhibitor ( Fig . 4D ) . ROS production was independently confirmed in Mo-DC by the use of pyocyanin ( N-methyl-1-hydroxyphenazine ) , an oxidative stress inducer , as denoted by the 1 . 8 fold increase in ROS generation at 3 h after stimulation ( Fig . 4D ) . The involvement of NOX in DENV-induced ROS accumulation was further confirmed by the increased phosphorylation of the p47 subunit of the NADPH-oxidase ( p = 0 . 0404 ) ( Fig . 4E ) . Interference with NADPH-oxidase activity using siRNA-mediated silencing of the catalytic gp91phox subunit limited ROS accumulation in response to de novo DENV infection ( p = 0 . 0328 ) ( Fig . 4F ) . To examine whether cellular oxidative stress impacted the immediate host response to DENV , we evaluated the effect of exogenous ROS addition on expression of DENV-induced antiviral genes . Treatment with increasing concentrations of hydrogen peroxide ( H2O2 ) did not stimulate immune responses in Mo-DC; however addition of H2O2 moderately potentiated the elevation of DENV-induced antiviral gene expression ( Fig . 4G ) . Next , the role of ROS in triggering the early host response to DENV2 was evaluated by treating infected Mo-DC with increasing concentrations of DPI , an NADPH-oxidase inhibitor . Strikingly , phosphorylation of IRF3 , STAT1 and IκBα , as well as the induction of ISGs such as RIG-I and IFIT1 – all markers of the antiviral response – were inhibited in a dose-dependent manner by DPI ( Fig . 5A ) . The observation that NOX-inhibitor blocked DENV-induced immune response was further confirmed by quantitative intracellular measurement of STAT1 phosphorylation . Indeed , DPI prevented the increase in STAT1 phosphorylation detected by PhosFlow following DENV infection . Importantly , IFNβ-induced STAT1 phosphorylation was not affected by the DPI treatment ( Fig . 5B ) . Using a customized BioMark chip , antiviral and inflammatory genes such as type I IFNs ( IFNA2 , IFNB1 ) , pro-inflammatory cytokines and chemokines ( IL1β , CCL5 ) and ISGs ( MX1 , IFITM1/2/3 , OASL , IDO1 , OAS3 , DDX58 ) were inhibited by DPI in a dose dependent manner in DENV-infected cells ( Fig . 5C , upper right box ) . Cytokine release ( IFN-α , TNF-α and IL-6 ) was also impaired in the presence of the antioxidant molecule ( Fig . 5D ) . The use of antioxidant molecules with different modes of action ( S3A Figure ) recapitulated the effect observed with DPI and impaired the induction of antiviral and inflammatory gene expression ( Fig . 5E ) . Importantly , all antioxidant molecules tested in this panel did not affect cell survival , as shown in S3B Figure and S3C Figure . Inhibition of NADPH-oxidase activity using transient knock-down of the catalytic gp91phox subunit also decreased IFIT1 protein expression following de novo DENV infection ( Fig . 5F ) . No increase in DENV RNA accumulation was detected in the presence of the NOX-inhibitor ( 3 µM ) after 24 h of infection ( S4 Figure ) . However , pre-treatment of cells with a higher concentration of DPI ( 30 µM ) led to an increase in DENV viral RNA accumulation in the same conditions ( S4 Figure ) . Importantly , DPI treatment resulted in increased DENV infectivity and replication at 48 h post-infection , as demonstrated by the increased number of DENV-infected cells ( i–ii ) and viral titers ( iii ) ( Fig . 5G ) . The ROS-mediated induction of antiviral and inflammatory genes required live and replicating virus , since formalin-inactivation and UV-inactivation of DENV2 completely suppressed the induction of the immune response ( S5A Figure and S5B Figure ) . Also , DPI inhibited antiviral and inflammatory responses induced by DENV2 strain 16681 ( S5C Figure ) , indicating that ROS-mediated antiviral induction is a common feature of the DENV2 serotype and is not restricted to a specific strain . Collectively , these data demonstrate that DENV infection of Mo-DC triggers an intracellular accumulation of NOX-derived ROS , which are essential for the induction of the antiviral and inflammatory immune responses and the control of DENV infection . DENV-infected DC were clearly apoptotic , based on Annexin-V staining: 27±5 . 15% ( infected ) vs 6 . 69±1% ( control ) at 24 h and 73 . 65±4 . 2% ( infected ) vs 22 . 96±3 . 88% ( control ) at 48 h ( Fig . 6A ) . Upregulation of mRNA levels for pro-apoptotic genes such as BCLX , BIM , and CASP4 upon DENV infection ( Fig . 6B ) was consistent with the transcriptome analysis that identified the induction of apoptosis-associated pathways 24 h after DENV infection ( Fig . 2B and Fig . 3B ) . To assess the release of mitochondrial ROS , cells were stained with mitoSOX , a probe specific for mitochondria-derived ROS; the number of mitoSOX-positive cells increased from 31 . 9±12 . 3% ( uninfected ) to 56 . 8±7 . 9% ( infected ) at 48 h after infection . DiOC6 was also used to determine the loss of mitochondrial potential upon DENV infection: only 7 . 9±0 . 2% uninfected cells were positive , whereas 47 . 2±10 . 2% of infected cells were positive for mitochondrial depolarization . Consistent with the release of mitochondrial ROS and mitochondrial depolarization , intracellular levels of cleaved caspase-3 increased from 5 . 6±1 . 3% in uninfected cells to 28 . 9±1 . 2% in infected cells ( Fig . 6C ) . Regression analysis indicated that the percentage of infected cells at 24 h correlated with the percentage of apoptotic cells at 48 h after infection ( Fig . 6D ( i ) ) . Furthermore , both mitochondrial ROS release and mitochondrial depolarization were statistically associated with apoptosis induction ( Fig . 6D ( ii ) and ( iii ) ) , thus demonstrating DENV-infected Mo-DC undergo mitochondrial-dependent apoptosis . When DC were pre-treated with the NOX-inhibitor DPI , a statistically significant decrease in apoptosis of DENV-infected cells was observed ( ∼80% for DENV only infection compared to ∼52% for DENV+DPI infection ) , indicating that mitochondrial-dependent apoptosis was also dependent , at least in part , on NOX-generated ROS ( Fig . 6E ) . Based on the array data , a key sensor of cellular stress , the transcription factor p53 was strongly activated following DENV infection ( Fig . 2D ) . Inhibition of p53 , using the specific inhibitor pifithrin-α was able to partially suppress DENV-induced apoptosis ( Fig . 6F ) , as did the pan-caspase inhibitor Z-VAD-fmk in Mo-DC ( Fig . 6F ) . Altogether , these results argue that NOX-dependent induction of ROS stimulated p53-regulated mitochondrial and caspase-dependent apoptosis . While infected cells displayed apoptotic markers as described above , uninfected bystander Mo-DC cells did not undergo apoptosis , but rather increased expression of the differentiation and activation markers CD83 and CD86 ( Fig . 6G and S6A–C Figure ) , CD40 , CD80 , CD86 and PD-L1 ( S6D Figure ) . When cells were pre-treated with DPI prior to DENV infection , the number of CD83-positive bystander cells decreased by 2 . 2 fold , compared to non-treated cells ( Fig . 6H ) . To determine if ROS production altered the antiviral response in uninfected bystander cells via cytokine release , conditioned media from DENV-infected DC pre-treated or not with DPI was transferred to uninfected Mo-DC ( Fig . 6I ) . Pre-treatment with conditioned media from DPI-treated DC altered the susceptibility of naïve cells to DENV infection , as shown by the ∼2-fold increase in DENV E protein expression . Altogether , ROS contributes to mitochondria-dependent apoptosis , and also contributes to the maturation of uninfected bystander DC . Defense against sustained antioxidant production and the inhibition of ROS are important protective mechanisms that are regulated by the activation of Nrf2-transcription factor and downstream Nrf2-target genes . Based on the array data ( Fig . 3A ) , Nrf2 target genes such as HMOX-1 , SOD2 , NQO1 , as well as the metallothionein and ferritin families , were all rapidly stimulated by de novo DENV2 infection ( Fig . 7A ) and transient induction of these genes was confirmed by qPCR ( Fig . 7B ) . Levels of heme-oxygenase-1 ( HMOX-1 ) and superoxide dismutase-2 ( SOD-2 ) mRNA were sensitive to the ROS scavenger DPI which abrogated the increase in HMOX-1 and SOD-2 ( Fig . 7C ) . When Nrf2 expression was silenced using Nrf2-specific siRNA ( both at the mRNA ( Fig . 7D ) and at the protein level ( S7A Figure ) , decreases in the mRNA levels of Nrf2-dependent antioxidant genes were also observed ( S7B Figure ) . Functionally , the redox homeostasis was critically affected in Nrf2-deleted Mo-DC , as shown by the ∼3 fold increase in ROS accumulation ( S7C Figure ) . Although silencing of Nrf2 only slightly increased DENV2 RNA accumulation ( Fig . 7E ) and DENV infectivity ( Fig . 7F ) after 24 h of infection , the impairment of Nrf2 expression drastically potentiated oxidative stress response in DENV-infected cells ( Fig . 7G ) . Indeed , a ∼2 fold increase in ROS generation was observed between DENV-infected control- and siRNA-expressing , Nrf2-transfected cells ( Fig . 7G ) for the same number of infected cells ( Fig . 7F ) . Finally , the mRNA levels of genes associated with the antiviral and inflammatory response such as IFIT1 , RSAD2 , DDX58 , CXCL10 and IFNb ( Fig . 7H ) , as well as genes involved in the apoptotic response such as NOXA , BCLX , and RIPK1 ( Fig . 7I ) were all significantly increased . Altogether , these data demonstrate that the Nrf2-regulated antioxidant pathway is stimulated as part of the stress response after DENV infection; the Nrf2-dependent genes regulate the levels of ROS production and thus modulate the immune and apoptotic responses against DENV infection ( Fig . 8 ) . Evaluation of the early host immune response to DENV infection is essential for a complete understanding of the complex immunopathogenesis associated with the development of mild or severe dengue fever in patients . Previous studies have demonstrated that DENV can trigger an innate immune response that includes the release of antiviral and inflammatory cytokines [50] , [53] , [54] , while other studies demonstrate the ability of DENV to antagonize the induction of innate responses via cleavage of the endoplasmic reticulum adaptor STING [51] , [55] . To uncover novel regulatory pathways involved in DENV infection of Mo-DC , we have for the first time used a transcriptome-wide expression analysis , coupled with biochemical dissection , to investigate the early host response to DENV infection in primary human dendritic cells - an important pool of cells infected early in vivo after the bite of the mosquito Aedes aegypti . Here , we demonstrate that: 1 ) DENV preferentially infected myeloid cells as they differentiated in vitro to mature Mo-DC; 2 ) DENV2 infection triggered antiviral , inflammatory , and oxidative stress pathways with distinct kinetics; 3 ) DENV2 infection generated a NOX-dependent intracellular accumulation of ROS; 4 ) ROS production mediated activation of the IRF3/STAT1- and NF-κB-mediated innate immune responses; 5 ) ROS production mediated p53 mitochondrial-dependent apoptosis and contributed to bystander Mo-DC maturation/activation; and 6 ) Nrf2-regulated target genes limited the oxidative stress response , and ultimately modulated ROS-induced immune and apoptotic responses . These results highlight a requirement for the oxidative stress response in the generation of the host innate immune response to DENV infection . Activation of the NADPH-oxidase ( NOX ) complex and generation of reactive oxygen species ( ROS ) has been described for several viral infections , including hepatitis C virus ( HCV ) , Rhinovirus , and HIV [56]–[58] . We demonstrate that DENV infection also activates NOX-dependent ROS production in Mo-DC . In some infection models , viral proteins such as Nef and Tat for HIV and NS3 for HCV were shown to specifically stimulate the NOX complex [59]–[61] . NOX activity was also regulated by spleen tyrosine kinase ( Syk ) -mediated phosphorylation of the NOX p47phox subunit [62]; Syk kinase is downstream of the surface receptor CLEC5A , which was shown to promote inflammasome activation and inflammatory cytokine release in DENV infection [63] , [64] . Importantly , TLR3 , a receptor critically involved in RNA sensing , was recently shown to stimulate NOX-dependent ROS production that was required for NF-κB , IRF3 and STAT1 activation in murine macrophages in response to the synthetic dsRNA Poly ( I:C ) [65] . Furthermore , exogenous addition of oxidative stress potentiated the TLR3 response to dsRNA in airway epithelial cells [66] . Finally , the specific TLR7 agonist imiquimod also elevated basal superoxide production through enhanced NOX2 activity in macrophages [67] . Further studies are now required to determine the exact mechanism ( s ) involved in DENV-induced NOX-dependent ROS production in human Mo-DC . ROS were long considered as toxic , microbe-induced by-products involved in the killing of pathogens [18]; however , their function as second messengers that regulate immune signaling suggests a much broader role in host defense against viruses [19]–[23] . ROS production was in fact required to trigger the antiviral and inflammatory responses to DENV infection in DC , and was confirmed by both chemical and genetic inhibition of the NOX complex . Blockade of NOX activation or ROS production inhibited antiviral and inflammatory responses , including the IRF3/STAT1 antiviral axis and the NF-κB inflammatory pathway ( Fig . 4 ) . The IRF3 pathway has previously been demonstrated to be regulated by oxidative stress variations . Indeed , the expression level of the non-canonical IKK-like kinase , IKKε , is itsef NOX-regulated and participated in the immune response induced by the respiratory syncytial virus ( RSV ) [68] . NOX-derived ROS were also shown to activate the RIG-I/MAVS/IRF3 antiviral axis in epithelial cells , and were required to maintain the constitutive level of MAVS expression [22] . In contrast , statistical changes in MAVS or IKKε expression following NOX inhibition in primary DENV-infected DC were not observed in this study ( S8A–C Figure ) , suggesting that DENV-induced ROS may regulate host response via post-translational modification of proteins involved in antiviral signaling , as was described previously for S-glutathionylation of TRAF3 and TRAF6 [19] . Other non-infectious biological processes such as impairment of autophagy also support the idea that oxidative stress modulates the sensitivity to antiviral signaling . Indeed , blocking of autophagy allows for oxidative stress accumulation through defective mitochondria and leads to the amplification of RLR signaling [69] . Altogether , these studies cumulatively highlight the complexity of ROS involvement in the stimulation of antiviral responses and argues that the innate immune response integrates both viral RNA sensing and detection of homeostatic perturbations to coordinate an appropriate host response . The Nrf2-mediated antioxidant response was one of the top differentially regulated pathways early after DENV infection , resulting in the expression of many cytoprotective enzymes such as HMOX-1 , SOD2 , NQO1 , GCLC and GCLM , that function together to maintain an appropriate redox status , and thus protect cells from ROS-induced damage [24]–[26] . The importance of Nrf2 activity during viral pathogenesis was demonstrated recently in a study showing that Marburg virus ( MARV ) hijacked the Nrf2 pathway leading to a persistent activation of Nrf2-dependent antioxidant and cytoprotective genes , temporarily blocking cell death of MARV-infected cells , and thus facilitating viral proliferation [70] , [71] . Another study involving Nrf2 knockout mice demonstrated that mice challenged with Respiratory Syncytial Virus ( RSV ) or influenza had both higher viral replication and increased inflammatory responses and injury in their lungs [34] , [72] , [73] . Consistent with these observations , genetic silencing of Nrf2 in primary Mo-DC deregulated intracellular redox homeostasis and led to increased inflammatory and apoptotic responses . The importance of Nrf2 in DENV pathogenesis was more recently illustrated in a study of DENV-infected HepG2 xenografted SCID mice treated with the tripeptide glutathione ( GSH ) , an anti-oxidant whose intracellular levels are also regulated by Nrf2 . GSH prevented DENV-induced oxidative stress and liver injury by inhibiting pro-inflammatory cytokine production [40] . The same observation was made in vitro where treatment of DENV-infected HepG2 cells with GSH prevented the increase in ROS accumulation . Administration of antioxidant molecules such as GSH or other Nrf2 activators may be a novel strategy to treat and limit symptoms associated with DENV disease . DC are potent antigen presenting cells that , after sensing of pathogens , migrate from peripheral tissues to the lymph nodes and drive CD4+ and CD8+ T cell responses [74] . Here , we demonstrate that DENV-infected Mo-DC undergo mitochondria-dependent apoptosis , driven by an increase in ROS and facilitated by p53 transcription factor . Uninfected bystander DC , on the other hand , are not killed but rather mature to DC expressing maturation and activation markers , as previously reported [50] . ROS exposure and the immune response generated in infected cells , rendered the bystander uninfected DC less susceptible to DENV replication , most probably as a consequence of released soluble factors from infected cells . Meanwhile inhibition of ROS with DPI decreased expression of maturation markers and increased susceptibility to DENV infection . Thus , ROS production may not only impact infected cells but also affect DC maturation indirectly , by altering the cytokine milieu of uninfected bystander DC; in turn DC maturation in context of DENV infection may alter priming of the T cell response . There are no diagnostic markers presently available that will determine whether a DENV-infected patient will develop a mild illness or progress to a more severe dengue fever , associated with DENV-induced hemorrhagic fever or shock syndrome . However , markers of oxidative stress have been reported in patients with severe DENV infection , suggesting a relationship between oxidative stress and viral pathogenesis in patients [41] , [43] , [44] . Soundravally et al demonstrated an association between the induction of proinflammatory cytokines and the levels of lipid peroxidation in patients [43] . Earlier studies also demonstrated that DENV-infected Mo-DC overproduce matrix metalloproteinase-9 ( MMP-9 ) , a result also suggested by our array analysis ( Fig . 2B ) . The induction of MMP-9 by DENV-infected Mo-DC enhanced endothelial permeability in vitro and was proposed as a marker for disease severity [75] . Interestingly , increased oxidative species through NADPH-oxidase activation or upon TLR3 ligation were also shown to regulate MMP-9 expression [30] , [76] , [77] . Furthermore , mice lacking the p47 NADPH-oxidase subunit displayed a reduction in hemorrhage development and disease severity after DENV infection [78] . Altogether , these findings highlight a key role for NADPH-oxidase in the oxidative stress-related pathology of DENV , and suggest that both NADPH-oxidase activity , ROS levels or associated ROS-induced molecules may be useful biomarkers to predict disease severity . In conclusion , DENV infection of DC induces intracellular ROS levels that regulate the magnitude of the activation of innate antiviral immune responses and stimulate apoptosis . Parallel activation of antioxidant pathways regulated by Nrf2 also contributes to the regulatory control of antiviral and apoptotic responses by maintaining redox homeostasis . ROS were identified as an essential component of the host response to DENV infection; a further understanding of the molecular details underlying the biological targets of ROS during DENV infection may facilitate identification of novel treatment strategies for dengue-associated diseases . Human peripheral blood mononuclear cells ( PBMC ) were isolated from buffy coats of healthy , seronegative volunteers in a study approved by the IRB and by the VGTI-FL Institutional Biosafety Committee ( 2011-6-JH1 ) . Written informed consent approved by the VGTI-FL Inc . ethics review board ( FWA#161 ) was provided to study participants . Research conformed to ethical guidelines established by the ethics committee of the OHSU VGTI and Martin Health System . Briefly , PBMC were isolated from freshly collected blood using the Ficoll-Paque PLUS medium ( GE Healthcare Bio ) as per manufacturer's instructions . CD14+ monocytes were isolated by positive selection using CD14 microbeads and a magnetic cells separator as per kit instructions ( Miltenyi Biotech ) . Purified CD14+ monocytes were cultured for 7 days either in six-well plates ( 1 . 5×106 cells ) or 100 mm dishes ( 15×106 cells ) in 2 mL ( 6-well plate ) or 10 mL ( 100 mm dish ) , respectively of complete Mo-DC differentiation medium ( Miltenyi Biotech . ) . On day 3 , the medium was replenished with fresh medium . Purity of CD14− CD1a+ DC-SIGN high moDC was typically >80% . DENV serotype 2 ( DENV2 ) strain New Guinea C ( DENV NGC ) or DENV2 strain 16681 were produced on C6/36 cells and quantified on Vero cells as previously reported [79] . In control experiments , virus was inactivated using formalin 0 . 05% in PBS at 22°C or UV-inactivated for 1 h on ice . For infection , except where indicated , immature Mo-DC were infected at a multiplicity of infection of 20 in a small volume of medium without FBS for 3 hours at 37°C . Following adsorption , cells were washed twice in serum-free medium and incubated with complete medium containing cytokines prior to analysis . Mock-infected Mo-DC were treated according to the same procedure . All procedures with live DENV2 were performed in a Biosafety level 2+ facility at the Vaccine and Gene Therapy Institute of Florida . The DENV2 kinetics microarray experiment was performed as a single experiment on Mo-DC derived from 3 independent healthy donors . Mo-DC were infected at an MOI of 20 as described above and cells were collected at various times and lysed using RLT lysis buffer ( Qiagen ) for RNA extraction . Briefly , RNA were extracted using RNeasy Micro Kits ( Qiagen ) . The quantity and the quality of the RNA were validated using a NanoDrop 2000c ( Thermo Fisher ) . Samples were then amplified using Illumina TotalPrep RNA amplification kits ( Ambion ) . The microarray analysis was conducted using 750 ng of biotinylated complementary RNA hybridized to HumanHT-12_V4 BeadChips ( Illumina ) at 58°C for 20 hours . The data were collected with Illumina GenomeStudio software . First , arrays displaying unusually low median intensity , low variability , or low correlation relative to the bulk of the arrays were discarded from the rest of the analysis . Quantile normalization , followed by a log2 transformation using the Bioconductor package LIMMA was applied to process microarrays . Missing values were imputed with the R package ( http://cran . r-project . org/web/packages/impute/index . html ) . In order to identify differentially expressed genes between uninfected and infected samples , the LIMMA package from Bioconductor was used . For data mining and functional analyses , genes that satisfied a p value ( <0 . 05 ) with ≥1 . 3 fold change ( up or down ) were selected . Probes that do not map to annotated RefSeq genes and control probes were removed . The expected proportions of false positives ( FDR ) were estimated from the unadjusted p value using the Benjamini and Hochberg method . All network analysis was done with Ingenuity Pathway Analysis ( IPA: Ingenuity systems ) . The differentially expressed genes selected based on above criteria were mapped to the ingenuity pathway knowledge base with different colors . The significance of the association between the dataset and the canonical pathway was measured in two ways: ( 1 ) A ratio of the number of genes from the dataset that map to the pathway divided by the total number of genes that map to the canonical pathway was displayed; ( 2 ) by over-representation analysis Fisher's exact test was used to calculate a p-value determining the probability that the association between the genes in the dataset and the canonical pathway is explained by chance alone . The pathways were ranked with −log p-values . The pathway enrichment and network analyses were done using Ingenuity Pathway Analysis ( IPA: Ingenuity systems ) . The differentially expressed genes were further selected based on p-value ( 0 . 001 ) and subsequently were mapped to the Ingenuity Pathway knowledgebase . The significance of the association between the dataset and the canonical pathway was measured in two ways: ( 1 ) A ratio of the number of genes from the dataset that map to the pathway divided by the total number of genes that map to the canonical pathway was displayed; ( 2 ) by overrepresentation analysis: Fisher's exact test was used to calculate a p-value determining the probability that the association between the genes in the dataset and the canonical pathway is explained by chance alone . The top ranking pathways were selected by ranking −log p-values . The selected pathways were then represented as networks by grouping genes involved in a pathway as a cloud by retaining the relationships represented as edges . Manual curation was further employed to annotate selected pathways by adding genes and their relationships to other genes in networks that are not depicted by Ingenuity . Subsequently , genes were color-coded based on the fold-changes ( green – downregulated; red – upregulated ) . Heatmaps of these genes were generated to display both fold-changes and membership of genes in one or more pathways; these heatmaps were created using the R statistical computing environment . The data have been deposited in the NCBI Gene Expression Omnibus ( GEO Series accession number GSE58278 ) . Total RNA was isolated from cells using RNeasy Kit ( Qiagen ) as per manufacturer's instructions . RNA was reverse transcribed using the SuperScript VILO cDNA synthesis kit according to manufacturer's instructions ( Invitrogen ) . PCR primers were designed using Roche's Universal Probe Library Assay Design Center ( www . universalprobelibrary . com ) . Quantitative RT-PCR was performed on a LightCycler 480 system using LightCycler 480 Probes Master ( Roche ) . The N-fold differential expression of mRNA gene expression was expressed as 2−ΔΔCt . The DENV2 kinetics BioMark experiment was performed with Mo-DC derived from 3 independent healthy donors . Total RNA and cDNA were prepared as described above . Intron-spanning PCR primers were designed using Roche's Universal Probe Library Assay Design Center ( www . universalprobelibrary . com ) and obtained from the Integrated DNA Technology company ( USA ) ( S1 Table ) . cDNA along with the entire pool of primers were pre-amplified for 14 cycles using TaqMan PreAmp Master Mix as per manufacturer's protocol ( Applied Biosystems ) . cDNA was treated with Exonuclease I ( New England Biolabs ) . cDNA samples were prepared with 2X FastStart TaqMan Probe Master ( Roche ) , GE sample loading buffer ( Fluidigm ) and Taq Polymerase ( Invitrogen ) . Assays were prepared with 2X assay loading reagent ( Fluidigm ) , primers ( IDT ) and probes ( Roche ) . Samples and assays were loaded in their appropriate inlets on a 48 . 48 BioMark chip . The chip was run on the BioMark HD System ( Fluidigm ) , which enabled quantitative measurement of up to 48 different mRNAs in 48 samples under identical reaction conditions . Runs were 40 cycles . Raw Ct values were calculated by the real time PCR analysis software ( Fluidigm ) and software-designated failed reactions were discarded from analysis . All data are presented as a relative quantification with efficiency correction based on the relative expression of target gene versus the geomean of ( GAPDH+Actin+β2 microglobulin ) as the invariant control . The N-fold differential expression of mRNA gene samples was expressed as 2−ΔΔCt . The heatmaps were produced with the following package; pheatmap: Pretty Heatmaps . R package version 0 . 7 . 7 http://CRAN . R-project . org/package=pheatmap . Gene level expression is shown as −ΔΔCt or gene-wise standardized expression ( Z score ) . The sequences of primers used as well as their complementary probes are listed in the S1 Table . Protein lysates ( 20 to 40 µg ) from Mo-DC were subjected to western blot analysis . Membranes were probed with primary antibodies: anti-pIRF3 at Ser 396 ( EMD Millipore ) , anti-IRF3 ( IBL , Japan ) , anti-IRF7 ( EMD Millipore ) , anti-RIG-I ( EMD Millipore ) , anti-IFIT1 ( Thermo Fisher Scientic ) , anti pSTAT1 at Tyr701 ( Cell Signaling ) , anti-STAT1 ( Cell Signaling ) , anti–pIκBα at Ser32 ( Cell Signaling ) , anti-IκBα ( Cell Signaling ) , anti-p47phox at Ser359 ( AssayBioTech ) , anti-p47phox ( Sigma Aldrich ) , anti-STING ( Cell Signaling ) , anti-gp91phox ( Santacruz Biotechnology ) , anti-Nrf2 ( Cell Signaling ) , anti-β-actin ( Odyssey , USA ) . Antibody signals were detected by immunofluorescence using the IRDye 800CW and IRDye 680RD secondary antibodies ( Odyssey , USA ) and the LI-COR imager ( Odyssey , USA ) . Protein expression levels were determined and normalized to β-actin using the ImageJ software ( National Institutes of Health , Bethesda , USA ) . Cytokine production was evaluated in the supernatants of DENV2-infected Mo-DC using a BD CBA flex set ( IFN-α , TNF-α , IL-6 , IL-1β , IL-10 , IL-12p70 ) as per manufacturer's recommendations . The BD FACS Array Bioanalyzer was used to process the samples and perform the analysis . Two different methods were used for siRNA transfection of Mo-DC . A total of 3×106 Mo-DC were transfected in a cuvette in the presence of 100 pmol of control ( sc-37007 ) , Nrf2 ( sc-37030 ) or gp91-phox ( sc-35503 ) human siRNA ( Santa Cruz Biotechonlogy , USA ) using the Amaxa 4D-Nucleofector Technology for 48 h . The Amaxa P3 Primary Cell 4D Nucleofector X Kit was used with the electroporation program EA-100 . Another method based on a transfection reagent was alternatively used to transfect lower amount of cells . A total of 4×105 Mo-DC was transfected in 24-well plates in the presence of 40 pmol of control ( sc-37007 ) , Nrf2 ( sc-37030 ) , gp91-phox ( sc-35503 ) human siRNA ( Santa Cruz Biotechonlogy , USA ) using 6 µL of HiPerfect Transfection Reagent ( Qiagen ) for 48 h . Values were expressed as the mean ± SEM and statistical analysis , except where indicated , was performed with Microsoft Excel or Graph Pad Prism , using an unpaired , two-tailed Student's t test to determine significance . P values of less than 0 . 05 were considered statistically significant , *** , p<0 . 001; ** , p<0 . 01 , and * , p<0 . 05 .
Dengue virus ( DENV ) , the leading arthropod-borne viral infection in the world , represents a major human health concern with a global at risk population of over 3 billion people . Currently , there are no antivirals or vaccines available to treat patients with dengue fever , nor is it possible to predict which patients will progress to life-threatening severe dengue fever . Markers associated with oxidative stress responses have been reported in patients with severe DENV infection , suggesting a relationship between oxidative stress and viral pathogenesis . In order to uncover biological processes that determine the outcome of disease in patients , we utilized human dendritic cells , the primary target of DENV infection , in an in vitro model . Transcriptional analysis of pathways activated upon de novo DENV infection revealed a major role for cellular oxidative stress in the induction of antiviral , inflammatory , and cell death responses . We also demonstrated that antioxidant mechanisms play a critical role in controlling antiviral and cell death responses to the virus , acting as feedback regulators of the oxidative stress response . This report highlights the importance of oxidative stress responses in the outcome of DENV infection , and identifies this pathway as a potential new entry-point for treating dengue-associated diseases .
You are an expert at summarizing long articles. Proceed to summarize the following text: Hearing and vestibular function depend on mechanosensory staircase collections of hair cell stereocilia , which are produced from microvillus-like precursors as their parallel actin bundle scaffolds increase in diameter and elongate or shorten . Hair cell stereocilia contain multiple classes of actin-bundling protein , but little is known about what each class contributes . To investigate the roles of the espin class of actin-bundling protein , we used a genetic approach that benefited from a judicious selection of mouse background strain and an examination of the effects of heterozygosity . A congenic jerker mouse line was prepared by repeated backcrossing into the inbred CBA/CaJ strain , which is known for excellent hearing and minimal age-related hearing loss . We compared stereocilia in wild-type CBA/CaJ mice , jerker homozygotes that lack espin proteins owing to a frameshift mutation in the espin gene , and jerker heterozygotes that contain reduced espin levels . The lack of espins radically impaired stereociliary morphogenesis , resulting in stereocilia that were abnormally thin and short , with reduced differential elongation to form a staircase . Mean stereociliary diameter did not increase beyond ∼0 . 10–0 . 14 µm , making stereocilia ∼30%–60% thinner than wild type and suggesting that they contained ∼50%–85% fewer actin filaments . These characteristics indicate a requirement for espins in the appositional growth and differential elongation of the stereociliary parallel actin bundle and fit the known biological activities of espins in vitro and in transfected cells . The stereocilia of jerker heterozygotes showed a transient proximal-distal tapering suggestive of haploinsufficiency and a slowing of morphogenesis that revealed previously unrecognized assembly steps and intermediates . The lack of espins also led to a region-dependent degeneration of stereocilia involving shortening and collapse . We conclude that the espin actin-bundling proteins are required for the assembly and stabilization of the stereociliary parallel actin bundle . A stunning example of cytoskeleton-mediated morphogenesis is the formation of hair cell stereocilia , which act as primary mechanosensory detectors in the auditory and vestibular systems [1] , [2] . Stereocilia are fingerlike projections that contain a specialized cytoskeletal element , the parallel actin bundle [3] , aligned axially at their core . The parallel actin bundle , which consists of hexagonally packed unidirectional actin filaments cross-linked by actin-bundling proteins to produce a regular ∼12–13 nm ( center-to-center ) interfilament spacing , exhibits the properties of a molecular scaffold that sets the dimensions of stereocilia and influences their mechanical properties [3]–[5] . During development , highly precise staircase collections of stereocilia are produced from microvillus-like precursors as their parallel actin bundle scaffolds selectively undergo an increase in diameter , through the addition of more actin filaments to the parallel actin bundle , and their constituent actin filaments elongate or shorten [4] , [5] . The plasma membrane of the stereocilium remains in close proximity to the parallel actin bundle throughout morphogenesis , so that increases in stereociliary diameter reflect increases in the number of actin filaments in the parallel actin bundle and changes in stereociliary length correspond to changes in the length of the actin filaments in the parallel actin bundle [6] . The dimensions of stereocilia vary in a remarkably regular way , not only within a given collection , but also according to hair cell type and position in the cochlea or vestibular system [7]–[10] . This attests to an impressive degree of spatial precision in actin-cytoskeletal regulation . A growing list of deaf mutant mice with malformed stereocilia demonstrates the importance of stereociliary morphogenesis to hair cell mechanoelectrical signal transduction [11] , [12] . The modifications in parallel actin bundle dimensions that underlie stereociliary morphogenesis are presently thought to involve actin-bundling proteins [13]–[17] , actin-capping proteins [18]–[20] , unconventional myosin motors and their cargoes [21]–[23] . Although multiple classes of actin-bundling protein have been identified in hair cell stereocilia , relatively little is known about what each class contributes [13]–[17] . One class of actin-bundling protein implicated in hair cell stereocilia is the espin family [24] . Discovered originally in Sertoli cell junctional plaques [25] , espins are encoded by a single gene , but are produced in multiple isoforms [24]–[27] . Espins bind to and cross-link actin filaments into parallel actin bundles in vitro with high affinity and in a Ca2+-insensitive manner [28]–[30] , exert a potent cooperative effect on the twist of actin filaments in parallel actin bundles [31] and elicit a dramatic , concentration-dependent elongation of parallel actin bundles in cells [27] , [32]–[34] . In hair cell stereocilia , espin antibody staining is detected along the length of the parallel actin bundle in the body of the stereocilium , both in adulthood and during morphogenesis , but not in the rootlet [14] , [16] , [32] , [35] , [36] . Espin expression and accumulation in stereocilia are hallmarks of hair cell differentiation in situ and by stem cells in culture [35] , [37] , [38] . In addition , the espin gene is the target of mutations associated with deafness and vestibular dysfunction , including the jerker mutation in mice [14] , [39] , [40] . The jerker mutation is a spontaneous mutation noted in the collection of a mouse fancier and first described in 1941 [41]–[43] . Homozygous jerker mice exhibit the stereotyped shaker-waltzer behavior indicative of hair cell defects , including deafness , circling , head tossing and hyperactivity . In 2000 , the jerker mutation was shown to be a frameshift mutation in the espin gene ( c . 2426delG; Espnje ) on mouse chromosome 4 [14] , and this was verified by independent physical mapping studies [44] . Because homozygous jerker mice lack espin proteins and jerker heterozygotes contain approximately half-normal espin levels [14] , the examination of jerker mice promises to reveal a great deal about the functions of espins . Earlier studies examining stereociliary ultrastructure in inbred jerker mice with uncharacterized genetic backgrounds detected the degeneration of stereocilia and loss of hair cells in jerker homozygotes [33] , [45]–[47] . Although the results were encouraging , these earlier studies did not compare wild-type mice of the same genetic background and either were not systematic or examined only a single hair cell type and inner ear location . In addition , the effects on the vestibular system [45] were not investigated in detail . Importantly , the detection of a related group of degenerative changes in jerker heterozygotes with later onset [45]–[48] was difficult to reconcile with the presumed recessive nature of the jerker mutation . This naturally raised concerns about possible complications owing to strain-specific genetic modifier effects or age-related hearing loss , which have been detected in a number of mouse strains [49] . A recent proteomic analysis of stereocilia detected espins at lower levels than some other actin-bundling proteins [17] , raising additional questions about the roles of espins . To help elucidate the roles of espins in hair cell stereocilia , we have carried out a systematic scanning electron microscopic study of hair cell stereocilia examining a congenic jerker mouse line we prepared using the CBA/CaJ inbred strain . The CBA/CaJ strain was chosen for the genetic background because CBA/CaJ mice exhibit excellent hearing and minimal age-related hearing loss [50] . Unlike earlier studies , we compared jerker homozygotes and heterozygotes to wild-type mice of the same genetic background , analyzed hair cells from multiple inner ear locations in the cochlea and vestibular system , examined specimens without metal coating , measured stereociliary width and length and focused on the critical period of early postnatal development . We determined that the absence of espin proteins drastically alters stereociliary morphogenesis , resulting in marked decreases in stereocilium diameter , length and stability . In addition , we uncovered an informative group of transient developmental defects in jerker heterozygotes , which contain reduced espin levels . Examination of the stereocilia of vestibular hair cells in +/je mice revealed an unexpected developmental defect: transient tapering . As shown in Figure 11C , the width of stereocilia on the extrastriolar hair cells of +/je mice ( dashed line ) was intermediate to those in je/je and +/+ mice at P0 , but eventually increased to become highly similar to that in +/+ mice by P20 . What is remarkable is that this increase in stereociliary width was gradual and took place first in the proximal part of stereocilia and later in the distal part , resulting in stereocilia that were transiently tapered from P0 through P10 ( Figure 11C and Figure 12A ) . A similar gradual tapering of stereocilia was observed in the peripheral zone of the cristae ampullares in +/je mice at P5 ( Figure 12D ) . Stereociliary tapering , but of a more extreme and abrupt nature , was also seen in the central zone of the cristae ampullares in +/je mice at P0 , P5 and P10 ( P5 shown in Figure 12B and 12C ) . The distal segment of these stereocilia was often dramatically thinner than the proximal segment , giving the appearance of a candle with a wick ( Figure 12B and 12C ) . Notably , up to a length of ∼4 µm , the width of the proximal segment of these stereocilia was similar to that in +/+ mice ( compare Figure 12B and Figure 7A ) . By P20 , few of these extremely thin distal segments remained ( Figure 12F ) , suggesting that most had grown longer and widened sufficiently to match their proximal segments . Also evident in the central zone of P5 +/je mice were stereocilia with eccentric protruding distal tips , which were suggestive of intermediates caught in a relatively early stage of additional elongation ( Figure 12E , arrowheads ) . A close scrutiny of inner hair cells also revealed the transient tapering of stereocilia in +/je mice . This tapering was especially evident for stereocilia in the tallest row in the apical region of the cochlea at P5 ( Figure 13D ) , but was also detected in the middle region ( Figure 13B ) . The tapering was not detected in +/+ mice ( Figure 13A , 13C , 13E and 13G ) . In the basal and middle regions of the cochlea , the tapering was no longer detected at P10 ( Figure 13F ) , but it was still partially evident in the apical region ( Figure 13H ) . By P20 , the tapering of the tallest stereocilia was only observed in the extreme apical region of the cochlea ( >95% from cochlear base ) . In this region , a partial tapering was still observed at P20 ( Figure 13J and 13K ) and even at 8 months of age ( Figure 13L ) , but not in +/+ mice ( Figure 13I ) . The most consistent morphogenesis defect we observed in je/je mice was the failure of stereocilia to increase in mean diameter beyond ∼0 . 10–0 . 14 µm . Because stereocilia grow to different diameters in +/+ mice , this made the stereocilia of je/je mice ∼50–60% thinner than wild type for inner hair cells and utricular hair cells and ∼30–40% thinner than wild type for outer hair cells . The increase in stereocilium diameter during morphogenesis is presumed to reflect the appositional growth of the parallel actin bundle scaffold at the core , in which additional actin filaments are added at the periphery of the existing parallel actin bundle [4]–[6] . A 50–60% decrease in stereocilium diameter translates into a 75–84% decrease in cross-sectional area . Assuming that the actin filaments in the abnormally thin projections maintain the standard ∼12–13 nm parallel actin bundle interfilament spacing [30] , [31] , the parallel actin bundle scaffold of je/je mouse hair cell stereocilia could contain as little as 16–25% of the number of actin filaments found in the stereocilia of +/+ mice . The abnormally thin utricular stereocilia in P20 je/je mice labeled with fluorescent phalloidin ( Figure 1G ) , suggesting that they contain actin filaments , a conclusion that has been confirmed by transmission electron microscopy ( GS and JRB , unpublished results ) . Definitive assessments of the numbers , continuity and packing of these actin filaments will come from systematic serial-section analyses . The present examination of stereociliary dimensions suggests that espin proteins are required to increase the diameter of the stereociliary parallel actin bundle beyond a limiting value . A role in the appositional growth of the stereociliary parallel actin bundle would be entirely consistent with the espins' activity as actin-bundling proteins that can efficiently cross-link actin filaments into parallel actin bundles in vitro [28]–[30] and with the immunocytochemical localization of espins to hair cell stereocilia throughout the process of stereociliary morphogenesis [35] , [37] . Defects in the appositional growth of the parallel actin bundle could also explain the transient stereociliary tapering we discovered in young +/je mice . This novel phenotype is most likely a sign of haploinsufficiency , in which espin protein levels are limiting . The transient tapering is consistent with a slowing in the appositional growth of the stereociliary parallel actin bundle , and we propose that this slowing revealed some exclusive views of assembly intermediates and steps that are difficult to resolve in +/+ mice . For example , the direction of the taper and its subsequent filling suggest that the appositional growth of the parallel actin bundle proceeds in a proximal-to-distal direction and , thus , likely involves the barbed-end elongation of shorter actin filaments positioned in the peripheral layers of a wider , more proximal segment of the core bundle . How could reducing espin levels by approximately one-half slow the appositional growth of the parallel actin bundle ? Beyond cross-linking actin filaments into parallel actin bundles [28]–[30] , espins cause a concentration-dependent , barbed-end elongation of microvillus-type parallel actin bundles in transfected epithelial cells [32] , [34] . Like espin-mediated actin filament bundling [28] , the 116-amino acid espin carboxy-terminal actin-bundling module is necessary and sufficient for this barbed-end elongation activity , and putative F-actin-binding sites located at either end of the module are required [32] . Thus , one possibility is that wild-type levels of espin cross-links are needed both to cause the barbed-end elongation of shorter actin filaments situated at the periphery of the parallel actin bundle and to attach the newly elongated filament segments to the bundle . In addition to these activities that emphasize the role of the espin actin-bundling module , espins can bind monomeric actin via their WH2 domain [27] , [32] , [57] and can elicit WH2 domain-dependent parallel actin bundle formation when targeted to specific locations – centrosomes [57] , nucleoli [57] or filopodial tips [23] – in transfected cells . Thus , it is possible that wild-type espin levels are also needed to deliver polymerizable actin monomer into the stereocilium and to sustain the actin polymerization reactions needed to increase the number and length of actin filaments at the periphery of the parallel actin bundle . A slowing of stereociliary morphogenesis in +/je mice might also account for the eccentric protruding distal tips of stereocilia we observed in the central zone of cristae ampullares at P5 . These structures , which may represent pioneering elongation intermediates of reduced diameter , are a potential source of the abruptly tapered distal segments of stereocilia ( “wicks” ) we observed as assembly intermediates in the central zone of early postnatal +/je mice . Thus , with the slowing brought about by reduced espin levels , the morphogenesis of these long vestibular stereocilia beyond the immature stage ( Figure 5A ) appears resolvable into two additional phases of elongation , each requiring an increase in stereociliary diameter: a phase A that produces stereocilia of relatively similar intermediate length and a phase B involving differential elongation to final length . In the vestibular hair cells of je/je mice , stereociliary morphogenesis appears to stall when the increase in diameter associated with phase A does not proceed to completion . Unlike the situation in je/je mice , stereocilia in +/je mice can largely recover from having reduced espin levels . In fact , we found aged +/je mice to be remarkably similar to +/+ mice in stereociliary morphology , hair cell abundance and auditory brainstem response thresholds . Thus , we conclude that the jerker mutation is indeed recessive and that the stereociliary degeneration , extensive hair cell loss and deafness observed previously by Sjöström and Anniko [45]–[48] in aged jerker heterozygotes of an uncharacterized genetic background are attributable to another influence , e . g . , a genetic modifier , age-related hearing loss or disease . Although multiple studies have suggested a connection between espins and the elongation of stereocilia [23] , [32]–[35] , our results indicate that the relationship between espins and stereociliary length is complicated . It is true that , in general , we found the stereocilia of je/je mice to be significantly shorter than their counterparts in +/+ mice . However , we determined that this seemingly generic response to a lack of espins actually reflects a complicated mixture of defects in stereociliary morphogenesis , affecting width and length , together with defects in stereociliary stability , which vary according to inner ear region . For example , the early-stage graded elongation of stereociliary precursors appears remarkably similar in the presence and absence of espins . Cochlear stereocilia are shorter in je/je mice primarily because they subsequently shorten and disappear . Our examination of extrastriolar hair cells in the utricular macula suggests that , for long vestibular stereocilia , it is elongation phase B , involving the final differential elongation , that is markedly attenuated in je/je mice . Although espins could contribute directly to this differential elongation through the parallel actin bundle elongation activity mentioned above [32] , it is also possible that they contribute in a more indirect manner . For example , a certain threshold in the number of espin cross-links , in actin filament twist or in parallel actin bundle diameter might need to be attained before additional stereocilium elongation can proceed via mechanisms involving other proteins . Construction of a taller stereocilium might simply require a broader base with suitable cross-links . Importantly , stereocilia in the vestibular system of je/je mice showed pronounced elongation beyond the precursor stage , e . g . , up to lengths of ∼6 µm in utricular maculae and ∼8 µm in cristae . Thus , clearly substantial stereociliary elongation can take place in the absence of espins . Superimposed on an inability of stereocilia to widen and elongate fully , we observed major defects in stereocilium stability in je/je mice . This was especially noticeable in the cochlea , where stereocilia rapidly shortened and disappeared , often so fast as to obscure defects in morphogenesis . A qualitatively different and slower form of stereociliary collapse and resorption was evident in the striolar/central regions of the vestibular system in je/je mice . Thus , espins are required to avoid these types of degenerative change , which are suggestive of mechanical weakness , fragmentation and/or depolymerization of the stereocilium's parallel actin bundle scaffold . A likely possibility is that the espins' high-affinity , Ca2+-resistant cross-links are needed to stabilize the parallel actin bundle against depolymerization , fragmentation and mechanical insult . Actin-bundling proteins are known to retard actin depolymerization in vitro [58] , [59] , and an espin-mediated increase in the number of actin filaments in the parallel actin bundle would be expected to make the bundle more sturdy [60] . In a related way , the presence or absence of espin cross-links could determine why some parallel actin bundle-containing projections ( stereocilia ) are spared while others ( microvilli ) are cleared from the apical surface of the hair cell during stereociliary morphogenesis in +/+ mice . The occasional loss of a short-row stereocilium from outer hair cells that we observed in +/je mice could reflect a similar , yet localized parallel actin bundle disassembly process initiated when espin levels drop below a critical threshold . Higher levels of immunolabeling for espins have been detected at suspected sites of stereociliary damage [61] , raising the intriguing possibility that espins may also play important roles in parallel actin bundle repair . Thus , it is conceivable that the various types of stereociliary degeneration we observed in je/je mice are extreme manifestations of faulty parallel actin bundle repair . Remarkably , stereocilia in the extrastriolar/peripheral regions of the je/je mouse vestibular system , although abnormally thin and short , resisted shortening and collapse for much longer periods , even though we showed that these stereocilia normally contain espins . Since the striolar/central and extrastriolar/peripheral regions contain similar numbers of type I and type II hair cells [55] , [62] , we conclude that the pattern of stereociliary degeneration in the vestibular system of je/je mice varies primarily according to region instead of vestibular hair cell type . These differences could be tied to known regional differences in parameters such as hair cell birth date [63] , afferent response characteristics [64] , hair cell physiology [65] or interstereociliary links [66] , but could also reflect regional differences in stereociliary actin dynamics or actin-cytoskeletal proteins . Parallel actin bundles in cells typically contain multiple classes of actin-bundling protein [3] . Accordingly , espins are not the only actin-bundling proteins in hair cell stereocilia . The other actin-bundling proteins believed to be present include plastin 1 ( I-fimbrin ) , plastin 3 ( T-fimbrin ) , fascin-2 and TRIOBP , and all except fascin-2 are believed to be present in hair cells in early postnatal development [15]–[17] , [35] , [67] . Given the multiplicity of actin-bundling proteins , it is truly remarkable that these other actin-bundling proteins and the espin-like protein , which has also been detected in stereocilia [17] , are insufficient to compensate for the lack of espins in je/je mice . Like fimbrins/plastins and fascin , espins are relatively small monomeric globular proteins that preferentially cross-link actin filaments in parallel fashion [30] , [31] , [68] , [69] . The fact that espins show no obvious sequence homology with fimbrins/plastins and fascins raises the possibility that they supply cross-links of a qualitatively different nature . Accordingly , espin cross-links are much more potent than those of fascin at over-twisting the actin filaments in parallel actin bundles [31] . This over-twisting , which likely reflects a high degree of conformational rigidity in the espins , is predicted to allow for an optimum number of interfilament cross-links to form and could lead to enhanced stability for the parallel actin bundle [31] . Actin filament bundling and the cooperative effect on actin filament twist are both realized even at relatively low espin stoichiometry ( espin-actin ratio , ∼1/50 ) [29] , [31] . This may be important because , despite the intense espin antibody labeling we observed along vestibular stereocilia ( Figure 1C and 1E ) , one group recently reported that they recovered espin tryptic peptides at lower yield than those from other actin-bundling proteins in ripped-off preparations of chicken and rat vestibular hair cell stereocilia [17] . Irrespective of their actual stoichiometry , however , we conclude that espins fulfill indispensable and relatively early roles in the multistep assembly of the stereociliary parallel actin bundle . A precedent for multistep parallel actin bundle assembly can be seen in the developing neurosensory bristles of Drosophila , which form through the sequential actions of a putative espin ortholog , forked , followed by a fascin ortholog , singed [70] . Shin et al . [17] recently showed that fascin-2 appeared in stereociliary parallel actin bundles relatively late during the differential elongation of stereocilia and tended to concentrate near the distal end of the longest stereocilia . Thus , the sequential actions of espin and fascin-2 in hair cell stereocilia may be orthologous to the sequential actions of forked and singed in Drosophila bristle parallel actin bundles . A lack of espins may not only impede later-acting actin-bundling proteins , but might also irretrievably impair the parallel actin bundle substrate on which the other proteins involved in length regulation and mechanoelectrical signal transduction depend . This study was carried out on mice in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . The protocol was approved by the Northwestern University Animal Care and Use Committee ( Protocols 2004-0427 , 2007-0427 and 2008-1321 ) . Perfusion fixation and measurements of auditory brainstem response were performed under sodium pentobarbital anesthesia . Organ removal for western blotting was performed following euthanasia under CO2 gas-induced narcosis by decapitation with a rodent guillotine . All efforts were made to minimize animal suffering . Inbred CBA/CaJ mice ( stock number 000654 ) and jerker mice of the standard commercially available strain ( JE/LeJ; stock number 000259 ) were purchased from the Jackson Laboratory and bred and housed in the barrier-level mouse vivarium in the Center for Comparative Medicine at Northwestern University Feinberg School of Medicine . Homozygous jerker males were bred with heterozygous jerker or wild-type females . Homozygous animals older than P10 could be identified by their distinctive shaker-waltzer behavior . Genotypes were confirmed by DNA sequence analysis of PCR products obtained from tail genomic DNA [14] . We produced the congenic jerker mouse line used in this study ( CBA/CaJ . JE/LeJ-Espnje ) by repeated backcrossing into the CBA/CaJ inbred strain for 13–15 generations , according to the following scheme: male jerker homozygotes of generation n were mated with wild-type CBA/CaJ females , and the resulting heterozygous progeny from two different breeder pairs were mated to produce male jerker homozygotes of generation n+1 . Approximately every 3 generations , the wild-type CBA/CaJ female breeder stock was refreshed with mice newly purchased from the Jackson Laboratory . Wild-type mice of the JE/LeJ strain were produced by mating heterozygotes and identified by genotyping . Mice were anesthetized by intraperitoneal injection with sodium pentobarbital ( 60 mg/kg ) and briefly perfused through the ascending aorta with 0 . 9% ( w/v ) NaCl followed by fixative solution: 4% ( w/v ) formaldehyde ( freshly prepared from paraformaldehyde ) in 0 . 1 M sodium phosphate buffer , pH 7 . 4 . The inner ear was removed by dissection . A small hole was made at the top of the cochlea with the tip of a fine forceps , and the semicircular canals were broken open . Through these openings the inner ear was gently flushed with ∼0 . 3 ml of 2% ( w/v ) paraformaldehyde in 0 . 1 M sodium phosphate buffer , pH 7 . 4 , and then postfixed for an additional 1 h . Utricular maculae were dissected away from bony labyrinths . To carefully expose the epithelium , the overlying membranous labyrinth was removed , and the otoconial membrane was gently removed using a single strand from a brush . Specimens were treated with 3% ( v/v ) normal goat serum , 1% ( w/v ) bovine serum albumin , 0 . 2% ( v/v ) Triton X-100 in TBS ( 100 mM Tris , 150 mM NaCl , pH 7 . 4 ) , and incubated overnight with affinity purified rabbit polyclonal espin antibody at a concentration of 1 µg/ml . The espin antibody , which we raised against purified recombinant rat espin 2B and affinity purified on columns of rat espin 2B-Sepharose 4B , is known to react with all espin isoforms , including epitopes that are amino-terminal to site of the frameshift mutation in jerker espins [27] , [51] . The bound antibody was detected by Alexa594-goat anti-rabbit IgG ( Invitrogen ) . F-actin was visualized using Alexa488-phalloidin ( Invitrogen ) . Specimens were mounted with Vectashield ( Vector Laboratories ) and examined using the Nikon PCM2000 system confocal microscope and Simple PCI Program . Dissected cerebella were cryoprotected in 30% ( w/v ) sucrose dissolved in phosphate-buffered saline . Frozen sections , 30 µm thick , were cut in the sagittal plane on a freezing-stage sliding microtome . For bright-field microscopy , sections were processed for immunohistochemistry according to an avidin-biotin amplification protocol . Briefly , the endogenous peroxidase activity was blocked with 0 . 3% ( v/v ) H2O2 and 10% ( v/v ) methanol in TBS . Sections were treated with 3% ( v/v ) normal goat serum , 1% ( w/v ) bovine serum albumin , 0 . 2% ( v/v ) Triton X-100 in TBS and then incubated with either mouse anti-calbindin monoclonal antibody ( 1:5 , 000; Sigma ) or affinity purified rabbit polyclonal espin antibody ( see above ) . Bound antibody was detected using biotinylated donkey anti-mouse or anti-rabbit IgG ( GE Healthcare ) , the ABC Elite kit ( Vector Laboratories ) and diaminobenzidine ( Sigma ) . Images were captured with the Spot RT CCD video camera ( Diagnostics Instruments ) mounted on the Nikon Eclipse 800 microscope using the Spot RT Software 3 . 5 . 8 . Whole-mount images of eyes were captured with the Nikon digital DN100 camera mounted on the Olympus SZH10 stereomicroscope . All images were stored and processed in Adobe Photoshop CS2 . Brightness and contrast were adjusted . Mice ( ∼6 months old ) were euthanized by decapitation while under CO2 gas-induced narcosis . Testes and kidneys were removed by dissection , weighed and homogenized in 9 ( kidney ) or 18 ( testis ) volumes ( ml/g ) of ice-cold 0 . 25 M sucrose , 3 mM imidazole-HCl , pH 7 . 4 , containing 2 mM phenylmethylsulfonyl fluoride and 1% ( v/v ) Protease Inhibitor Cocktail ( Sigma P 8849 ) using 8 up-and-down strokes of a motor-driven 10-ml Teflon-glass Potter-Elvehjem homogenizer spinning at 3000 rpm . SDS gel buffer concentrate containing dithiothreitol was added , and the samples were heated at 95-100°C for 3 min with intermittent agitation on a vortex mixer . Gel samples derived from 1 . 8 mg ( testis ) or 3 . 6 mg ( kidney ) of wet tissue mass were resolved in SDS gels and transferred to nitrocellulose membrane . The blots were labeled with affinity purified rabbit polyclonal espin antibody ( see above ) at a concentration of 0 . 1 µg/ml using the ECL system ( GE Healthcare ) . Apparent molecular mass was estimated using the BenchMark Prestained Protein Ladder ( Invitrogen ) . Mice of the designated age and of either sex were anesthetized by intraperitoneal injection with sodium pentobarbital ( 60 mg/kg ) and briefly perfused through the ascending aorta with 0 . 9% ( w/v ) NaCl followed by 5-20 ml of fixative solution: 2 . 5% glutaraldehyde and 2 mM CaCl2 in 0 . 1 M sodium cacodylate buffer , pH 7 . 4 . The inner ear was removed by dissection . A small hole was made at the top of the cochlea with the tip of a fine forceps , and the semicircular canals were broken open . Through these openings the inner ear was gently flushed with ∼0 . 3 ml of 2 . 5% glutaraldehyde fixative solution and then postfixed overnight at 4°C . The membranous labyrinth , containing the cochlea and vestibular end organs , was removed by dissection . Cochlear specimens were prepared by removing the stria vascularis , Reissner's membrane and tectorial membrane . The cochlear spiral was cut into basal , middle and apical segments for processing . The utricular maculae and two adjacent , horizontal and anterior , cristae ampullares were also removed by dissection . The otolithic membrane was gently removed from the macular surface using a single strand from a brush . Specimens were processed using an osmium-thiocarbohydrazide method adapted from Hunter-Duvar [71] , which included three 1-h incubations with 1% ( w/v ) OsO4 , with 20-min incubations with saturated thiocarbohydrazide inserted after the first and second OsO4 treatments . The specimens were then dehydrated using a graded series of ethanol solutions and critical-point dried using liquid CO2 as the transitional fluid . Uncoated specimens were mounted on a Hitachi specimen stub using silver electroconductive paint and viewed using a Hitachi S-4800 field emission scanning electron microscope operated at 5 kV . Stereociliary dimensions were measured using NIH ImageJ ( Wayne Rasband , National Institutes of Health , Bethesda , MD; http://rsb . info . nih . gov/ij/ ) , and analyzed using Instat 3 . 0 ( GraphPad Software ) . To minimize foreshortening , specimens were rotated and tilted in the scanning electron microscope so that , for the purposes of measurement , the stereocilia under examination were approximately perpendicular to the direction of view . The length and width of cochlear hair cell stereocilia were measured at P0 ( n = 3 mice of each genotype ) and P5 ( n = 2 mice of each genotype ) . For each mouse , images of 10 outer and 10 inner hair cells were collected at 20 , 000X magnification from each of three different cochlear locations: base ( ∼20% from base ) , mid ( ∼50% from base ) and apex ( ∼80% from base ) . We measured the lengths of the 4 tallest stereocilia and the widths of 5 randomly selected stereocilia on each cochlear hair cell . The perpendicular alignment of stereocilia was difficult to accomplish for cochlear specimens from je/je mice , because these stereocilia were often bent in different directions ( e . g . , Figure 8G ) . We measured the lengths of the 4 tallest stereocilia on each hair cell , regardless of their location in the collection . In +/+ mice , the tallest stereocilia were located in the tallest row , near the kinocilium , whereas in je/je mice the tallest stereocilia could be found at other locations in the collection ( e . g . , Figure 8H ) . Widths were measured at 2 different positions , at the midpoint and near the top , and then averaged . The number of total surface projections ( stereocilia and microvillus-like structures ) on 10 outer hair cells from each row ( 30 outer hair cells total ) at each of the three cochlear locations were counted at P5 and P10 for mice of all three genotypes . Likewise , the numbers of missing stereocilia on outer hair cells , as evidenced by a characteristic gap in the shortest row of stereocilia ( Figure 4D and 4E ) , were counted for +/+ and +/je mice at P10 . The width of stereocilia on extrastriolar hair cells was measured at 1-µm intervals along the length . The analysis included 3 or 4 mice of each of the three genotypes at P0 , P5 , P10 and P20 , 2 je/je mice at P40 and 1 je/je mouse each at P60 and P90 . To avoid immature hair cells ( e . g . , Figure 5A and 5B ) , only hair cells with stereocilia of lengths >7 µm ( +/+ or +/je ) or >3 µm ( je/je ) were included . Images of the stereociliary collection ( n = 20–28 hair cells for each genotype and each time point ) were transferred to Adobe Photoshop , where a grid of lines ( spaced at 1-µm intervals ) was placed over the image ( Figure 11A ) . Widths were measured for clearly resolved stereocilia that crossed a line or , in the case of the distal tip , ended close to a line . In addition , we measured widths at the extreme base , where the stereocilia emerged from the apical surface of the hair cell . The width measurements at each distance were averaged for individual hair cells ( see graph in Figure 11A ) . These data were combined ( Figure 11B ) to calculate mean ± SD for each genotype and postnatal age ( Figure 11C ) . In most instances , we obtained large numbers of measurements ( >100 ) , which did not show a Gaussian distribution . Therefore , such measurements were analyzed using the nonparametric Kruskal-Wallis test followed by Dunn's multiple comparisons test . In two instances , sample sizes were , by necessity , considerably smaller and showed a Gaussian distribution: width measurements at 11 and 12 µm from the base of extrastriolar stereocilia ( Figure 11C ) . These measurements were analyzed by one-way analysis of variance followed by the Bonferroni multiple comparisons test . The average height of the collection of stereocilia on the extrastriolar hair cells of je/je mice ( Figure 5E ) was estimated from measurements of 100 hair cells at each postnatal age . Mice were anesthetized by intraperitoneal injection with sodium pentobarbital ( initial , 80 mg/kg; maintenance , 17 mg/kg ) . Auditory brainstem responses were obtained by subtracting ipsilateral mastoid potentials from vertex potentials measured relative to a ground electrode placed in the neck . The electrodes were connected to a differential amplifier ( ISO-80 , World Precision Instruments ) with a high input-impedance ( >1012 Ω ) set to 80 dB . Further filtering of the signal ( 300–3000 Hz ) was obtained through an IP90 filter ( Frequency Devices ) . The sampling rate was 200 kHz , and responses to 100 stimulus presentations were averaged . Auditory brainstem response thresholds were defined as sound levels required for a visible response to acoustic stimuli . The noise floor in an average recording was typically 1 µV . In particular , the appearance of wave II was monitored . Voltage commands for acoustical stimuli were generated using a computer KPCI 3110 I/O board ( Keithley Instruments , Inc . ) inserted into a personal computer and were used to drive a DT 770Pro headphone ( Beyerdynamic ) . For acoustically evoked auditory brainstem responses , tone bursts ( 12 ms duration , 1 ms rise/fall ) with different carrier frequencies were presented at a rate of 4 Hz . The sound pressure was measured with a real head coupler [72] . Means and standard errors were calculated for the electrophysiological thresholds . Measurements were analyzed by one-way analysis of variance followed by the Tukey-Kramer multiple comparisons test .
Stereocilia are the fingerlike projections of inner ear hair cells that detect sound and motion . Stereocilia grow to specific lengths and diameters and form staircase-like arrays . The changes in size appear to be driven by matching alterations in the dimensions of an underlying molecular scaffold consisting of a bundle of actin filaments cross-linked by actin-bundling proteins . To elucidate the roles of the espin actin-bundling proteins in hair cell stereocilia , we carry out an in-depth accounting of stereociliary size and shape in the jerker mutant mouse , which lacks the espin proteins because of a mutation in the espin gene . We examine a new and improved jerker mouse with a genetic background known for high-quality lifelong hearing . We find that , in the absence of espins , stereocilia do not increase in diameter or complete their elongation , but instead bend , shorten , and disappear . Although the specifics vary according to inner ear region , the stereociliary defects are profound and can readily account for the deafness and balance problems of jerker mice and humans with certain espin gene mutations . Even reducing espin levels by one-half leads to temporary defects in stereociliary diameter . Thus , espins play crucial roles in the formation and maintenance of hair cell stereocilia .
You are an expert at summarizing long articles. Proceed to summarize the following text: Aneuploidy , the relative excess or deficiency of specific chromosome types , results in gene dosage imbalance . Plants can produce viable and fertile aneuploid individuals , while most animal aneuploids are inviable or developmentally abnormal . The swarms of aneuploid progeny produced by Arabidopsis triploids constitute an excellent model to investigate the mechanisms governing dosage sensitivity and aneuploid syndromes . Indeed , genotype alters the frequency of aneuploid types within these swarms . Recombinant inbred lines that were derived from a triploid hybrid segregated into diploid and tetraploid individuals . In these recombinant inbred lines , a single locus , which we call SENSITIVE TO DOSAGE IMBALANCE ( SDI ) , exhibited segregation distortion in the tetraploid subpopulation only . Recent progress in quantitative genotyping now allows molecular karyotyping and genetic analysis of aneuploid populations . In this study , we investigated the causes of the ploidy-specific distortion at SDI . Allele frequency was distorted in the aneuploid swarms produced by the triploid hybrid . We developed a simple quantitative measure for aneuploidy lethality and using this measure demonstrated that distortion was greatest in the aneuploids facing the strongest viability selection . When triploids were crossed to euploids , the progeny , which lack severe aneuploids , exhibited no distortion at SDI . Genetic characterization of SDI in the aneuploid swarm identified a mechanism governing aneuploid survival , perhaps by buffering the effects of dosage imbalance . As such , SDI could increase the likelihood of retaining genomic rearrangements such as segmental duplications . Additionally , in species where triploids are fertile , aneuploid survival would facilitate gene flow between diploid and tetraploid populations via a triploid bridge and prevent polyploid speciation . Our results demonstrate that positional cloning of loci affecting traits in populations containing ploidy and chromosome number variants is now feasible using quantitative genotyping approaches . Most eukaryotic genomes maintain genes in a one-to-one relationship by their syntenic organization on chromosomes . This normal stoichiometry between chromosomes of a set can sometimes be disrupted , resulting in altered dosage of both genes and their encoded products . Such disruptions can arise via the nondisjunction of chromatids and chromosomes during mitosis and meiosis and result in uneven chromosome numbers , a condition called aneuploidy . Trisomy , the most common form of viable aneuploidy is characterized by the presence of one extra chromosome in an otherwise diploid background . The observation of stereotypical phenotypes for trisomics of each chromosome type illustrated that genetic factors are sensitive to dosage [1–5] . Indeed , the proper functioning of cells and organisms relies on molecular complexes , which require a delicate balance between components for proper operation [6] . Even a slight departure from this balance can have dramatic phenotypic or developmental consequences [6 , 7] as exemplified by the many haplo-insufficient genes identified in human as tumor suppressors [8] and as essential or regulatory genes in yeast [7 , 9] and Drosophila [10 , 11] . In aneuploids , where dosage variations affect whole chromosomes rather than single genes , the consequences can be severe when the copy numbers of many dosage-sensitive genes are altered at once . Therefore , an alteration of gene dosage such as it occurs in aneuploids typically has unfavorable consequences . Interestingly , aneuploidy is not always deleterious and can be persistent . For example , aneuploid cells are normally found in certain tissues such as the brain and the placenta , where they appear to play a functional role [12–15] . Aneuploidy has been associated with invasive cancer [16 , 17] and controversially proposed to play a causal role in malignancy [18] . Although cancer is obviously deleterious to the affected organism , somatic selection of aneuploid sectors underscores the fact that dosage imbalance can be advantageous to cells . Finally , aneuploid individuals are common in plants and in yeast and provide a pool of phenotypic variation not present in the euploid population . In specific conditions , these phenotypes can be advantageous , and the corresponding aneuploid karyotypes selected . Such successful aneuploids have been observed both in nature and in industry [19–22] . Thus , although dramatic alterations of phenotype are associated with aneuploidy , this condition can be compatible with efficient function and even fitness . There is also tremendous variation for aneuploidy tolerance between different organisms . For reasons that remain unknown , plants are generally less sensitive than animals to the type of dosage imbalance caused by aneuploidy [17] . Indeed , in humans , most aneuploidies are embryo-lethal , and the few that are viable are associated with severe developmental defects [23] . In contrast , trisomics of all chromosome types as well as more complex aneuploid types have been described in several plant species [23–27] . There is also considerable variation between plant species in the degree of lethality caused by dosage imbalance . For example , the progeny produced by triploids of different plant species vary in the extent and frequency of aneuploidy . During triploid meiosis , three sets of chromosomes must be allocated to two poles , producing mostly aneuploid gametes . The progeny produced by such gametes should consist of a swarm of aneuploid types ranging from near diploid to near tetraploid . Such a swarm is produced by triploids of certain species , such as A . thaliana [24] , but triploids of other species fail to produce such a range of aneuploids generating instead mostly diploids and near diploids [3 , 23 , 28 , 29] . Finally , sensitivity to aneuploidy can differ between varieties of the same species . One such case was reported in tomato in which cherry tomato produced aneuploids with an average of one more extra chromosome than those of a large-fruited tomato variety [30] . Similar observations were made in barley in which the aneuploids produced by a wild variety carried a higher number of extra chromosomes , were more vigorous , and exhibited higher fertility than the aneuploids produced by a triploid of a cultivated variety [31 , 32] . In A . thaliana , comparison of identical trisomics of the Columbia ( Col-0 ) and Landsberg erecta ecotypes uncovered differences in fertility and transmission rate of the trisomic chromosome [5 , 33–35] . A detailed genetic characterization of natural variation for tolerance to aneuploidy and cloning of the responsible loci have so far not been possible . Recent technological advances in Arabidopsis that combine molecular karyotyping and quantitative genotyping now allow quantitative genetic analysis of aneuploid populations [36] . Here , we report the first step towards positional cloning of a locus affecting aneuploid survival in Arabidopsis . We previously investigated the effect of genotype on the rate of aneuploidy production by comparing the karyotype swarms in the progeny of two triploids of A . thaliana . One genotype , the CCC triploid , was produced from a cross between diploid Col-0 and its synthetically derived tetraploid ( 4x-Col ) . The other , the CWW triploid , was produced from a cross between diploid Col-0 and the naturally occurring tetraploid Warschau ( Wa-1 ) . We demonstrated that both of these triploids were fertile and produced a swarm of aneuploid progeny [24] . Genotype influenced both fertility of the triploids and the composition and performance of their aneuploid swarms . Additionally , recombinant inbred lines ( RILs ) produced from the progeny of a CWW triploid [37] resolved into two cohorts of near-diploid and near-tetraploid genome contents ( Figure 1 ) . Genetic analysis of these RILs identified transmission distortion at genetic markers on Chromosome 1 in the near-tetraploid but not the near-diploid lines [24] . In the present report , we investigated the genetic mechanisms responsible for the ploidy-dependent selection of this locus , which we call SENSITIVE TO DOSAGE IMBALANCE ( SDI ) . Our results demonstrate that transmission distortion favoring the Wa-1 allele of SDI occurs in genome content classes containing the most severe aneuploids , produced by selfing a triploid . This indicates a role for SDI in aneuploidy survival , possibly by buffering the dosage-related challenges associated with aneuploidy . Potential mechanisms consistent with these observations and their evolutionary implications are discussed . We produced an F2 family from tetraploid CCWW plants ( Figure 2A ) . Ninety CCWW F2 individuals were genotyped at 11 markers , including nga280 and the linked MN1 . 2 . No markers exhibited transmission ratio distortion in this population ( Figure 2B ) . Thus , the polymorphism at SDI is unlikely to be critical for the survival of tetraploids . As expected from our previous studies of tetraploids [36] , several aneuploid individuals were identified among this CCWW F2 population ( Figure 2A ) . To investigate whether the SDI allele from Wa-1 was selected in the aneuploid individuals in this F2 population , the percentage of the Wa-1 allele in the aneuploid and tetraploid subpopulations were compared . Of the 11 markers tested , only MN1 . 2 ( but not nga280 ) exhibited a significant difference between the two subpopulations . A higher percentage of the Wa-1 allele was present in the aneuploids than in the euploid tetraploid F2 progeny ( t-test p-value = 0 . 0396 ) ( Figure 2C ) . The previously characterized populations derived by selfing of the CWW triploid [24] were tested for selection at SDI . In both the CWW F2 population and the near-diploid RILs , the percentage of Wa-1 allele at MN1 . 2 was , on average , higher in the aneuploid individuals than in the euploid individuals ( Figure 3A , inset ) . This trend was weak and not significant ( t-test p-value = 0 . 38 and 0 . 34 , respectively ) . A shortcoming of grouping all CWW-produced aneuploids is that differences in the severity of aneuploidy and thus differences in selection for aneuploidy tolerance were not accounted for . To investigate the relationship between selection of the Wa-1 allele and aneuploidy severity , a quantitative measure of karyotype-dependent selection was developed . As previously reported , the CWW F2 is a complex swarm of aneuploids of various karyotypes [24] . This swarm does not match the predicted outcome of triploid meiosis , presumably due to lethality differentially affecting these karyotypes [24] . The expected frequency of each genome content class was calculated previously ( see Figure 2C in [24] ) . For each genome content class , the ratio of expected-to-observed frequencies ( Figure 3A ) was used to calculate the aneuploidy selection index ( ASI ) ( see Material and Methods for details ) ( Figure 3B ) . Negative and positive values for ASI indicated overrepresentation and underrepresentation of a class relative to the expected frequency , respectively . To test the biological significance of the ASI , we examined the relationship between ASI and seed production in the CWW F2 population . Both the percentage of plump seed ( as an estimate of seed viability ) and the total number of seed produced by each of the CWW F2 individuals were recorded ( Figure 4A ) . Their relationship with ASI was determined by regression analyses ( Figure 4B ) . Both regressions were highly significant ( p-value = 0 . 0002 for seed viability and <0 . 0001 for seed counts ) . This demonstrated that ASI is a biologically relevant measure correlated with the viability selection acting upon aneuploid classes and predicts the strength of viability selection in the next generation . Thus , ASI represents an excellent early indicator of karyotype-modulated viability selection . To test whether the Wa-1 allele at SDI could be linked to increased survival of aneuploid individuals , the relationship between marker genotype and ASI was investigated by regression analysis . The percentage of Wa-1 allele at three markers , all located at the bottom of Chromosome 1 were significantly associated with ASI ( Table 1 ) . The most significant association between genotype and ASI was found at MN1 . 2 ( Figure 5 ) . This association was consistent with a role for SDI in modulating the viability of aneuploids . Unfortunately , because there is no diploid of Wa-1 , it was not possible to perform a similar analysis on the progeny of a CCW triploid , which could have controlled for potential effects of preferential pairing or segregation . It is possible that an unidentified meiotic mechanism affected chromosome segregation such that it would be responsible for the effect observed at SDI . We have observed that the percentage Wa-1 allele at SDI increases with genome content ( regression p-value = 0 . 0006 , r2 = 0 . 12 ) . Yet , when the effects of genome content and ASI were tested simultaneously , only the effect of ASI ( p-value = 0 . 0096 ) remained significant while that of genome content ( p-value = 0 . 41 ) did not . This suggested that the apparent increase of percentage Wa-1 allele with genome content was due to the correlation between ASI and genome content ( regression p-value < 0 . 0001 , r2 = 0 . 44 ) and not due to an overall increased percentage Wa-1 allele in disomic gametes . The percentage Wa-1 allele at MN1 . 2 was lower than expected in the diploid individuals and aneuploids of low genome content ( Figure 5 ) . This observation was unexpected but appears to fit within a genome-wide phenomenon . In both the CWW F2 and RIL populations , the percentage Wa-1 allele was on average lower than the expected 66% throughout the genome [37] . Although we do not have an explanation for this observation , marker MN1 . 2 is not unusual in this respect . The association between genotype at MN1 . 2 and karyotype was also analyzed using the progeny of pseudo-backcrosses ( pBC ) involving the CWW triploid . In these populations , one of the two parents is a CWW triploid while the other parent is either diploid Col-0 or its tetraploid derivative ( 4x-Col ) . ASI values for each chromosome content class were calculated separately for each of the four pBC populations: CWW × Col-0; CWW × 4x-Col; Col-0 × CWW; 4x-Col × CWW . An association between ASI and marker genotype was again tested by regression . As observed in the CWW F2 aneuploid swarm , the percentage of Wa-1 allele at MN1 . 2 increased with the ASI but the regression was not significant for any of the four populations studied ( p-values between 0 . 23 and 0 . 92 ) . Thus , selection for SDI in the progeny of a triploid was only visible in the context of a selfed triploid , where zygotes can be more severely imbalanced and where aneuploidy can have both maternal and paternal origin . The percentage of Wa-1 allele at SDI increased with our measure of aneuploidy selection in the CWW F2 population . Thus , selection at SDI in the CWW F2 was karyotype-dependent . Yet , karyotype-dependent selection at SDI was not significant in the progeny of the pBC . Comparing the theoretical population of aneuploids produced by a selfed triploid to those produced in the pBC suggests possible explanations for this observation ( Figure 6 ) . The pBC populations are exclusively composed of moderate aneuploid ( dosage deviation of no more than one chromosome , light blue ) and euploid ( red ) individuals . These individuals are also present in the triploid F2 distribution , where they only represent a minor proportion ( Figure 6A versus 6C ) . More extreme aneuploid individuals ( in green in Figure 6 ) , containing two copies of some chromosome types and four copies of others can be formed from a selfed triploid when aneuploid gametes carrying extra copies of the same chromosome types fertilize each other . In fact , this class of aneuploids constitutes the majority of the possible CWW F2 karyotypes and cannot be produced in pBCs , where one parent contributes only euploid gametes ( Figure 6 ) . The presence of these extreme aneuploids is supported by the fact that individuals with extreme phenotypes ( e . g . , extreme dwarfism or complete sterility ) were observed in the CWW F2 but not in the pBCs ( unpublished data ) , and that the genome content classes with higher ASI values are those with the highest predicted frequency of severe aneuploids . The theoretical proportion of these severe aneuploids increases rapidly with increasing chromosome number . For example , triploids with five chromosome types are expected to produce 56% severe aneuploids . On the other hand , triploid individuals with ten chromosome types , such as maize , are expected to produce 89% severe aneuploid progeny . If the severe aneuploid types are more strongly selected against , one would therefore expect that seed yield and viability would be lower in triploids of species with higher chromosome number . The differences in karyotype distributions between the progeny of a selfed triploid and those from a pBC are not limited to the presence or absence of the extreme aneuploid class . Also present in the triploid F2 but absent from the pBC , is a second class of moderate aneuploids: those formed following the fusion of two ( instead of one ) aneuploid gametes ( Figure 6A , dark blue ) . These make up the second largest group of expected progeny in the selfed triploid . In aneuploid gametes , the production of dosage-sensitive factors needed for the success of fertilization and early development is compromised . In a fertilization event in which both gametes are aneuploid , the inappropriate production of factors such as those responsible for interploidy seed failure and the endosperm dosage factors [38] are most likely to result in seed failure . Finally , in a selfed triploid opportunities for selection at the gametophyte generation occur in both pollen and ovules . This doubles the impact of selection on the haploid generation as compared to the pBCs . Consistent with the association of SDI selection with aneuploidy severity in the CWW F2 , the highest percentages of Wa-1 allele at SDI are associated with the genome content classes that are predicted to contain the highest percentage of severe aneuploids ( genome content classes 2 . 8 , 3 . 0 , and 3 . 2 ) . As argued above , selection against severe aneuploids may be an important determinant of genome content distribution in triploid progeny . Our data suggest that other processes also contribute to it . If selection against severe aneuploids was the only driving force , one would expect a completely symmetrical bimodal distribution ( encompassing the red , orange , and light blue individuals in Figure 6 ) . The observed distribution ( Figure 3A ) is bimodal but not symmetrical . It has been hypothesized that carrying an excess of several types of chromosomes ( such as in genome content classes 2 . 6 or 2 . 8 ) is more deleterious than carrying only one chromosome in excess ( genome content classes 2 . 2 or 3 . 2 ) . This hypothesis is based on the observation that most regulatory interactions are negative [10] . Therefore , increasing the number of copies at more loci ( or stated differently , having a minority of loci in relative deficiency ) should increase the probability of negatively affecting loci involved in crucial cellular or developmental processes and that are not located on the additional chromosome copies [10] . Our results agree with this hypothesis: in each half of the distribution , individuals with lower genome contents are more common than individuals with higher genome content ( Figure 3A ) . We have shown that the naturally collected tetraploid ecotype Wa-1 produced aneuploid individuals more often than Col-0 [36] . In addition , an allele from Wa-1 is associated with the survival of severe aneuploid individuals ( Figures 2 and 5 ) . Considering the obvious negative consequences of aneuploidy , is there a counterbalancing advantage that could justify the persistence of such a trait ? Persistent aneuploidy has been reported in several specific situations where the aneuploid phenotype confers a selective advantage relative to the diploid phenotype . For example , segmental aneuploidy is frequent in yeast deletion mutants [39] and can confer a growth advantage to the aneuploid cells compared to the euploid ones [39] . In humans , aneuploid cells have recently been found to be an integral part of the functional pool of neurons and in placental trophoblasts , consistent with a functional role for aneuploidy in these contexts [12–15] . In plants , it is believed that aneuploidy can play a role in speciation and phenotype evolution [40] . Additionally , aneuploidy is very frequent in polyploid populations [2 , 40] as well as in the process of polyploid formation through the triploid bridge in species for which triploids are readily produced and are fertile [24 , 41] . In neopolyploids , aneuploidy may contribute to phenotypic variability [40] and become fixed through selection for advantageous karyotypes . An allele associated with increased tolerance to dosage imbalance would therefore increase the probability that advantageous karyotypes arise and reproduce successfully . In addition , it would increase the fertility of triploids produced from unreduced gametes or interploidy hybridization . This would enhance gene flow between diploid and polyploid subpopulations via a triploid bridge and aneuploid swarms and allow the sharing of alleles arising in either population . Such recurrent triploid formation between diploid and polyploid populations and repeated formation of polyploid derivatives would therefore hinder polyploid speciation and may explain why multiple karyotypes are often cataloged as a single species based on shared ecological and morphological traits . The fact that selection acting on SDI is associated with the presence of extreme aneuploids suggests that SDI acts to buffer the effects of dosage imbalance . This buffering could enhance the survival of aneuploid gametes or that of the fertilization products , either the zygote itself or the endosperm and could be mediated through a number of mechanisms . Selection at SDI could stem directly from specific dosage effects on one or a few dosage-sensitive genes . Aneuploidy affects the regulation of genes located both on the varied chromosome and on the rest of the genome [17 , 42–45] . The intensity of these effects varies from gene to gene and can be positive or negative , illustrating the complexity of the regulatory networks [10 , 17 , 44 , 46] . Observations in maize and Drosophila have led to the idea of a “dosage-regulatory hierarchy , ” in which the expression of a given gene might be regulated by several dosage-dependent regulators , which in turn are involved in the regulation of several target genes [10] . This hypothesis would be consistent with the possibility that selection at SDI stems from the misregulation of a specific dosage-sensitive gene linked to SDI . Karyotype-dependent selection at SDI could also originate from a genome-wide effect mediated by SDI . Changes in chromosome number affect overall genome maintenance , function , and regulation [47 , 48] . For example , polyploidy results in variation in epigenetic regulation , as demonstrated by ploidy-sensitive gene silencing and paramutation in Arabidopsis [48–50] . Epigenetic silencing in polyploids and aneuploids may result directly from dosage imbalance . This idea is supported by our understanding of the mechanisms underlying dosage compensation in flies , mammals , and worms , which all rely on chromatin remodeling [51] . In plants , trisomy dependent epigenetic instability has been reported for a transgenic locus in tobacco [52 , 53] . Similarly , cancerous cells are associated with both aneuploidy and epigenetic modifications [17 , 54] . In addition , meiotic silencing of unpaired DNA has been demonstrated in Neurospora crassa [55] , in X-chromosome imprinting in C . elegans [56] , and in sex chromosome inactivation in mammals [57] . It is possible that the presence of unpaired chromosomes during triploid or aneuploid meiosis has similar consequences . Thus , it is possible that the SDI locus encodes a regulator mediating a genome-wide epigenetic response to dosage imbalance . The possible involvement of SDI in epigenetic modifications of the genome or in its regulation is an attractive hypothesis that the recent publication of the complete methylome of A . thaliana [58 , 59] , natural variation in A . thaliana methylation level [60 , 61] , and our ability to detect and karyotype aneuploid individuals [36] will help address . In conclusion , we have established a quantitative measure for aneuploid survival . Using this trait , we have demonstrated the feasibility of genetic mapping in aneuploids and associated a locus to the variation in aneuploid survival observed in Arabidopsis . Characterization of the gene ( s ) responsible for SDI should facilitate a better understanding of the mechanisms governing the sensitivity to dosage imbalance and aneuploid syndromes . All plants were grown on soil ( Sunshine Professional Peat-Lite mix 4 , SunGro Horticulture , http://www . sungro . com ) in a growth room lit by fluorescent lamps ( Model TL80; Philips , http://www . lighting . philips . com ) at 22 ± 3 °C with a 16 h:8 h light:dark photoperiod or in a greenhouse at similar temperatures and light regimes , with supplemental light provided by sodium lamp illumination as required . Tetraploid lines were described previously [24] . Col-0 represents the diploid ecotype Columbia , 4x-Col represents tetraploidized Col-0 , and Wa-1 represents the naturally occurring tetraploid ecotype Warschau-1 ( CS6885 ) . C and W refer to basic genomes or alleles of Col-0 and Wa-1 , respectively . The CCWW F2 population ( n = 90 ) was obtained by crossing 4x-Col as the seed parent to Wa-1 and allowing three F1 individuals to self pollinate . The Col-0 × Wa-1 RILs described by Schiff and coworkers [37] were a kind gift from Shauna Somerville ( Carnegie Institution , Stanford University ) . The CWW triploid plants were generated by crossing Col-0 as the seed parent to Wa-1 . The CWW F2 population ( n = 109 ) was generated as described [24] . In order to reduce the complexity of the aneuploid swarm produced by a triploid , pBC populations were generated by crossing CWW triploids to either diploid Col-0 or tetraploid 4x-Col , in both directions [36] . The four types of pBC populations and the number of individuals analyzed in the context of this report were as follows: Col-0 × CWW ( n = 80 ) , CWW × Col-0 ( n = 102 ) , 4x-Col × CWW ( n = 33 ) , CWW × 4x-Col ( n = 47 ) . Single siliques were harvested into individual tubes , and all the seeds from each fruit were counted using a dissecting microscope . To estimate seed viability , seeds were characterized as “plump” if they contained a visible embryo structure at least 20% the size of wild-type seed or “shriveled” if they did not . On average , five individual siliques were counted for each individual . Mean values for each group of plants were compared pair-wise using Student's t-test and p-values < 0 . 05 were considered significant . All individuals in the pBC , the CWW F2 , and the RIL populations were analyzed for genome content as previously described [24 , 36] . Briefly , control A . thaliana samples of known genome content were run before , between , and after experimental samples and used to create a standard curve , allowing us to determine the genome content of our experimental samples . Individuals were categorized by genome content values expressed as a multiple of the haploid genome content of Col-0 . On this scale , a 2 . 0 corresponds to a diploid individual , and a 4 . 0 corresponds to a tetraploid individual . The number of categories was chosen based on how many chromosome number classes were expected ( four classes between diploidy and triploidy as well as four classes between triploidy and tetraploidy ) . We have previously shown that this method is accurate and precise for A . thaliana aneuploids by comparing our flow results to complete karyotypes ( r2 = 0 . 983 in [36] ) . Quantitative genotyping was performed as previously described [36] . Different populations were genotyped at different markers . The progeny of the pBCs were genotyped at all 12 markers previously described [36] , and the data were used to infer the complete karyotype of each individual [36] . The CCWW F2 individuals were genotyped at eight of those 12 markers located on four of the five chromosome types , namely MN1 . 5 , MN1 . 6 , MN1 . 7 , MN1 . 2 , nga1126 , nga1145 , MN4 . 2 , and MSAT5 . 19 as well as at the three additional markers nga280 , F5I14 , and nga692 all located on Chromosome 1 . The CWW F2 individuals were genotyped at MN1 . 5 , MN1 . 7 , MN1 . 2 , nga280 , F5I14 , nga1126 , nga1145 , and MSAT5 . 19 located on three of the five chromosome types . Finally one marker , MN1 . 2 , located approximately 5 . 7 cM distal to the centromere from nga280 , was added to the Col-0 × Wa-1 RILs genotype data [24 , 37] . Selection at MN1 . 2 in the near-tetraploid RILs was evaluated according to previous protocols [24] and was identical to that of nga280 . In the near-diploid population , the percentage of Wa-1 allele at MN1 . 2 was slightly higher than at nga280 . As a result , comparisons between diploid and tetraploid RILs were not significant after a Bonferroni correction for 11 independent tests but a strong trend was evident ( Fisher Exact test p-value = 0 . 009 ) . MN1 . 2 spans a deletion polymorphism between Col-0 and Wa-1 , while nga280 amplifies a polymorphic microsatellite repeat . Because of the technical advantages of using an indel polymorphism for quantitative genotyping [36] , and because of the close linkage to nga280 , we employed MN1 . 2 for the quantitative genetic analyses of the pBCs . MN markers were designed by identifying short insertions or deletions between Col-0 and Wa-1 present in the sequence database provided by the Magnus Nordborg laboratory ( http://walnut . usc . edu/apache2-default ) [62] . The sequence and modification of these primers was summarized previously [36] . Forward primers for markers nga280 , F5I14 , and nga692 [24 , 63] were labeled with 6-FAM , NED , and ROX respectively . For the pBC populations , chromosome doses were inferred from quantitative fluorescent PCR as previously described [36] . Individuals were categorized depending on their chromosome number . Individuals with 10 , 15 , or 20 chromosomes were classified as euploids , while all other individuals were classified as aneuploid . Individuals from the CCWW F2 population were only partially karyotyped as data were only available for four of the five chromosome types . Individuals for which all quantitative genotypes were consistent with tetraploidy ( n = 72 ) were classified as euploid , while individuals for which the quantitative genotypes for at least one chromosome type indicated the presence of additional or missing chromosomal copies were classified as aneuploid ( n = 18 ) . For each population , the numbers of Col-0 and Wa-1 alleles were counted . For example , a CWWW genotype contributed three Wa-1 alleles and one Col-0 allele . The ratio of C to W within the population was compared to the expected 1:1 ratio using chi-squared tests . Transmission ratio distortion in the CCWW F2 as compared to the expected 1:1 ratio was tested by chi-square analysis . Test significance was set at a p-value < 0 . 0083 , equivalent to a Bonferroni correction for a p < 0 . 05 and six independent tests on four chromosome types . Only the euploid individuals were used for this analysis ( n = 72 ) , to eliminate any possible effect of aneuploidy on genotype . In order to statistically test the effect of marker genotypes on various traits , genotypes were expressed quantitatively as the percentage of Wa-1 allele . These values were inferred directly from the genotypes determined using quantitative fluorescent PCR . For example , the CCCW genotype was assigned a quantitative genotype value of 25 . These values were used to test the relationship between marker genotype and ASI ( see below ) by regression analysis . For the CWW F2 populations , regressions associated with p-values < 0 . 01 were considered significant , equivalent to a Bonferroni corrected p < 0 . 05 for five independent tests on three chromosome types . Similarly , for the pBC populations , regressions associated with p-values < 0 . 005 to control for ten independent tests on ten chromosome types . A value for ASI was calculated for each genome content class of the CWW F2 population . The expected frequency of each genome-content class was calculated assuming random assortment of three sets of chromosomes and no selection for or against karyotypes . For each genome content class , the observed frequency was calculated by dividing the number of individuals observed in that genome content class by the total number of individuals . The values for ASI for each genome content class were obtained using the following formula: ASI = log 2 ( expected frequency/observed frequency ) . Using this formula , chromosome number classes that were overrepresented relative to their expected frequencies were assigned negative ASI values , while positive ASI values indicated selection against a chromosome number class . Some of the genome content classes included few individuals . We tested an alternative version of ASI in which these classes were excluded from the analysis . Although the p-values obtained were higher , the percentage of Wa-1 allele was significantly affected by ASI at the same markers as presented in Table 1 . A similar approach was applied to the pBC populations with the exception that genome content classes were replaced by chromosome number classes since each of the pBC individuals had been molecularly karyotyped [36] . ASI values were calculated separately for each pBC population . The Arabidopsis Information Resource ( TAIR ) ( http://www . arabidopsis . org ) accession number for Wa-1 is CS6885 .
Each eukaryotic genome is subdivided into a specific number of chromosome types , which in turn are present in a characteristic number of copies , usually the same for all chromosomes . In the condition called aneuploidy , copy number differs among chromosome types , disrupting their balance and that of their encoded factors . As a result , aneuploidy is associated with developmental defects and death . For example , most types of human aneuploids are unviable: the only autosomal aneuploidy compatible with protracted survival , Down syndrome , is caused by the presence of three copies , instead of two , of the very small Chromosome 21 . In plants , aneuploidy is more common and less deleterious . This suggests that plants can more easily tolerate the effects of aneuploidy and can be used to investigate them . Here , we used the model plant Arabidopsis thaliana to produce and investigate populations of aneuploid individuals . By comparing genetically distinct aneuploid populations , we identified a chromosomal region that is associated with greater aneuploid survival . Characterizing the genetic mechanism modulating the response to changes in chromosomal dosage and aneuploid survival will help understand how genome organization affects biological processes and why aneuploidy results in such severe developmental defects .
You are an expert at summarizing long articles. Proceed to summarize the following text: Circadian rhythms enable organisms to synchronise the processes underpinning survival and reproduction to anticipate daily changes in the external environment . Recent work shows that daily ( circadian ) rhythms also enable parasites to maximise fitness in the context of ecological interactions with their hosts . Because parasite rhythms matter for their fitness , understanding how they are regulated could lead to innovative ways to reduce the severity and spread of diseases . Here , we examine how host circadian rhythms influence rhythms in the asexual replication of malaria parasites . Asexual replication is responsible for the severity of malaria and fuels transmission of the disease , yet , how parasite rhythms are driven remains a mystery . We perturbed feeding rhythms of hosts by 12 hours ( i . e . diurnal feeding in nocturnal mice ) to desynchronise the host’s peripheral oscillators from the central , light-entrained oscillator in the brain and their rhythmic outputs . We demonstrate that the rhythms of rodent malaria parasites in day-fed hosts become inverted relative to the rhythms of parasites in night-fed hosts . Our results reveal that the host’s peripheral rhythms ( associated with the timing of feeding and metabolism ) , but not rhythms driven by the central , light-entrained circadian oscillator in the brain , determine the timing ( phase ) of parasite rhythms . Further investigation reveals that parasite rhythms correlate closely with blood glucose rhythms . In addition , we show that parasite rhythms resynchronise to the altered host feeding rhythms when food availability is shifted , which is not mediated through rhythms in the host immune system . Our observations suggest that parasites actively control their developmental rhythms . Finally , counter to expectation , the severity of disease symptoms expressed by hosts was not affected by desynchronisation of their central and peripheral rhythms . Our study at the intersection of disease ecology and chronobiology opens up a new arena for studying host-parasite-vector coevolution and has broad implications for applied bioscience . The discovery of daily rhythms in parasites dates back to the Hippocratic era and a taxonomically diverse range of parasites ( including fungi , helminths , Coccidia , nematodes , trypanosomes , and malaria parasites [1–6] ) display rhythms in development and several behaviours . Yet , how rhythms in many parasite traits are established and maintained remains mysterious , despite their significance , as these traits underpin the replication and transmission of parasites [7] . For example , metabolic rhythms of Trypanosoma brucei have recently been demonstrated to be under the control of an oscillator belonging to the parasite , but the constituents of this oscillator are unknown [8] . In most organisms , endogenous circadian oscillators ( “clocks” ) involve transcription-translation feedback loops whose timing is synchronised to external cues , such as light-dark and feeding-fasting cycles [9 , 10] but there is generally little homology across taxa in the genes underpinning oscillators . Multiple , convergent , evolutionary origins for circadian oscillators is thought to be explained by the fitness advantages of being able to anticipate and exploit predictable daily changes in the external environment , as well as keeping internal processes optimally timed [11 , 12] . Indeed , the 2017 Nobel Prize in Physiology/Medicine recognises the importance of circadian oscillators [13 , 14] . The environment that an endoparasite experiences inside its host is generated by many rhythmic processes , including daily fluctuations in the availability of resources , and the nature and strength of immune responses [15 , 16] . Coordinating development and behaviour with rhythms in the host ( or vector ) matters for parasite fitness [17] . For example , disrupting synchrony between rhythms in the host and rhythms in the development of malaria parasites during asexual replication reduces parasite proliferation and transmission potential [18 , 19] . Malaria parasites develop synchronously during cycles of asexual replication in the host’s blood and each developmental stage occurs at a particular time-of-day . The synchronous bursting of parasites at the end of their asexual cycle , when they release their progeny to infect new red blood cells , causes fever with sufficient regularity ( 24 , 48 , or 72 hourly , depending on the species ) to have been used as a diagnostic tool . Malaria parasites are assumed to be intrinsically arrhythmic and mathematical modelling suggests that rhythms in host immune effectors , particularly inflammatory responses , could generate rhythms in the development of malaria parasites via time-of-day-specific killing of different parasite developmental stages [20 , 21] . However , the relevant processes operating within real infections remain unknown [22] . Our main aim is to use the rodent malaria parasite Plasmodium chabaudi to ask which circadian rhythms of the host are involved in scheduling rhythms in parasite development . In the blood , P . chabaudi develops synchronously and asexual cycles last 24 hours , bursting to release progeny ( schizogony ) in the middle of the night when mice are awake and active . We perturbed host feeding time ( timing of food intake ) , which is known to desynchronise the phase of rhythms from the host’s central and peripheral oscillators , and we then examined the consequences for parasite rhythms . In mammals , the central oscillator in the brain ( suprachiasmatic nuclei of the hypothalamus , SCN ) , is entrained by light [10 , 23] . The SCN is thought to shape rhythms in physiology and behaviour ( peripheral rhythms ) by entraining peripheral oscillators via hormones such as glucocorticoids [24] . However , oscillators in peripheral tissues are self-sustained and can also be entrained by several non-photic cues , such as the time-of-day at which feeding occurs [25 , 26] . Thus , eating at the wrong time-of-day ( e . g . diurnal feeding in nocturnal mice ) leads to altered timing of oscillators , and their associated rhythms in peripheral tissues . This phase-shift is particularly apparent in the liver where an inversion in the peak phase of expression of the circadian oscillator genes Per1 and Per2 occurs [26] . Importantly , eating at the wrong time-of-day does not alter rhythmic outputs from the central oscillator [25] . In murine hosts with an altered ( diurnal ) feeding schedule , the development rhythms of parasites remained synchronous but became inverted relative to the rhythms of parasites in hosts fed at night . Thus , feeding-related outputs from the hosts peripheral timing system , not the SCN , are responsible for the timing ( phase ) of parasite rhythms . We also reveal that the inversion of parasite rhythms corresponds to a phase-shift in blood glucose rhythms . That parasites remain synchronous during the rescheduling of their rhythm coupled with evidence that immune responses do not set the timing of parasite rhythms , suggests parasites are responsible for scheduling their developmental rhythm , and may express their own circadian rhythms and/or oscillators . Furthermore , our perturbed feeding regimes are comparable to shift work in humans . This lifestyle is well-known for increasing the risk of non-communicable diseases ( cancer , type 2 diabetes etc . [27] ) but our data suggest the severity of malaria infection ( weight loss , anaemia ) is not exacerbated by short-term desynchronisation of the central and peripheral oscillators . First , we examined the effects of changing the time of food intake on the phasing of circadian rhythms in host body temperature and locomotor activity ( Fig 1 ) . Body temperature is a commonly used phase marker of circadian timing because core body temperature increases during activity and decreases during sleep [28 , 29] . Mice were given access to food for 12 hours in each circadian cycle , either in the day ( LF , light fed ) or night ( DF , dark fed ) . All food was available ad libitum and available from ZT 0–12 ( ZT refers to ‘Zeitgeber Time’; ZT 0 is the time in hours since lights on ) for LF mice , and from ZT 12–24 for DF mice . All experimental mice were entrained to the same reversed photoperiod , lights on: 7pm ( ZT 0/24 ) , lights off: 7am ( ZT 12 ) , for 2 weeks prior to starting the experiment ( Fig 1 ) . We found a significant interaction between feeding treatment ( LF or DF ) and the time-of-day ( day ( ZT 0–12 ) or night ( ZT 12–24 ) ) that mice experience elevated body temperatures ( χ2 ( 5 , 6 ) = 75 . 89 , p < 0 . 0001 ) and increase their locomotor activity ( χ2 ( 5 , 6 ) = 39 . 57 , p < 0 . 0001; S1 Table ) . Specifically , DF mice have elevated body temperature and are mostly active during the night ( as expected ) whereas LF mice show no such day-night difference in body temperature and locomotor activity , due to a lack of night time elevation in both measures where food and light associated activity are desynchronised ( Fig 2 ) . We also find the centres of gravity ( CoG; a general phase marker of circadian rhythms , estimated with CircWave ) , are slightly but significantly earlier in LF mice for both body temperature ( approximately 2 hours advanced: χ2 ( 3 , 4 ) = 28 . 17 , p < 0 . 0001 ) and locomotor activity ( approximately 4 hours advanced: χ2 ( 3 , 4 ) = 27 . 32 , p < 0 . 0001 ) ( S1 Table ) . Therefore , the LF mice experienced a significant change in the daily profile of activity , which is reflected in some phase advance ( but not inversion ) relative to DF mice , and significant disruption to their body temperature and locomotor activity rhythms , particularly during the night . Because an altered feeding schedule does not affect the phase of the SCN [25] , our data suggest that rhythms in body temperature and locomotor activity in LF mice are shaped by both rhythms in feeding and the light-dark cycle [30] . Finally , the body weight of LF and DF mice did not differ significantly after 4 weeks ( χ2 ( 3 , 4 ) = 0 . 02 , p = 0 . 9 ) and both groups equally gained weight during the experiment ( S1 Fig ) , corroborating that LF mice were not calorie restricted . Having generated hosts in which the phase relationship between the light-entrained SCN and food-entrained rhythms are altered ( LF mice ) or not ( DF mice ) , we then infected all mice with the rodent malaria parasite Plasmodium chabaudi adami genotype DK ( Fig 1 ) from donor mice experiencing a light-dark cycle 12 hours out of phase with the experimental host mice . After allowing the parasite’s developmental rhythms to become established ( see Materials and Methods ) we compared the rhythms of parasites in LF and DF mice . We hypothesised that if parasite rhythms are solely determined by rhythms driven by the host’s SCN ( which are inverted in the host mice compared to the donor mice ) , parasite rhythms would equally shift and match in LF and DF mice because both groups of hosts were entrained to the same light-dark conditions . Yet , if rhythms in body temperature or locomotor activity directly or indirectly ( via entraining other oscillators ) contribute to parasite rhythms , we expected that parasite rhythms would differ between LF and DF hosts . Further , if feeding directly or indirectly ( via food-entrained oscillators ) drives parasite rhythms , we predicted that parasite rhythms would become inverted ( Fig 1 ) . In the blood , P . chabaudi parasites transition through five developmental stages during each ( ~24hr ) cycle of asexual replication ( Fig 3A ) [6 , 31] . We find that four of the five developmental stages ( rings , and early- , mid- , and late-trophozoites ) display 24hr rhythms in both LF and DF mice ( Fig 3B , S2 Table , S2 Fig ) . The fifth stage—schizonts—appear arrhythmic but this stage sequesters in the host’s tissues [32 , 33] and so , are rarely collected in venous blood samples . Given that all other stages are rhythmic , and that rhythms in ring stages likely require their parental schizonts to have been rhythmic , we expect schizonts are rhythmic but that sequestration prevents a reliable assessment of their rhythms . The CoG estimates for ring , and early- , mid- , and late-trophozoite stages are approximately 10–12 hours out-of-phase between the LF and DF mice ( Fig 3B and 3C , S2 Table ) . For example , rings peak at approximately ZT 10 in LF mice and peak close to ZT 23 in DF mice . The other stages peak in sequence . Schizogony ( when parasites burst to release their progeny ) occurs immediately prior to reinvasion , therefore we expect it occurs during the day for the LF mice and night for DF mice [7] . The almost complete inversion in parasite rhythms between LF and DF mice demonstrates that feeding-related rhythms are responsible for the phase of parasite rhythms , with little to no apparent contribution from the SCN and/or the light: dark cycle . Changing the feeding time of nocturnal mice to the day time has similarities with shift work in diurnal humans [34] . This lifestyle is associated with an increased risk of acquiring non-communicable diseases ( e . g . cancer , diabetes ) [35] and has been recapitulated in mouse models [e . g . 36 , 37 , 38] . In contrast , in response to perturbation of their feeding rhythm , infections are not more severe in hosts whose circadian rhythms are desynchronised ( i . e . LF hosts ) . Specifically , all mice survived infection and virulence ( measured as host anaemia; reduction in red blood cells ) of LF and DF infections is not significantly different ( comparing minimum red blood cell density , χ2 ( 3 , 4 ) = 0 . 11 , p = 0 . 74; S3A Fig ) . As described above , changes in body mass were not significantly different between treatments ( S1 Fig ) . Using a longer-term model for shift work may reveal differences in infection severity , especially when combined with the development of non-communicable disease . There are no significant differences between parasite densities in LF and DF hosts during infections ( LF versus DF on day 6 post infection , χ2 ( 3 , 5 ) = 0 . 66 , p = 0 . 42 , S3B Fig ) . This can be explained by both groups being mismatched to the SCN of the host , which we have previously demonstrated to have negative consequences for P . chabaudi [18] . Our previous work was carried out using P . chabaudi genotype AJ so is not directly comparable to our results presented here , because DK is a less virulent genotype [39] . Instead , a comparison of our results to data collected previously for genotype DK , in an experiment where SCN rhythms of donor and host mice were matched ( see Materials and Methods; infections were initiated with the same strain , sex , and age of mice , the same dose at ring stage ) reveals a cost of mismatch of donor and host entrainment . Specifically , parasite density on day 6 ( when infections have established but before parasites start being cleared by host immunity ) is significantly lower in infections mismatched to the SCN ( LF and DF ) compared to infections matched to the SCN ( χ2 ( 3 , 5 ) = 16 . 71 , p = 0 . 0002 , mean difference = 2 . 21e+10 parasites per ml blood ) ( see S4A Fig ) . In keeping with a difference in parasite replication , hosts with matched infections reach lower red blood cell densities ( χ2 ( 3 , 5 ) = 18 . 87 , p < 0 . 0001 , mean difference = 5 . 29e+08 red blood cells per ml blood ) . The mismatched and matched infections compared above also differ in whether hosts had food available throughout the 24-hour cycle or for 12 hours only ( LF and DF ) . Restricting food to 12 hours per day does not affect host weight ( S1 Fig ) and mice still undergo their main activity bout at lights off even when food is available all the time . Therefore , we propose that rather than feeding duration , mismatch to the host SCN for as few as 5 cycles is costly to parasite replication and reduces infection severity . Because peripheral and SCN driven rhythms are usually in synchrony , we suggest parasites use information from food-entrained oscillators , or metabolic processes , to ensure their development is timed to match the host’s SCN rhythms . Instead of organising their own rhythms ( i . e . using an “oscillator” whose time is set by a “Zeitgeber” or by responding directly to time-of-day cues ) , parasites may allow outputs of food-entrained host oscillators to enforce developmental rhythms . Previous studies have focused on rhythmic immune responses as the key mechanism that schedules parasite rhythms ( via developmental-stage and time-of-day specific killing [20 , 21] ) . Evidence that immune responses are rhythmic in naïve as well as infected hosts is increasing [15 , 16] , but the extent to which peripheral/food-entrained oscillators and the SCN drive immune rhythms is unclear . Nonetheless , we argue that rhythms in host immune responses do not play a significant role in scheduling parasites for the following reasons: First , mismatch to the host’s peripheral rhythms ( which occurs in DF mice but not LF mice as a feature of our experimental design ) does not cause a significant reduction in parasite number ( S3B Fig ) , demonstrating that stage-specific killing cannot cause the differently phased parasite rhythms in LF and DF mice . Second , while changing feeding time appears to disrupt some rodent immune responses [40 , 41] , effectors important in malaria infection , including leukocytes in the blood , do not entrain to feeding rhythms [42 , 43] . Third , inflammatory responses important for killing malaria parasites are upregulated within hours of blood stage infection [44] so their footprint on parasite rhythms should be apparent from the first cycles of replication [19] . In contrast , rhythms of parasites in LF and DF mice do not significantly diverge until 5–6 days post infection , after 5 replication cycles ( S3 Table , Fig 4 ) . Fourth , an additional experiment ( see Materials and Methods ) reveals that rhythms in the major inflammatory cytokines that mediate malaria infection ( e . g . IFN-gamma and TNF-alpha: [45 , 46 , 47 , 48] ) follow the phase of parasite rhythms ( Fig 5 ) , with other cytokines/chemokines also experiencing this phenomenon ( S5 Fig ) . Specifically , mice infected with P . chabaudi genotype AS undergoing schizogony at around midnight ( ZT17 ) , produce peaks in the cytokines IFN-gamma and TNF-alpha at ZT21 and ZT19 respectively ( following a significantly 24h pattern: IFN-gamma p = 0 . 0055 , TNF-alpha p = 0 . 0015 ) . Whereas mice infected with mismatched parasites undergoing schizogony around ZT23 ( 6 hours later ) , experience 3–6 hour delays in the peaks of IFN-gamma and TNF-alpha ( IFN-gamma: ZT0 , TNF-alpha: ZT1; following a significantly 24h pattern: IFN-gamma p = 0 . 0172 , TNF-alpha p = 0 . 0041 ) . Thus , even if parasites at different development stages differ in their sensitivity to these cytokines , these immune rhythms could only serve to increase synchrony in the parasite rhythm but not change its timing . More in-depth analysis of LF and DF infections provides further support that parasites actively organise their developmental rhythms . We examined whether parasites in DF mice maintain synchrony and duration of different developmental stages during rescheduling to the host’s SCN rhythms . Desynchronisation of oscillators manifests as a reduction in amplitude in rhythms that are driven by more than one oscillator ( e . g . parasite and host oscillator ) . No loss in amplitude suggests that parasites shift their timing as a cohort without losing synchrony . Parasite rhythms in LF and DF mice did not differ significantly in amplitude ( χ2 ( 6 , 7 ) = 1 . 53 , p = 0 . 22 , S4A Table ) and CoGs for sequential stages are equally spaced ( χ2 ( 10 , 18 ) = 11 . 75 , p = 0 . 16 , S2 Table ) demonstrating that parasite stages develop at similar rates in both groups . The rhythms of parasites in LF and DF mice were not intensively sampled until days 6–8 PI , raising the possibility that parasites lost and regained synchrony before this . Previously collected data for P . chabaudi genotype AS infections mismatched to the host SCN by 12 hours that have achieved a 6-hour shift by day 4 PI also exhibit synchronous development ( S4B Table and S6 Fig ) , suggesting that parasites reschedule in synch . That parasite rhythms do not differ significantly between LF and DF mice until day 5–6 post infection ( Fig 4 ) could be explained by the parasites experiencing a phenomenon akin to jet lag . Jet lag results from the fundamental , tissue-specific robustness of circadian oscillators to perturbation , which slows down the phase shift of individual oscillators to match a change in ‘time-zone’ [10] . We propose that the most likely explanation for the data gathered from our main experiment for genotype DK , and that collected previously for AJ and AS , is that parasites possess intrinsic oscillators that shift collectively , in a synchronous manner , by a few hours each day , until they re-entrain to the new ‘time-zone’ . Because there is no loss of amplitude of parasite rhythms , it is less likely that individual parasites possess intrinsic oscillators that re-entrain at different rates to the new ‘time-zone’ . The recently demonstrated ability of parasites to communicate decisions about asexual to sexual developmental switches [49] could also be involved in organising asexual development . If parasites have evolved a mechanism to keep time and schedule their rhythms , what external information might they synchronise to ? Despite melatonin peaks in lab mice being brief and of low concentration [50 , 51] , the host’s pineal melatonin rhythms have been suggested as a parasite time cue [52] . However , we can likely rule pineal melatonin , and other glucocorticoids , out because they are largely driven by rhythms of the SCN , which follow the light-dark cycle and have not been shown to phase shift by 12 hours as a result of perturbing feeding timing [25]; some glucocorticoid rhythms appear resistant to changing feeding time [53] . Whether extra-pineal melatonin , produced by the gut for example [54] , could influence the rhythms of parasites residing in the blood merits further investigation . Body temperature rhythms have recently been demonstrated as a Zeitgeber for an endogenous oscillator in trypanosomes [8] . Malaria parasites are able to detect and respond to changes in environmental temperature to make developmental transitions in the mosquito phase of their lifecycle [55 , 56] , and may deploy the same mechanisms to organise developmental transitions in the host . Body temperature rhythms did not fully invert in LF mice but they did exhibit unusually low ( i . e . day time ) temperatures at night . Thus , for body temperature to be a time-of-day cue or Zeitgeber it requires that parasites at early developmental stages ( e . g . rings or early trophozoites ) are responsible for time-keeping because they normally experience low temperatures during the day when the host is resting . The same logic applies to rhythms in locomotor activity because it is very tightly correlated to body temperature ( Pearson’s correlation R = 0 . 85 , 95% CI: 0 . 82–0 . 88 ) . Locomotor activity affects other rhythms , such as physiological oxygen levels ( daily rhythms in blood and tissue oxygen levels ) , which can reset circadian oscillators [57] and have been suggested as a time cue for filarial nematodes [4] . Feeding rhythms were inverted in LF and DF mice and so , the most parsimonious explanation is that parasites are sensitive to rhythms related to host metabolism and/or food-entrained oscillators . Malaria parasites have the capacity to actively alter their replication rate in response to changes in host nutritional status [58] . Thus , we propose that parasites also possess a mechanism to coordinate their development with rhythms in the availability of nutritional resources in the blood . Further work could explore whether parasites use information via the kinase ‘KIN’ to regulate their timing [58] . KIN shares homology with AMP-activated kinases ( AMPK ) , mammalian metabolic sensors implicated in both circadian timing and metabolic regulation [59] . Glucose , and other sugars that require metabolising , suppresses the activation of AMPK and its subsequent nutrient-sensing signalling cascade , with KIN proposed to act as a nutrient sensor to reduce parasite replication rate in response to calorie restriction during malaria infection [58] . Rhythms in blood glucose are a well-documented consequence of rhythms in feeding timing [60] and glucose is an important resource for parasites [61] . We performed an additional experiment to quantify blood glucose rhythms in ( uninfected ) LF and DF mice ( Fig 6A and 6B ) . Despite the homeostatic regulation of blood glucose , we find its concentration varies across the circadian cycle , and is borderline significantly rhythmic in DF mice ( p = 0 . 07 , peak time = ZT17 . 84 , estimated with CircWave ) and follows a significantly 24-hour pattern in LF mice ( p < 0 . 0001 , peak time = ZT8 . 78 ) . Glucose rhythms/patterns are shaped by feeding regime ( time-of-day: feeding treatment χ2 ( 18 , 32 ) = 45 . 49 , p < 0 . 0001 ) . Specifically , during the night , DF mice have significantly higher blood glucose than LF mice ( t = 3 . 41 , p = 0 . 01 , mean difference 20 . 6mg/dl±7 . 32 ) and there is a trend for LF mice to have higher blood glucose than DF mice during the day ( t = -0 . 94 , p = 0 . 78 , mean difference 7 . 9mg/dl±9 . 86 ) . Titrating whether glucose availability is high or low would only provide parasites with information on whether it is likely to be day or night , and a 12-hour window in which to make developmental transitions should erode synchrony , especially as glucose rhythms are weak in DF mice . Instead , parasites may use the sharp rise in blood glucose that occurs in both LF and DF mice after their main bout of feeding as a cue for dusk ( S5 Table; regions with solid lines connecting before and after feeding in Fig 6 ) , using KIN as a sensor [58] . In line with the effects of feeding timing we observe in mice , a recent study of humans reveals that changing feeding time can induce a phase-shift in glucose rhythms , but not insulin rhythms [43] . Alternatively , parasites may be sensitive to fluctuations in other factors due to rhythms in food intake , such as amino acids [62] or other rhythmic metabolites that appear briefly in the blood after feeding , changes in oxygen consumption , blood pressure or blood pH [63 , 64] . In summary , we show that peripheral , food-entrained host rhythms , but not central , light-entrained host rhythms are responsible for the timing of developmental transitions during the asexual replication cycles of malaria parasites . Taken together , our observations suggest that parasites have evolved a time-keeping mechanism that uses daily fluctuations in resource availability ( e . g . glucose ) as a time-of-day cue or Zeitgeber to match the phase of asexual development to the host’s SCN rhythms . Why coordination with the SCN is important remains mysterious . Uncovering how parasites tell the time could enable an intervention ( ecological trap ) to “trick” parasites into adopting suboptimal rhythms for their fitness . We compared the performance of parasites in our main experiment ( in which infections were initiated with parasites from donor mice that were mismatched to the host’s SCN rhythms by 12 hours ) , to the severity of infections when infections are initiated with parasites from donor mice that are matched to the host’s SCN rhythms . Twelve infections were established in the manner used in our main experiment ( eight-week-old male mice , strain MF1 , intravenously infected with 1 x 107 P . chabaudi DK parasitised RBC ) , except that donor SCN rhythms were matched to the experimental host’s SCN rhythm and hosts had access to food day and night . Densities of parasites were quantified from blood smears and RBC density by flow cytometry on day 6 and 9 PI , respectively . We chose to compare parasite density in matched infections to LF and DF infections on day 6 PI because parasites are approaching peak numbers in the blood ( before host immunity starts to clear infections ) and their high density facilitates accurate quantification when using microscopy . This experiment probes whether host immune responses mounted during the early phase of malaria infection could impose development rhythms upon parasites . We entrained N = 86 eight-week-old female mice , strain MF1 , to either a reverse lighting schedule ( lights on 7pm , lights off 7am , N = 43 ) or a standard lighting schedule ( lights on 7am , lights off 7pm , N = 43 ) . Donor mice , infected with P . chabaudi genotype AS , were entrained to a standard lighting schedule to generate infections matched and 12 hours mismatched relative to the SCN in the experimental mice . Mice were intravenously injected with 1 x 107 parasitised RBC at ring stage . Genotype AS has intermediate virulence [39] and was used to ensure immune responses were elicited by day 4 PI . We terminally sampled 4 mice every 3 hours over 30 hours starting on day 4 PI , taking blood smears , red blood cell counts and collecting plasma for Luminex cytokine assays . Cytokines were assayed by the Human Immune Monitoring Centre at Stanford University using mouse 38-plex kits ( eBiosciences/Affymetrix ) and used according to the manufacturer’s recommendations with modifications as described below . Briefly , beads were added to a 96-well plate and washed in a Biotek ELx405 washer . 60uL of plasma per sample was submitted for processing . Samples were added to the plate containing the mixed antibody-linked beads and incubated at room temperature for one hour followed by overnight incubation at 4°C with shaking . Cold and room temperature incubation steps were performed on an orbital shaker at 500–600 rpm . Following the overnight incubation , plates were washed as above and then a biotinylated detection antibody was added for 75 minutes at room temperature with shaking . Plates were washed as above and streptavidin-PE was added . After incubation for 30 minutes at room temperature a wash was performed as above and reading buffer was added to the wells . Each sample was measured as singletons . Plates were read using a Luminex 200 instrument with a lower bound of 50 beads per sample per cytokine . Custom assay control beads by Radix Biosolutions were added to each well . We staged the parasites from the blood smears collected from the infections used to assay cytokines ( above ) to investigate their synchrony during rescheduling . The infections from mismatched donor mice began 12 hours out of phase with the host SCN rhythms and the CoG for ring stage parasites reveals they had become rescheduled by 6 hours on day 4 PI . We focus on the ring stage as a phase marker–for the analysis of synchrony in these data and the divergence between LF and DF parasites–because rings are the most morphologically distinct , and so , accurately quantified , stage . In a third additional experiment , we entrained 10 eight-week-old male mice , strain MF1 , to a standard lighting schedule for 2 weeks before randomly allocating them to one of two feeding treatments . One group ( N = 5 ) were allowed access to food between ZT 0 and ZT 12 ( equivalent to the LF group in the main experiment ) and the other group ( N = 5 ) allowed access to food between ZT 12 and ZT 0 ( equivalent to the DF group ) . After 10 days of food restriction we recorded blood glucose concentration every 2 hours for 30 hours , using an Accu-Chek Performa glucometer . We used CircWave ( version 1 . 4 , developed by R . A . Hut; available from https://www . euclock . org ) to characterise host and parasite rhythms , and R v . 3 . 1 . 3 ( The R Foundation for Statistical Computing , Vienna , Austria ) for analysis of summary metrics and non-circadian dynamics of infection . Specifically , testing for rhythmicity , estimating CoG ( a reference point to compare circadian rhythms ) for host ( body temperature , locomotor activity , blood glucose concentration ) and parasite rhythms , and amplitude for parasite stage proportions , was carried out with CircWave for each individual infection . However , the cytokine data display high variation between mice ( due to a single sample from each mouse ) so we calculated a more robust estimate of phase than CoG by fitting a sine curve with a 24h period ( using CircWave ) and finding the maxima . Linear regression models and simultaneous inference of group means ( using the multcomp R package ) were run with R to compare summary measures that characterise rhythms , parasite performance , glucose concentration and disease severity . R was also used to construct and compared linear mixed effects models using which included mouse ID as a random effect ( to account for repeated measures from each infection ) to compare dynamics of parasite and RBC density throughout infections , and glucose concentration throughout the day . All procedures were carried out in accordance with the UK Home Office regulations ( Animals Scientific Procedures Act 1986; project licence number 70/8546 ) and approved by the University of Edinburgh . Euthanasia was performed using anaesthesia ( combination of Medetomidine and Ketamine ) followed by cervical dislocation and rigor mortis as confirmation of death .
How cycles of asexual replication by malaria parasites are coordinated to occur in synchrony with the circadian rhythms of the host is a long-standing mystery . We reveal that rhythms associated with the time-of-day that hosts feed are responsible for the timing of rhythms in parasite development . Specifically , we altered host feeding time to phase-shift peripheral rhythms , whilst leaving rhythms driven by the central circadian oscillator in the brain unchanged . We found that parasite developmental rhythms remained synchronous but changed their phase , by 12 hours , to follow the timing of host feeding . Furthermore , our results suggest that parasites themselves schedule rhythms in their replication to coordinate with rhythms in glucose in the host’s blood , rather than have rhythms imposed upon them by , for example , host immune responses . Our findings reveal a novel relationship between hosts and parasites that if disrupted , could reduce both the severity and transmission of malaria infection .
You are an expert at summarizing long articles. Proceed to summarize the following text: Differentiation of the fish-borne trematodes belonging to the Opisthorchiidae , Heterophyidae and Lecithodendriidae is important from a clinical and epidemiological perspective , yet it is impossible to do using conventional coprological techniques , as the eggs are morphologically similar . Epidemiological investigation therefore currently relies on morphological examination of adult worms following expulsion chemotherapy . A PCR test capable of amplifying a segment of the internal transcribed spacer region of ribosomal DNA for the opisthorchiid and heterophyid flukes eggs taken directly from faeces was developed and evaluated in a rural community in central Thailand . The lowest quantity of DNA that could be amplified from individual adults of Opisthorchis viverrini , Clonorchis sinensis and Haplorchis taichui was estimated at 0 . 6 pg , 0 . 8 pg and 3 pg , respectively . The PCR was capable of detecting mixed infection with the aforementioned species of flukes under experimental conditions . A total of 11 . 6% of individuals in rural communities in Sanamchaikaet district , central Thailand , were positive for ‘Opisthorchis-like’ eggs in their faeces using conventional parasitological detection techniques . In comparison to microscopy , the PCR yielded a sensitivity and specificity of 71 . 0% and 76 . 7% , respectively . Analysis of the microscopy-positive PCR products revealed 64% and 23% of individuals to be infected with O . viverrini and C . sinensis , respectively . The remaining 13% ( three individuals ) were identified as eggs of Didymozoidae , presumably being passed mechanically in the faeces following the ingestion of infected fishes . An immediate finding of this study is the identification and first report of a C . sinensis–endemic community in central Thailand . This extends the known range of this liver fluke in Southeast Asia . The PCR developed herein provides an important tool for the specific identification of liver and intestinal fluke species for future epidemiological surveys . It is estimated that approximately 17 million people are currently infected with fish-borne trematodes worldwide [1] . In Asia , Opisthorchis viverrini is known to occur in Thailand , Laos , Cambodia and southern Vietnam and Clonorchis sinensis in Korea , China , Taiwan and northern Vietnam [2] , [3] . Liver fluke infection in Thailand is unevenly distributed with a highly endemic focus of infection in the northeast region [4] . Previous parasite surveys have mostly focussed on these communities and frequently found infection with O . viverrini mixed with minute intestinal flukes of the Heterophyidae and Lecithodendriidae [5] , [6] . The heterophyids Haplorchis taichui and less frequently H . pumilio are the most common minute intestinal flukes recovered . Microscopic examination of faecal samples for the presence of eggs using the formalin-ether concentration technique ( FECT ) is currently considered the most sensitive and reliable method for screening liver and intestinal flukes and is therefore the most widely employed technique for fluke parasite surveys [7] . This technique is limited by its capacity to differentiate between the Opisthorchiidae , Heterophyidae and Lecithodendriidae , which have similar egg morphologies . Eggs can therefore only be characterised as ‘Opisthorchis/Clonorchis- like’ [5] , [6] , but no further . A definitive diagnosis to species level requires morphological identification of adult flukes following expulsion chemotherapy [8] , [9] . The ability to differentiate the species of liver and minute intestinal flukes is important from both a clinical and epidemiological perspective . Heavy infections with the minute intestinal flukes are associated with diarrhoea , mucus-rich faeces , dyspepsia , nausea and vomiting [10] , whereas infections with the liver flukes result in mostly biliary and hepatic disease . The frequency and types of pathology and clinical disease among C . sinensis and O . viverrini also seem to differ [7] . For example , cholelithiasis is one of the more serious complications of clonorchiasis , but a rare complication of opisthorchiasis . Although both flukes are implicated as predisposing factors for cholangiocarcinoma , this is more frequent with O . viverrini . From an epidemiological perspective , C . sinensis has a wider definitive host range than O . viverrini [11] , [12] which makes control more challenging . To overcome the diagnostic limitations associated with conventional parasitological methods , a number of PCR-based techniques capable of amplifying species of flukes directly from eggs in faeces have been developed [13]–[16] . An O . viverrini-specific PCR test capable of detecting O . viverrini eggs directly from human faeces was shown to have an analytical sensitivity of 100% , 68 . 2% and 50% compared to the Stoll's egg count containing >1000 , 200 to 1000 and <200 eggs per g of faeces respectively and an analytical sensitivity of 97 . 8% under experimental conditions [16] . This PCR assay proved less reliable however , once evaluated under field conditions with an overall diagnostic sensitivity of 45% compared to the FECT [17] . PCR tests based on amplification of the mitochondrial gene for the identification and discrimination of C . sinensis and O . viverrini [13] and C . sinensis , O . viverrini and H . taichui [15] have been developed and shown to be analytically sensitive under experimental conditions . Amplicons for the targeted fluke species could be obtained in reactions containing 0 . 78 ng of genomic DNA [13] and 10−4 ng of genomic DNA [15] , however the assays have yet to be evaluated and compared to conventional parasitological methods in the field . Here we developed a PCR test capable of specifically amplifying a segment of the internal transcribed spacer ( ITS-2 ) region of ribosomal DNA ( rDNA ) from opisthorchiid and heterophyid flukes directly from eggs in faeces . The ITS-2 has successfully discriminated species from many digenean families and has become the default region of choice for distinguishing species of trematodes [18] . This PCR test is evaluated in terms of both analytical and diagnostic sensitivity and specificity against conventional parasitological methods in a community endemic for liver fluke infection in central Thailand . This study is also the first to demonstrate the occurrence of a C . sinensis endemic community in Thailand . A rural community consisting of a total population of approximately 5465 people in Nayao village , Sanamchaikaet District , Chachoengsao Province , 150 km east of Bangkok was chosen for this cross-sectional study . The area lies in a low basin of land which is suitable for cultivation of rice which provides the principle income for the province . The dietary habit of eating raw and fermented fish dishes such as ‘koi pla’ , ‘pla som’ and ‘pla ra’ from fish sourced at local ponds is popular among residents of this community . Houses were chosen at random and household members informed of the study by medical students from the Phramongkutklao College of Medicine . After signing human ethics consent forms , single stool samples were collected from a total of 335 individuals of all ages , gender and backgrounds during a 10-day period in mid November 2004 . Participants found positive for gastrointestinal parasites received free anthelmintic treatment from the medical doctors on the research team in order to increase compliance . All samples were qualitatively evaluated and run in parallel for the presence of Opisthorchis-like eggs using a direct faecal smear ( DFS ) , Kato Katz ( KK ) technique and the FECT by experienced parasitology technicians from the Phramongkutklao College of Medicine in the field . Any remaining faecal material was fixed in 20% dimethylsulfoxide ( DMSO ) saturated with salt for transport to the University of Queensland for molecular testing . A single individual found positive for ‘Opisthorchis-like’ eggs in their faeces was treated with a single dose of praziquantel ( 40 mg per kg ) and was then given 30 g magnesium sulfate with as much water as possible to facilitate expulsion of adult flukes . Whole diarrhoetic stools were collected and washed several times before isolating the adult flukes [9] . Adult flukes that had been expelled by this individual were fixed in 70% ethanol for molecular and morphological identification at the University of Queensland . Morphological identification was performed by staining the adult fluke with haematoxylin , dehydrating it in alcohol and clearing it in methyl salicylate before mounting in Canada balsam . This study was approved by the Murdoch University Human and Animal Ethics Committees of Western Australia and the Ethical Committee , the Medical Department Royal Thai Army . Adult flukes of O . viverrini , C . sinensis and H . taichui were extracted using the Qiagen DNeasy Blood and Tissue Kit according to manufacturer's instructions . Those faecal samples found microscopically positive for ‘Opisthrochis-like’ eggs using at least one parasitological test and where sufficient quantities of stool remained , were subjected to DNA extraction and PCR ( n = 31 ) . In addition , 30 faecal samples negative for ‘Opisthorchis-like’ eggs by microscopy were also randomly selected and subjected to DNA extraction and PCR . All PCR reactions were conducted by a single experienced molecular biologist that was blind to the results of the parasitological test results for each sample . It was observed that subjecting ‘Opisthorchis-like’ eggs purified by a saturated salt and glucose gradient to freezing in liquid nitrogen followed by thawing them at 98–100°C resulted in the eggs ‘disintegrating’ to release genomic DNA . Two hundred milligrams of faeces were suspended in 1 . 4 ml ATL tissue lysis buffer ( Qiagen , Hilden , Germany ) and this suspension subjected to 5 cycles of freezing-thawing at liquid nitrogen temperatures . DNA was then isolated from the supernatant using the QIAamp DNA Mini Stool Kit according to manufacturer's instructions . Final elutions of DNA were made in 50 µl of elution buffer instead of 200 µl as recommended by the manufacturer . Sequences of the ITS-2 region of C . sinensis , O . viverrini , O . felineus , H . taichui , H . pumilio and Centrocestus sp . ( GenBank accession nos . EF688144 , EF688143 , AY584735 , DQ513403 , DQ513405 , AY245705 , AY245706 , AY245699 ) were aligned using Clustal W ( http://align . genome . jp/ ) and the primer pair: RTFlukeFa 5′CTTGAACGCACATTGCGGCC-3′ and RTFlukeRa 5′-CACGTTTGAGCCGAGGTCAG-3′ were designed to amplify a 375 bp , 381 bp and 526 bp region of O . viverrini , C . sinensis and H . taichui , respectively , The PCR primers , were also designed with the potential to amplify other species of opisthorchiid and heterophyid flukes . The PCR assay was carried out in a volume of 20 µl containing 1×PCR buffer from Qiagen ( Tris-HCl , KCl , ( NH4 ) 2SO4 , 1 . 5 mM MgCl2; pH 8 . 7 ) additional MgCl2 to give a final 2 . 0 mM concentration , 200 µM of each dNTP , 0 . 25 µM of each primer , and 1 unit Hot Star Taq DNA polymerase ( Qiagen ) . The PCR cycle consisted of an initial stage: 94°C for 15 min , 60°C for 1 min and 72°C for 2 min followed by 35 cycles of 94°C for 30 sec , 60°C for 30 sec , 72°C for 30 sec , a final extension at 72°C for 7 min and a holding temperature of 12°C . PCR products were run on 1 . 5% agarose in 1×TAE buffer at 150V in a Biorad electrophoresis system and were purified using Qiagen spin columns ( Qiagen ) prior to sequencing . Where a multi-banded product was obtained , target bands were excised , frozen and cleaned up with a Quantum Prep Freeze ‘N Squeeze DNA Gel Extraction spin column ( Biorad ) or a Qiaquick Gel Extraction kit ( Qiagen ) . Sequencing was done using an ABI 3130xl Genetic Analyzer ( Applied Biosystems ) using Big Dye 3 . 0 chemistry , after which sequences were edited and assembled using Chromas Pro ( Technelysium Pty Ltd ) . Titration experiments were conducted to determine the analytical sensitivity of the PCR for the detection of C . sinensis , O . viverrini and H . taichui DNA . The assay's ability to detect artificially mixed infections with varying ratios of C . sinensis and O . viverrini with H . taichui were also assessed . Assuming microscopy as the ‘gold standard’ , the diagnostic sensitivity , and specificity together with their 95% confidence intervals were calculated for the PCR using the Wilson method . The assay's ability to detect artificially mixed infections of O . viverrini and C . sinensis was assessed by development of a PCR-RFLP as both species produced PCR products that could not be differentiated by size . Amplified ITS-2 products of RTFlukeFa – RTFlukeRa for C . sinensis and O . viverrini were digested with AcuI ( New England Biolabs ) . According to the restriction profile generated by Nebcutter V2 . 0 ( New England Biolabs ) , O . viverrini does not possess a restriction site for AcuI and remains uncut ( 375 bp ) , whereas C . sinensis has a single AcuI site and gives rise to two bands at 286 bp and 95 bp . Ten microlitres of PCR product were digested with 2 . 5 units of the restriction endonuclease AcuI ( New England Biolabs ) at 37°C for 3 hours in a volume of 20 µl . Using the primer pair RTFlukeFa and RTFlukeRb , DNA from morphologically identified adults of O . viverrini , C . sinensis and H . taichui gave specific products of 375 bp , 381 bp and 526 bp respectively . The lowest quantity of DNA that could be amplified from individual adults of O . viverrini , C . sinensis and H . taichui was estimated at 0 . 6 pg , 0 . 8 pg and 3 pg respectively . Appropriate sized amplicons were produced in reactions artificially mixing DNA of C . sinensis and O . viverrini separately , with H . taichui , in ratios of 1∶1 , 1∶2 , 1∶3 , 3∶1 and 2∶1 ( Fig . 1A and 1B ) . The PCR however , preferentially amplified O . viverrini when artificially mixed with H . taichui . Weak to negligible bands of H . taichui were produced when mixed in ratios of less than 1∶1 with O . viverrini ( Fig . 1B ) . The PCR-RFLP patterns for differentiating and detecting mixed infections of O . viverrini and C . sinensis are displayed ( Fig . 1C ) . The PCR-RFLP was successful at detecting artificially mixed infections of O . viverrini and C . sinensis in ratios of 1∶1 , 1∶2 . 1∶3 , 3∶1 and 2∶1 . For a diagrammatic guide to the study design and summary of diagnostic results refer to Fig . 2 . A total of 39 ( prevalence 11 . 6% , 95% CI , 8 . 6% , 14 . 92% ) individuals were found positive for ‘Opisthrochis-like’ eggs in their faeces using a combination of all three microscopic techniques ( DFS , KK and FECT ) . The FECT detected ‘Opisthorchis-like’ eggs in more faecal samples ( 25/31 ) than the DFS ( 9/31 ) and KK ( 10/31 ) methods . Using primer pair RTFlukeFa and RTFlukeRb , PCR-positive samples derived from DNA extracted directly from faeces produced a single product corresponding to the expected amplicon size for O . viverrini and C . sinensis ( approximately 380 bp ) . In three cases , non-specific amplicons were produced in addition to the target PCR product , however these amplicons were too weak ( faint ) to subject to DNA sequencing . The results of the PCR analysis of 31 microscopy positive and 30 microscopy negative samples are presented in Table 1 . The PCR test , when compared to the combined microscopy results yielded a sensitivity of 71 . 0% ( 95% CI , 53 . 4% , 83 . 9% ) , and specificity of 76 . 7% ( 95% CI , 59 . 1% , 88 . 2% ) . PCR detected an additional seven samples positive for liver fluke that were negative by microscopy . Mixed infections of O . viverrini and C . sinensis were detected in a single individual by PCR-RFLP . Morphological and genetic characterisation of the fluke species expelled by the human participant Adult fluke specimens isolated from a single human participant in the community were identified by morphology as Clonorchis sinensis [19] . Two adult fluke specimens subjected to PCR demonstrated 100% DNA sequence homology to the ITS-2 region of C . sinensis isolates from Japan and Russia ( GenBank accession nos . EF688144 and EF688143 ) . Phenogram construction of the ITS-2 region of the flukes using the neighbour-joining algorithm and maximum parsimony ( Fig . 3 ) , produced strong bootstrap support for the placement of 15 PCR-positive samples within a single clade corresponding to O . viverrini ( GenBank accession number AY584735 ) and 11 PCR-positive samples corresponding to C . sinensis ( GenBank accession nos . EF688144 , EF688143 ) . Mixed infections with fluke species were not observed by sequencing of the PCR product . Of the 22 individuals found positive for ‘Opisthorchis-like’ eggs by both microscopy and by PCR , 14 ( 64% ) were characterised as O . viverrini and five ( 23% ) as C . sinensis . In addition , three samples ( 13% ) microscopy positive for ‘Opisthorchis-like’ eggs produced amplicon sizes of approximately 410 bp each and upon sequencing , were genetically similar to the didymozoids ( parasites of fishes ) , Rhopalotrema elusiva ( GenBank accession no . AJ224759 ) and Indodidymozoon sp . ( GenBank accession no . AJ224754 ) . Six of the microscopy negative but PCR positive samples were genetically characterised as C . sinensis and a single sample as O . viverrini . No intra-species variation was observed for the O . viverrini isolates obtained from this community relative to those obtained from northeast Thailand ( positive control and published GenBank isolate AY584735 ) . Apart from three isolates of C . sinensis obtained from this community that differed by a transition at a single base , all other isolates of C . sinensis were identical to published ITS-2 sequences of C . sinensis from Japan ( GenBank accession no . EF688144 ) and Russia ( GenBank accession no . EF688143 ) . A significant finding of this present study was the identification and first report of a community endemic for C . sinensis in central Thailand . It is possible that the humans in this community were infected by imported fish or visited C . sinensis endemic areas , however when questioned about this , villagers reported only eating fish caught in local ponds or from local village markets and had no history of travelling outside Thailand . Previous studies have only reported C . sinensis in Korea , China , Taiwan , Japan , northern Vietnam and the far eastern part of Russia [20] . It is hypothesised that the geographical distribution of clonorchiasis closely parallels the distribution of the snail intermediate host [20] , however this assumption may not be as simple as previously thought . Species of Parafossarulus and Bithynia are most commonly reported to act as first intermediate hosts for C . sinensis . The important species in China , Korea and Japan is Parafossarulus manchouricus and P . anamalospiralis [7] . Other susceptible snails in China are reported as Bithynia fuchsiana , B . longicornis , Melanoides tuberculata and Assiminea lutea [20] . In Thailand , Bithynia siamensis goniomphalos , B . s . funiculate and B . s . siamensis act as hosts for O . viverrini [19] . In a recent survey of freshwater mollusks in Thailand , an intermediate host of C . sinensis , Melanoides tuberculata was isolated in the provinces to the south ( Chanthaburi ) and north ( Nakhon Ratchasima Province ) of our study area [21] . It is possible that this species of snail may be acting as the natural intermediate host of C . sinensis in Thailand . If this is true , then C . sinensis may be as geographically widespread as O . viverrini in Thailand , reflecting the geographical distribution of M . tuberculata , which was isolated from 9/15 districts sampled in the north , east and central regions of Thailand [21] . In saying this however , M tuberculata has been shown to harbour both C . sinensis and O . viverrini in both northern and southern regions of Vietnam , yet surveys to date have found C . sinensis to be restricted to the northern provinces and O . viverrini to the southern provinces [2] . The distribution of potential snail intermediate hosts therefore does not necessarily reflect the distribution of the liver flukes in Southeast Asia . The PCR test developed in this study provides a useful diagnostic tool for further epidemiological surveys to determine the distribution of these liver flukes in human and intermediate hosts . The PCR test developed in this study is capable of amplifying O . viverrini , C . sinensis and potentially the minute intestinal flukes , directly from eggs in faeces . In terms of test parameters , this assay demonstrated a superior sensitivity ( Se ) to the PCR developed by Stensvold et al . ( 2006 ) in the field ( Se = 70 . 9% compared to Se of 45 . 0% ) . It also has the added advantage of being able to amplify fluke species other than O . viverrini . It may be likely that the presence of faecal inhibitors and/or the unsuccessful ‘cracking open’ of these highly resistant eggs during DNA extraction accounted for the false negative results produced by the PCR in this study . The overall specificity ( Sp ) of the PCR evaluated using microscopy negative field samples were inferior to those reported by Stensvold et al . ( 2006 ) ( Sp = 76 . 7% , compared to Sp: 90 . 0% ) , however these assumed ‘false positive’ samples were being compared to the microscopy results ( DFS , KK , FECT ) which are in themselves not ‘gold standards’ . DNA sequences generated from the PCR products of these samples were characterised as either O . viverrini or C . sinensis and therefore the specificity of this PCR may be under-estimated . Three faecal samples microscopy positive for ‘Opisthorchis-like’ eggs were sequenced and identified as being close to the didymozoids Rhopalotrema elusiva and Indodidymozoon sp . This is not the first time that eggs of didymozoid flukes have been recovered in human faecal samples [22] . Flukes belonging to the Didymozoidae parasitize a wide range of species of marine fish and ingestion of these adult flukes by humans during the consumption of fish results in the mechanical passage of the relatively thick-shelled eggs into the faeces . Because of their dimensions ( 35–43×12–28 µm ) and morphology ( oval , operculate ) of the eggs of didymozoid flukes , they can easily be confused with eggs of the Opisthorchiidae , Heterophyidae and Lecithodendriidae . This added confusion may result in further inaccuracies when estimating the prevalence of liver and intestinal flukes in a community using conventional parasitological procedures alone . The apparent absence of H . taichui in the Sanamchaikaet district community was surprising given it is reported commonly in the northeast region of Thailand . It is possible that the PCR failed to amplify eggs from faecal samples with mixed infections of H . taichui and O . viverrini . Under experimental conditions , the PCR showed good analytical sensitivity for detecting H . taichui as a single infection and also when artificially mixed with C . sinensis , but failed to amplify a strong band when artificially mixed with O . viverrini . Since small intestinal flukes have commonly been found as mixed infections with liver fluke species [5] , [23] , the PCR developed in this study may not be successful at detecting these infections . In conclusion , we present data to demonstrate for the first time in Thailand a community endemic for C . sinensis infection . This significant finding undoubtedly opens a new chapter for further research into investigating the distribution , and prevalence of C . sinensis in Thailand and determining the natural intermediate host species capable of supporting its life cycle . Furthermore , the PCR described herein provides a valuable tool for screening and determining the species of liver and intestinal flukes in epidemiological surveys .
It is estimated that approximately 17 million people are currently infected with fish-borne flukes worldwide . The fish-borne liver flukes Opisthrochis viverrini and Clonorchis sinensis cause hepatic and biliary disease in humans . The minute intestinal flukes are widely distributed in southeast Asia and are increasingly recognised as an emerging pathogen associated with diarrhoea and gastritis . The most significant finding of this study is the discovery and first report of a C . sinensis–endemic community in Thailand . This finding was aided by the development and application of a new PCR-based technique capable of specifically detecting and characterising O . viverrini , C . sinensis and the minute intestinal flukes , directly from eggs in faeces . Since the eggs are morphologically similar , the fish-borne flukes cannot be differentiated on basis of microscopic examination of stool . This publication also questions the presumption that the distribution of fish-borne liver fluke species in Asia closely parallels the distribution of the snail intermediate hosts . The PCR provides a useful diagnostic tool for further large-scale epidemiological surveys to be carried out in Southeast Asia , which will shed further light on the distribution of these liver flukes in human and snail intermediate hosts with the advantage that targets for more arduous anthelmintic flushing confirmations can be carried out .
You are an expert at summarizing long articles. Proceed to summarize the following text: The timing of DNA synthesis , mitosis and cell division is regulated by a complex network of biochemical reactions that control the activities of a family of cyclin-dependent kinases . The temporal dynamics of this reaction network is typically modeled by nonlinear differential equations describing the rates of the component reactions . This approach provides exquisite details about molecular regulatory processes but is hampered by the need to estimate realistic values for the many kinetic constants that determine the reaction rates . It is difficult to estimate these kinetic constants from available experimental data . To avoid this problem , modelers often resort to ‘qualitative’ modeling strategies , such as Boolean switching networks , but these models describe only the coarsest features of cell cycle regulation . In this paper we describe a hybrid approach that combines the best features of continuous differential equations and discrete Boolean networks . Cyclin abundances are tracked by piecewise linear differential equations for cyclin synthesis and degradation . Cyclin synthesis is regulated by transcription factors whose activities are represented by discrete variables ( 0 or 1 ) and likewise for the activities of the ubiquitin-ligating enzyme complexes that govern cyclin degradation . The discrete variables change according to a predetermined sequence , with the times between transitions determined in part by cyclin accumulation and degradation and as well by exponentially distributed random variables . The model is evaluated in terms of flow cytometry measurements of cyclin proteins in asynchronous populations of human cell lines . The few kinetic constants in the model are easily estimated from the experimental data . Using this hybrid approach , modelers can quickly create quantitatively accurate , computational models of protein regulatory networks in cells . The cell division cycle is the fundamental physiological process by which cells grow , replicate , and divide into two daughter cells that receive all the information ( genes ) and machinery ( proteins , organelles , etc . ) necessary to repeat the process under suitable conditions [1] . This cycle of growth and division underlies all biological expansion , development and reproduction . It is highly regulated to promote genetic fidelity and meet the demands of an organism for new cells . Altered systems of cell cycle control are root causes of many severe health problems , such as cancer and birth defects . In eukaryotic cells , the processes of DNA replication and nuclear/cell division occur sequentially in distinct phases ( S and M ) separated by two gaps ( G1 and G2 ) . Mitosis ( M phase ) is further subdivided into stages: prophase ( chromatin condensation , spindle formation , and nuclear envelope breakdown ) , prometaphase ( chromosome attachment and congression ) , metaphase ( chromosome residence at the mid-plane of the spindle ) , anaphase ( sister chromatid separation and movement to opposite poles of the spindle ) , telophase ( re-formation of the nuclear envelopes ) , and cytokinesis ( cell division ) . G1 phase is subdivided into uncommitted and committed sub-phases , often referred to as G1-pm ( postmitotic interval ) and G1-ps ( pre S phase interval ) , separated by the ‘restriction point’ [2] . In this paper , we shall refer to the sub-phases G1-pm and G1-ps as ‘G1a’ and ‘G1b’ respectively . Progression through the correct sequence of cell-cycle events is governed by a set of cyclin-dependent kinases ( Cdk's ) , whose activities rise and fall during the cell cycle as determined by a complex molecular regulatory network . For example , cyclin synthesis and degradation are controlled , respectively , by transcription factors and ubiquitin-ligating complexes whose activities are , in turn , regulated by cyclin/Cdk complexes . Current models of the Cdk control system can be classified as either continuous or discrete . Continuous models track the changes of protein concentrations , Cj ( t ) for j = 1 , 2 , … , N , by solving a set of nonlinear ordinary differential equations ( ODEs ) of the form: ( 1 ) where ρr is the rate of the rth reaction and νir is the stoichiometric coefficient of species i in reaction r . To each rate term is associated one or more kinetic constants that determine exactly how fast the reaction proceeds under specific conditions . These kinetic constants must be estimated from experimental data , and often there is insufficient kinetic data to determine their values . Nonetheless , continuous models , based on rate equations , have been used successfully to account for the properties of cell proliferation in a variety of cell types: yeast [3] , [4] , [5] , fruit fly [6] , frog egg [7] , [8] , and cultured mammalian cells [9] , [10] , [11] . They have also proved successful in predicting novel cell-cycle characteristics [12] , [13] . Discrete models , on the contrary , represent the state of each regulatory protein as Bj ( τ ) = 0 or 1 ( inactive or active ) , and the state variables update from one discrete time step to the next ( τ = 0 , 1 , 2 , … = ticks of a metronome ) according to the rule: ( 2 ) where Ψj ( … ) is a Boolean function ( i . e . , it equates to either 0 or 1 ) determined by the topology of the reaction network . For Boolean networks ( BNs ) there is no notion of reaction ‘rate’ and , hence , no need to estimate kinetic constants . BN models of the Cdk regulatory network have been proposed for yeast cells [14] , [15] and for mammalian cells [16] . They have been used to study notions of ‘robustness’ of the cell cycle , but they have not been compared in detail to quantitative properties of cell cycle progression , and they have not been used as predictive tools . In this paper we propose to combine the strengths of both continuous and discrete modeling , while avoiding the weaknesses of each . Our ‘hybrid’ model is inspired by the work of Li et al . [14] , who proposed a BN for cell cycle controls . Their model employs 11 state variables that move around in a space of 211 = 2048 possible states . Quite remarkably they found that 1764 of these states converge quickly onto a ‘super highway’ of 13 consecutive states that represent a typical cell cycle trajectory ( G1b—S—G2—M—G1a ) . The results of Li et al . indicate that the cell cycle control network is ‘robustly designed’ in the sense that even quite large perturbations away from the usual sequence of cell cycle states are quickly restored to the super highway . In the model of Li et al . , G1a is a stable steady state; they do not address the signals that drive cells past the restriction point ( the G1a-to-G1b transition ) . Despite their intuitive appeal , Boolean models have severe limitations . First of all , metronomic time in BN's is unrelated to clock time in the laboratory , so Boolean models cannot be compared to even the most basic observations of time spent by cells in the four phases of the division cycle [1] . Also , these models do not incorporate cell size , so they cannot address the evident importance of cell growth in driving events of the cell cycle [17] , [18] , [19] . Lastly , cyclins are treated as either absent or present ( 0 or 1 ) , so Boolean models cannot simulate the continuous accumulation and removal of cyclin molecules at different stages of the cell cycle [20] . Our goal is to retain the elegance of the Boolean representation of the switching network , while introducing continuous variables for cell size , cell age , and cyclin composition , in order to create a model that can be compared in quantitative detail to experimental measurements with a minimal number of kinetic parameters that must be estimated from the data . To this end , we keep the cyclin regulators as Boolean variables but model the cyclins themselves as continuous concentrations that increase and decrease due to synthesis and degradation . Next , we replace the Boolean model's metronome with real clock time to account for realistic rates of cyclin synthesis and degradation , and for stochastic variability in the time spent in each Boolean state of the model . Finally , we introduced a cell size variable , M ( t ) , which affects progression through late G1 phase . M ( t ) increases exponentially with time as the cell grows and decreases by a factor of ∼2 when the cell divides . ( The assumption of exponential growth is not crucial; similar results are obtained assuming linear growth between cell birth and division . ) Since the pioneering work of Leon Glass [21] , [22] , hybrid ( discrete-continuous ) models have been employed by systems biologists in a variety of forms and contexts [23] , [24] , [25] . Engineers have been modeling hybrid control systems for many years [26] , [27] , [28] , and they have created powerful simulation packages for such systems [29]: SIMULINK [28] , SHIFT [30] , [31] and CHARON [32] , to name a few . We have not used these simulation packages because our model can be solved analytically . The modeling approach we are proposing is hybrid in two senses . First , we employ both continuous and discrete variables , and second we allow for both deterministic and stochastic processes . Concerning the components of the control system , we track cyclin levels as continuous concentration variables , but we use discrete Boolean variables to represent the activities ( ‘on’ or ‘off’ ) of the regulatory proteins ( transcription factors and ubiquitinating enzymes ) that control cyclin synthesis and degradation . This distinction is equivalent to a presumed ‘separation of time scales’: the activities of the regulatory proteins change rapidly between 0 and 1 , while the concentrations of cyclins change more slowly due to synthesis and degradation . The Boolean variables , we assume , proceed from one state to the next according to a fixed sequence corresponding roughly to the super highway of Li et al . [14] . The time spent in each state , however , is not a ‘tick’ of the metronome but rather the sum of a deterministic execution time ( which may be 0 ) plus a random , exponentially distributed waiting time . In this sense , the model combines deterministic and stochastic processes . In its present version , our model is not fully autonomous . The discrete variables do not update according to Boolean functions of the current state of the network . Rather , they go through a fixed sequence of states predetermined by the Boolean network model of Li et al . [14] . The discrete variables determine the rates of synthesis and degradation of the continuous variables ( the cyclins ) , and the cyclins feedback on the discrete variables by determining how much time is spent in some of the Boolean states . This strategy keeps the model simple and is appropriate for the cases , considered in this paper , of unperturbed cycling of ‘wild type’ cells , which travel serenely along the super highway of Li et al . To consider more complicated cases , of mutant cells that travel a different route through discrete state space or of cells that are perturbed by drugs or radiation , we will have to elaborate on this basic model with additional rules governing the interactions of the discrete and continuous variables . We are currently working on alternative strategies to adapt this basic modeling paradigm to more complex situations . Our model ( Fig . 1 ) tracks three cyclin species ( A , B and E ) , two transcription factors ( ‘TFE’ and ‘TFB’ ) and two different E3 ubiquitin-ligase complexes ( APC-C and SCF ) . TFE drives the synthesis of cyclins E and A early in the cell cycle ( comparable to the E2F family of transcription factors ) [33] , and TFB drives the synthesis of cyclins B and A late in the cell cycle ( comparable to FoxM1 and Myc ) [34] , [35] . The Anaphase Promoting Complex—Cyclosome ( APC-C ) is active during M phase and early G1 , when it combines with Cdc20 and Cdh1 to label cyclins A and B for degradation by proteasomes . We make a further distinction between Cdc20 activity on cyclin A ( Cdc20A , active throughout mitosis ) from Cdc20 activity on cyclin B ( Cdc20B , activated at anaphase ) . The SCF labels cyclin E for degradation via ubiquitination , but only when cyclin E is phosphorylated [36] , which we assume is correlated primarily with cyclin A/Cdk2 activity [37] . In our model , the two transcription factors and the four ubiquitination factors are each represented by a Boolean variable , BTFE , etc . For each cyclin component we write an ordinary differential equation , d[CycX]/dt = ksx−kdx[CycX] , where the rate ‘constants’ for synthesis and degradation , ksx and kdx , depend on the Boolean variables ( see Table 1 ) . Hence , each cyclin concentration is governed by a piecewise linear ODE . The parameters in the model ( , , etc . ) are assigned numerical values ( Table 1 ) , chosen to fit observations of how fast cyclins accumulate and disappear during different phases of the cell cycle . Next , we must assign rules for updating the Boolean variables in the model . We assume that the Boolean variables follow a strict sequence of states ( see Table 1 ) that corresponds roughly to the super highway discovered by Li et al . [14] . This sequence of states conforms to current ideas of how the mammalian cell cycle is regulated . Newborn cells are said to be in ‘G1a’ state , because they are not yet committed to a new round of DNA synthesis and mitosis . The transcription factors , TFE and TFB , are silent , and Cdh1/APC-C is active , so the levels of cyclins A , B and E are low in newborn cells . For a mammalian cell to leave the G1a state and commit to a new round of DNA replication and division , it must receive a specific set of extracellular signals ( growth factors , matrix binding factors , etc . ) , which up-regulate the activity of TFE . We assume that these ‘proliferation signals’ are present and that our ( simulated ) cell spends only a few hours in G1a before transiting into G1b . In our model , the time spent in G1a is an exponentially distributed random variable with mean = 2 h . When the cell passes the ‘restriction point’ and enters G1b , TFE is activated and CycE begins to accumulate . Among other chores , Cdk2/CycE inactivates Cdh1/APC-C , allowing Cdk2/CycA dimers to accumulate . In our model , the transition from early G1b to late G1b is weakly size dependent , because the condition for this transition is that [CycE]*Mass exceeds a certain threshold ( θE ) . Because this transition depends on cell mass , those cells that are larger than average tend to make the transition sooner , and cells that are smaller than average tend to make the transition later . This effect allows the cell population to achieve a stable size distribution . In the late G1b state , CycA/Cdk2 level rises to a certain threshold ( θA ) , when it triggers entry into S phase . Cdk2/CycA also promotes the degradation of cyclin E by SCF during S phase . We assume that DNA synthesis requires at least 7 h . Cyclin B begins to accumulate in late G1 and S , after Cdh1 is inactivated , but the major accumulation of cyclin B protein occurs in G2 phase , after DNA synthesis is completed and TFB is activated . The G2—M transition is delayed until enough Cdk1/CycB dimer accumulates ( [CycB]>θB′ ) to promote entry into prophase and the appearance Cdc20A/APC-C , which begins the process of cyclin A degradation [38] , [39] , [40] . Cdc20B/APC-C is activated at the metaphase—anaphase transition , where it promotes three crucial tasks: ( 1 ) separation of sister chromatids by the mitotic spindle , ( 2 ) partial degradation of cyclin B , and ( 3 ) re-activation of Cdh1 . Cdh1/APC-C degrades Cdc20 [41] , and then finishes the job of cyclin B degradation ( telophase ) . When [CycB] drops below the threshold θB″ , the cell finishes telophase and divides into two newborn daughter cells in G1 phase ( unreplicated chromosomes ) with low levels of cyclins A , B and E . We assume that cell division is symmetric , with some variability; i . e . , the mass of the two daughter cells at birth are δMdiv and ( 1−δ ) Mdiv , where Mdiv = mass of mother cell at division , and δ is a Gaussian-distributed random variable with mean = 0 . 5 and standard deviation = 0 . 0167 . In all simulations reported here we assume that cells grow exponentially between birth and division . However , we have also simulated linear growth , and the results are not significantly different . We introduce stochastic effects into the model by assuming that the time spent in each state of the Boolean subsystem , as it moves along the super highway , has a random component ( ) as well as a deterministic component ( ) : . From Table 1 , we see that for i = 1 , 6 , 7 , 8 , and h . For the remaining cases ( i = 2 , 3 , 5 , 9 ) , is however long it takes for the cyclin variable to reach its threshold . The stochastic component for each transition is a random number chosen from an exponential distribution with mean = λi . The random time delay is calculated from a uniform random deviate , r , by the formula = . The values chosen for the λi's are given in Table 1 . In the Methods section , we describe how we simulate the progression of a single cell through its DNA replication/division cycle . Because the model's differential equations are piecewise linear , they can be solved analytically , and an entire ‘cell cycle trajectory’ can be determined by computing a few random numbers and solving some algebraic equations . A typical result of such simulations , over three cell cycles , is illustrated in Fig . 1B . Not surprisingly , the accumulation and loss of the cyclins correlate with the activities of the cyclin regulators . At the beginning of each cycle , the cell starts in State 1 ( G1a phase in Table 1 ) , with low levels of all cyclin because TFE and TFB are off and Cdh1 is on . When the cell leaves G1a , TFE turns on and cyclin E rises rapidly , but cyclin A increases only modestly , because Cdh1 is still active in early G1b . Cdh1 turns off when cyclin E level crosses θE , allowing cyclin A to increase dramatically in late G1b and drive the cell into S phase ( State 4 ) . Cyclin B increases modestly in late G1 and S phase , because Cdh1 is off but TFB has not yet turned on . Cyclin E is degraded in S phase , because SCF is now active . When the cell finishes DNA synthesis , TFB turns on , causing further increase of cyclins A and B . When cyclin B level rises above its first threshold , θB′ , the cell enters prophase ( State 6 ) and then prometaphase-metaphase ( State 7 ) . During State 7 , cyclin A level drops precipitously because Cdc20A is turned on . After the replicated chromosomes are fully aligned on the mitotic spindle , Cdc20B turns on ( State 8 ) and cyclin B is partially degraded . Cdc20B activates Cdh1 ( State 9 ) and cyclin B is degraded even faster . When cyclin B level drops below its second threshold , θB″ , the cell divides and returns to G1a ( State 1 ) . Our first test for the hybrid model is to simulate flow cytometry measurements of the DNA content and cyclin levels in an asynchronous population of RKO ( colon carcinoma ) cells [42] . In the data set , a typical scatter plot has about 65000 data points , each point displaying the measurements of two observables in a single cell chosen at random from the cell cycle ( Fig . 2 ) . When the data are plotted in this way , they form a cloudy tube of points through a projection of the state space ( say , cyclin B versus cyclin A ) . Because there will be some cells from every phase of the cell cycle , the tube closes on itself . If the system were completely deterministic and the measurements were absolutely precise , the data points would be a simple closed curve ( a ‘limit cycle’ ) in the state space . The data actually present a fuzzy trajectory that snakes through state space before closing on itself . The indeterminacy of the points comes ( presumably ) from two sources: intrinsic noise in the molecular regulatory system ( modeled by the random waiting times , ) and extrinsic measurement errors , which we shall introduce momentarily . Our strategy for simulating flow-cytometry data is explained in more detail in the Methods section . In Fig . 2 we compare our simulated flow-cytometry scatter plots with experimental results of Yan et al . [42] . We color-code each cell in the simulated plot according to which Boolean State ( Table 1 ) the cell is in at the time of fixation . In Fig . 3 we plot cyclin E fluctuations , as predicted by our model , along with a projection of the cell cycle trajectory in a subspace spanned by the three cyclin variables ( A , B and E ) . As a further test of the utility of this modeling approach , we have used our hybrid model to simulate an exponentially growing population of an immortalized Human Umbilical Vein Endothelial cell line ( HUVEC ) . In the experiment ( Fig . 4A; see Methods ) , a culture is seeded with 5×104 cells on ‘Day 0’ and allowed to grow . At Day 6 , it reaches confluence and cell number plateaued at a constant level . To apply the hybrid model to this data , we had to devise a way to model contact inhibition , which arrests cells in a stable quiescent state . To this end , we assume that the transition probability , p , for exiting State 1 is a function of the number of cells alive at that time , N: ( 3 ) For 0<N1≪N0 , p is a sigmoidal function of N that drops abruptly from p0 to 0 for N>N0 . For each cell in this simulation , we set λ1 ( the mean for the random time spent in G1a ) to 1/p , and we choose p0 = 0 . 5 h−1 to conform to the value of λ1 in Table 1 . As the population size N increases , the time spent in G1a phase increases until cells eventually arrest in State 1 , and the growth curve , N ( t ) , levels off . In this case , State 1 in our model corresponds to a quiescent state ( G0 ) in which cells are alive but not proliferating . To make the simulation more tractable , we start off with 500 cells ( instead of 50 , 000 cells ) and follow the lineage of each initial cell until Day 10 . Every 24 hours , we compute the number of cells alive at that point of time and plot the results in Fig . 4A , along with the experimental data ( scaled down by a factor of 100 ) . The parameter values , N0 = 11 , 000 and N1 = 500 , are chosen to fit the simulation to the observed growth curve . From the model we can also compute the percentage of cells in G0/G1 , S and G2/M phases on each day ( Fig . 4C ) , and the results compare favorably with the experimental observations ( Fig . 4B ) . Lastly , we also simulate the patterns of cyclin A2 and cyclin B1 expression on each day for the growing population of HUVEC cells ( see Supporting Fig . S1 ) . We have constructed a simple , effective model of the cyclin-dependent kinase control system in mammalian cells and used the model to simulate faithfully the accumulation and degradation of cyclin proteins during asynchronous proliferation of RKO ( colon carcinoma ) cells . The model is inspired by the work of Li et al . [14] , who proposed a robust Boolean model of cell cycle regulation in budding yeast . Our goal was to retain the elegance of the Boolean representation of the switching network , while introducing continuous variables for cell size , cell age , and cyclin composition , in order to create a model that could be compared in quantitative detail to experimental measurements . We have shown that this model can accurately simulate flow-cytometric measurements of cyclin abundances in asynchronous populations of growing-dividing mammalian cells . The parameters in the model that allow for a quantitative description of the experimental measurements are easily estimated from the data itself . Now that the model is parameterized and validated for wild-type cells , we are currently extending it to handle the behavior of cell populations perturbed by drugs and by genetic interference . In some cases , only modest extensions of the model are required; in other cases , a more thorough overhaul of the way the discrete and continuous variables interact with each other is necessary . We have chosen parameter values in our model to capture the major features of cyclin fluctuations as measured by flow cytometry during the somatic division cycle of mammalian cells . We have used a human tumor cell line to calibrate our model . Between cell lines and normal human cultured cells , there are differences in the expressions of A and B cyclins [43]; however , when the levels of cyclin B1 were rigorously compared for HeLa , K562 , and RKO cells , both the patterns and magnitudes of expression are remarkably similar , apparently dependent to some degree on the rate of population growth [44] . In addition , the patterns of expression of cyclins A2 and B1 are similar for these human tumor cell lines and stimulated normal human circulating lymphocytes ( Supporting Fig . S2 ) . Overall , the simulation outputs have satisfying similarity both in pattern and magnitude to the real data for RKO cells , and our simulated expression patterns of cyclins A , B and E for the tumor cell line are quite similar to the simulated expression patterns in HUVEC cells ( see Supporting Fig . S1 ) . However , there remain some inconsistencies between our mathematical simulations and our experimental observations that point out where future modifications to the model are needed . For example , in the model DNA synthesis starts when cyclin A has accumulated to ∼8% of its maximum level ( see arrow in Fig . 2D; 50/600≈8% ) , whereas in our measurements DNA synthesis starts when cyclin A is ∼5% of its maximum level ( arrow in Fig . 2C ) . This discrepancy is tempered by the fact that we are not confident of the quantitative accuracy of cyclin A expression levels below ∼4% of its maximum level in Fig . 2C . Where we place the minimum expression level of cyclin A in Fig . 2D affects our estimate of the cyclin A level at onset of DNA synthesis ( 50 AU at present ) . By lowering the minimum expression level of cyclin A below 10 AU in Fig . 2D ( e . g . , by lowering k′sa ) , we could line up the two arrows in Figs . 2C and D . Nonetheless , we observe ( Supporting Fig . S3 ) that cyclin A expression correlates highly with BrdU incorporation , suggesting that significant accumulation of cyclin A begins simultaneously with the onset of DNA synthesis , whereas in our model cyclin A production begins in mid-G1 phase . This discrepancy could be minimized by lowering the cyclin A threshold ( θA ) in the model . The simulation ( Fig . 2B ) captures the observed accummulation of cyclin B in late G1 ( when Cdh1 turns off ) , but the simulated rise in cyclin B during S phase appears to be faster than the observed rise [45] ( compare the arrows in Figs . 2A and B ) . The simulation does capture the rapid accumulation of cyclin B observed in G2 . Finally , while we did not calibrate the cyclin E expression parameters to any specific dataset , the pattern of expression in Fig . 3A is quite similar to expected expression patterns for normal human somatic cells and some human tumor cell lines [46] . We believe that our hybrid approach will be generally useful for modeling macromolecular regulatory networks in cells , because it combines the qualitative appeal of Boolean models with the quantitative realism of reaction kinetic models . We simulate a flow cytometry experiment with our hybrid model in two steps . Step 1: Creating complete ‘life histories’ for thousands of cells . At the start of the simulation , we specify initial conditions at the beginning of the cycle ( State 1 ) for a progenitor cell . We used the following initial values of the state variables: [CycA] = [CycB] = [CycE] = 1 and M = 3 . Our strategy is to follow this cell through its cycle until it divides into two daughters . We then choose one of the two daughters at random and repeat the process , continuing for 32500 iterations . We discard the first 500 cells , and keep a sample of 32000 cells that have completed a replication-division cycle according to our model . In the second step , we create a simulated sample of 32000 cells chosen at random phases of the cell cycle , to represent the cells that were assayed by the flow cytometer . Let us consider cell i ( 1<i<32500 ) at the time of its birth , ti0 . By definition , this cell is in State 1 , and we assume that we know its birth mass , M ( ti0 ) , and its starting concentrations of cyclins A , B and E . Denote the starting concentrations as [CycA ( ti0 ) ] , [CycB ( ti0 ) ] , [CycE ( ti0 ) ] . In the ensuing discussion , unless it is necessary for clarity , we drop the i subscript , it being understood that we are talking about a representative cell in the population . We will follow this cell until it divides to produce a daughter cell with known concentrations of cyclins . According to Table 1 , a cell in State 1 has no special conditions to satisfy before moving to State 2 . Hence the residence time in State 1 is a random number chosen from an exponential distribution with mean λ1 = 2 h . The cell enters State 2 at t1 = t0+ . Assuming exponential growth , its size at this time is M ( t1 ) = M ( t0 ) exp{γ ( t1−t0 ) } = M ( t0 ) exp{γA1} , where γ is the specific growth rate of the culture and A1 = t1−t0 is the age of the cell when it exits State 1 . To illustrate how cyclin concentrations are computed at t = t1 , let us consider cyclin A as an example . During the interval t0<t<t1 , [CycA] satisfies a linear ODE with effective rate constants ksa1 = k′sa = 5 and kda1 = k′da+k″′da = 1 . 4 , because BTFE = BTFB = BCdc20A = 0 and BCdh1 = 1 for a cell in State 1 . We can compute the concentration of cyclin A at any time during this interval from ( 4 ) Setting t = t1 in this equation gives the number we seek . In this fashion , we start tabulating the following information for each simulated cell: Notice that , at t = t1 when the cell enters State 2 , the transcription factor ( TFE ) for cyclins E and A turns on , and these cyclins start to accumulate . The cell cannot leave State 2 until cyclin E accumulates to a sufficiently high level: [CycE] ( t ) ·M ( t ) = θE , according to Table 1 . When this condition is satisfied , the cell leaves State 2 and enters State 3 . The size dependence on this transition is a way to couple cell growth to the DNA replication-division cycle . According to the parameter settings in Table 1 , there is no stochastic component to the transition out of State 2 . We continue in this fashion until the cell leaves State 9 and returns to State 1 , when cyclin B is degraded at the end of mitosis . This is the signal for cell division . The age of the cell at division is A9 = t9−t0 , and the mass of the cell at division is M ( t9 ) = M ( t0 ) exp ( γ·A9 ) . The mass of the daughter cell at the beginning of her life history is Mdaughter ( t0 ) = δ·Mmother ( t9 ) , where δ is a random number sampled from a normal distribution of mean 0 . 5 and standard deviation 0 . 0167 to allow for asymmetries of cell division . Notice that simulating the life history of a single cell only requires generating about a dozen random numbers and performing a handful of algebraic calculations . At no point do we need to solve differential equations numerically . Hence we can quickly calculate the life histories of tens of thousands of cells . Step 2: Finding the DNA and cyclin levels of each cell in an asynchronous sample . In the flow cytometry experiments of Yan et al . [42] , a random sample of cells is taken from an asynchronous population , the cells are fixed and stained , and then run one-by-one through laser beams where fluorescence measurements are made . So each data point consists of measurements of light scatter ( related to cell size ) and fluorescence proportional to DNA and cyclin content for a single cell taken at some random point in the cell cycle . To simulate this experiment we must assign to each of our 32000 simulated cells a number φi selected randomly from the interval [0 , 1] , where φi refers to the fraction of the cell cycle completed by cell i when it was fixed and stained for measurement . Because each mother cell divides into two daughter cells , the density of cells at birth , φ = 0 , is twice the density of cells at division , φ = 1 . The ‘ideal’ probability density for an asynchronous population of cells expanding exponentially in number is ( 5 ) According to the ‘transformation method’ [47 , Chapter 7 . 2] , we compute φ as ( 6 ) where r is a random number chosen from a uniform distribution on [0 , 1] . In this way , we generate 32000 fractions , φi . If φi is the cell-cycle location of the ith cell when it is selected for the flow cytometry measurements , then its age at the time of selection is ai = φi·Ai9 , where Ai9 is the age of the ith cell at division . Given a value for ai , we then find the state n ( = 1 , 2 , … or 9 ) of the ith cell at the time of its selection: ( 7 ) where ti , n ( as defined above ) is the time at which the ith cell left state n to enter state n+1 . Once we know the state n of the cell , we can compute the concentration of each cyclin in the cell at its exact age ai by analogy to Eq . [4]: ( 8 ) where ksa , n and kda , n are the synthesis and degradation rate constants for cyclin A in state n . This is a straightforward calculation because in Step 1 we stored the values of tn and [CycA ( tn ) ] for every state of each cell . We can also calculate the mass of cell i at the time of its selection: ( 9 ) where M ( ti0 ) is the mass at birth of cell i and γ is the specific growth rate of the culture . Because the flow cytometer measures the total amount of fluorescence proportional to all cyclin A molecules in the ith cell , we take as our measurable the product of [CycA ( ai ) ] times M ( ai ) . Lastly we determine the DNA content of cell i at age ai according to: Now we have simulated values for the measurable quantities of each cell at the time point in the cell cycle when it was selected for analysis . Before plotting these numbers , we should take into account experimental errors , such as probe quality , fixation , staining and measurement . We do so by multiplying each measurable quantity ( DNA content and cyclin levels ) by a random number chosen from a Gaussian distribution with mean 1 and standard deviation = 0 . 03 for DNA measurements and 0 . 15 for cyclin measurements . These choices give scatter to the simulated data that is comparable to the scatter in the experimental data . Source codes for the hybrid model are provided in the Supporting Text S1 . Culture and fixation of RKO cells were described in [42] . The immortalized HUVEC cells [48] at passage 93 were seeded at 2 . 5×103 cells/cm2 in 10 ml EGM-2 media with 2% fetal bovine serum ( Lonza , Basel ) . Duplicate plates were prepared for each time point at days 1 , 2 , 3 , 4 , 5 , 6 , 7 , 10 , and 15 . Cells were fed every other day by replacing half the volume of used media . At the indicated times , cells were trypsinized , washed , and cell counts performed with a Guava Personal Cytometer ( Millipore , Billerica , MA ) . Fixation was as previously described [49]; briefly , cells were treated with 0 . 125% formaldehyde ( Polysciences , Warrington , PA ) for 10 min at 37°C , washed , then dehydrated with 90% Methanol . Cells were fixed in aliquots of 1×106 cells ( days 1–3 ) or 2×106 ( days 4–15 ) . Fixed cell samples were stored at −20°C until staining for cytometry . Staining and cytometry for RKO cells were described in [42] . Briefly , cells were trypsinized , fixed with 90% MeOH , washed with phosphate buffered saline , then stained with monoclonal antibodies reactive with cyclin B1 , cyclin A , phospho-S10-histone H3 , and with 4′ , 6-diamidino-2-phenylindole ( DAPI ) . For a detailed , updated version of antibodies , staining , and cytometry for cyclins A2 and B1 , phospho-S10-histone H3 , and DNA content , see Jacobberger et al . ( 38 ) . Data pre-processing was performed with WinList ( Verity Software House , Topsham , ME ) . Doublet discrimination ( peak versus area DAPI plot ) was used to limit the analysis to singlet cells; non-specific binding was used to remove background fluorescence from the total fluorescence related to cyclin A2 and B1 staining . The phycoerythrin channel ( cyclin A2 ) was compensated for spectral overlap from FITC or Alexa Fluor 488 . For simplification , very large 2C G1 HUVEC cells and any cells cycling at 4C→8C were removed from the analysis . These were present at low frequency . Data were written as text files then transferred to Microsoft Excel .
The physiological behaviors of cells ( growth and division , differentiation , movement , death , etc . ) are controlled by complex networks of interacting genes and proteins , and a fundamental goal of computational cell biology is to develop dynamical models of these regulatory networks that are realistic , accurate and predictive . Historically , these models have divided along two basic lines: deterministic or stochastic , and continuous or discrete; with scattered efforts to develop hybrid approaches that bridge these divides . Using the cell cycle control system in eukaryotes as an example , we propose a hybrid approach that combines a continuous representation of slowly changing protein concentrations with a discrete representation of components that switch rapidly between ‘on’ and ‘off’ states , and that combines the deterministic causality of network interactions with the stochastic uncertainty of random events . The hybrid approach can be easily tailored to the available knowledge of control systems , and it provides both qualitative and quantitative results that can be compared to experimental data to test the accuracy and predictive power of the model .
You are an expert at summarizing long articles. Proceed to summarize the following text: In a cross sectional study , 19 French and 23 Colombian cases of confirmed active ocular toxoplasmosis ( OT ) were evaluated . The objective was to compare clinical , parasitological and immunological responses and relate them to the infecting strains . A complete ocular examination was performed in each patient . The infecting strain was characterized by genotyping when intraocular Toxoplasma DNA was detectable , as well as by peptide-specific serotyping for each patient . To characterize the immune response , we assessed Toxoplasma protein recognition patterns by intraocular antibodies and the intraocular profile of cytokines , chemokines and growth factors . Significant differences were found for size of active lesions , unilateral macular involvement , unilateral visual impairment , vitreous inflammation , synechiae , and vasculitis , with higher values observed throughout for Colombian patients . Multilocus PCR-DNA sequence genotyping was only successful in three Colombian patients revealing one type I and two atypical strains . The Colombian OT patients possessed heterogeneous atypical serotypes whereas the French were uniformly reactive to type II strain peptides . The protein patterns recognized by intraocular antibodies and the cytokine patterns were strikingly different between the two populations . Intraocular IFN-γ and IL-17 expression was lower , while higher levels of IL-13 and IL-6 were detected in aqueous humor of Colombian patients . Our results are consistent with the hypothesis that South American strains may cause more severe OT due to an inhibition of the protective effect of IFN-γ . Infection with the protozoan parasite Toxoplasma gondii is a leading cause of visual impairment in numerous countries , being responsible for 30 to 50% of uveitis cases in immunocompetent individuals [1] . Ocular toxoplasmosis ( OT ) is a potential complication of both acquired and congenital toxoplasmosis [2] . The incidence of ocular toxoplasmosis has been estimated in Colombia ( Quindio region ) to be of three new episodes by 100 000 inhabitants by year [3] , while in British-born patients it has been estimated to be 0 . 4 cases per 100 , 000 population per year and the lifetime risk of disease to be 18 cases per 100 , 000 population [4] . In a Colombian study , 5 . 5% of the population in the province of Quindío exhibited retinochoroidal scars resulting from a postnatally acquired infection , with 20% of this group presenting reduced visual capacity . [3] , [5] . In a retrospective study on uveitis conducted in 693 Colombian patients , 417 of whom had a definitive diagnosis , toxoplasmosis was the most frequent cause with 276 cases ( 39 . 8% ) followed by idiopathic uveitis and toxocariasis [6] . Some differences between South American and European clinical case series were observed in terms of congenital transmission rates , probability of symptoms in congenital OT [7] , [8] , severity of ocular inflammation [9] and intraocular specific antibody levels [10] . However , no comparative clinical and biological studies have been performed yet in patients from both continents with laboratory-confirmed OT . The population structure of T . gondii in North America and Europe includes three highly prevalent clonal lineages , Types I ( haplogroup 1 , Clade A ) , II ( Haplogroup 2 , Clade D ) , and III ( haplogroup 3 , Clade , C ) which differ greatly in virulence in the mouse model . The vast majority of human and animal infections are caused by the relatively avirulent Type II strains . In contrast , heterogeneous atypical genotypes of T . gondii are associated with severe infections in humans in South America . They belong to various haplogroups: 4 , 5 , 8 10 and 15 , Clade F [11] , [12][13] . The high genetic diversity of Toxoplasma strains in the tropical zone of the Americas may partly explain why congenital toxoplasmosis is more symptomatic in South America than Europe , as was demonstrated in cohorts of congenitally infected children from different continents [8] , [14] , [15] . A comparative prospective cohort study of congenitally infected children in Brazil and Europe found that Brazilian children displayed eye lesions that were larger , more numerous , and more likely to affect the central part of the retina responsible for acute vision [7] . Anecdotal clinical cases were also reported in the literature , such as a severe atypical bilateral retinochoroiditis in a Brazilian patient , caused by a highly divergent , non-archetypal T . gondii strain [16] . Given the markedly different population structure of T . gondii in Europe and South America , it is relevant to study the implications of this diversity on human pathogenesis [17] . Therefore , we conducted a multicenter case series study in order to compare the different clinical and immunological characteristics between Colombian and French patients , collecting the same data and performing the same laboratory assays in patients with biologically confirmed OT . The findings were related to Toxoplasma strain genotyping and peptide-based strain serotyping in our patients . We collected data from consecutive patients who consulted at the Departments of Ophthalmology at Strasbourg University Hospital ( France ) and Quindío University Health Center ( Armenia , Colombia ) between August 2008 and August 2010 . Both departments were tertiary-level centers able to perform anterior chamber paracentesis . For both patient populations , a complete ocular examination was conducted , including best-corrected Snellen visual acuity , slit-lamp biomicroscopy , tonometry , and indirect ophthalmoscopy . The clinical diagnosis of OT was based on criteria previously described by G . Holland [6] , [18] . Screened patients with clinically suspected OT and seropositive for anti-Toxoplasma immunoglobulin G ( IgG ) antibodies were subsequently submitted to biological investigations to assess the local presence of Toxoplasma DNA and/or the intraocular antibody synthesis [19] to confirm OT . Ethics Committee/Institutional Review Board ( IRB ) approval were obtained from Hôpitaux Universitaires de Strasbourg ( PHRC 2007/3964 ) and Quindio University ( ACT 14 , 2008/23-06 ) . Written informed consent was obtained from all subjects . We analyzed the clinical characteristics of 19 French and 23 Colombian patients with active uveitis and biologically confirmed OT . Patients who were immunocompromised , suffered from other ocular infections , or received local or systemic anti-Toxoplasma treatment for active uveitis , were excluded . An assessment of the inflammation level and anatomic classification of uveitis was carried out according to the criteria proposed by the International Uveitis Study Group ( IUSG ) [20] . The size of the retinochoroidal lesions was measured in disc-diameters ( dd ) . Paired samples of aqueous humor and serum were obtained from each subject at the time of clinical diagnosis for laboratory analysis . The Colombian samples were stored locally at −80°C and then shipped together on dry ice to Strasbourg for laboratory analysis . Aqueous humor samples ( 100–150 µL ) were collected through anterior chamber paracentesis and stored , along with serum samples , at −80°C until analysis . The diagnosis of OT was first confirmed by real-time PCR detection of Toxoplasma DNA [21] . Positive PCR results were quantified using a standard curve with serial 10-fold dilutions from a calibrated suspension of T . gondii RH-Strain DNA . For PCR negative patients , immunoblot ( IB ) was performed in order to detect intraocular synthesis of Toxoplasma-specific antibodies ( LDBIO Diagnosis , Lyon , France ) . If both PCR and IB were unconclusive , a modified Goldmann-Witmer test was used to prove intraocular specificantibody synthesis [22] . The Bio-Plex Human 27-Plex Cytokine Panel assay ( Bio-Rad , Marne-la-Coquette , France ) was used according to the manufacurer's recommendations to measure cytokine and chemokine levels in aqueous humor . The assay plate layout consisted in a standard series in duplicate ( 1 to 32 000 pg/mL ) , four blank wells and 20 µL duplicates of AqH samples , diluted to 50 µL with BioPlex Human serum diluent [23] . A set of Toxoplasma seropositive cataract patients were used as control , 9 Colombian and 10 French . Data were analyzed with Bio-Plex Manager TM software V1 . 1 . DNA extraction for genotyping analysis was performed directly on ocular fluid samples and indirectly on infected cell cultures for six reference strains . GT1 , PTG , and CTG strains were selected as reference Types I , II , and III strains , respectively . TgCtCo02 , TgCtCo05 , and TgCtCo07 strains were selected as reference Colombian strains [24] , [25] . T . gondii DNA samples were subjected to genotyping analysis with 15 microsatellite markers in a multiplex PCR assay , as described elsewhere [26] . Serotyping of Toxoplasma infections was performed using 5 polymorphic synthetic peptides derived from the T . gondii dense granule proteins ( GRA ) , GRA6 and GRA7 . This test detects the presence of strain specific antibodies raised against Type II or non-Type II GRA6/7 alleles in patients infected with Type II or non Type II ( NE-II ) parasites respectively , as previously described [14] , [27] . Briefly , the ELISA results presented are an optical density ( OD ) index obtained by dividing the OD value at 405 nm for each of the 5 serotyping peptides by the mean of the OD readings for the 2 control peptides . Threshold values are determined by averaging the normalized OD ratio from 100 seronegative French samples and adding 2 standard deviations , above which normalized values are considered positive . Obtained results are divided in four populations depending on their reactivity to the 5 peptides: I/III , ATYP , no reactivity ( NR ) , and II [28] . I/III , ATYP and NR are considered as NE-II [14] . Sera from pregnant women , tested Toxoplasma seropositive in our laboratories , were used to assess the Toxoplasma serotype in a larger population from each country , 45 serum samples from Colombia and 100 from France . Mann-Whitney test followed by Dunn's Multiple Comparison test was applied for comparison of clinical and laboratory characteristics for French and Colombian patients with confirmed active ocular toxoplasmosis ( P values<0 . 05 were considered statistically significant; Stata software , College Station ( Tx ) USA ) . Fisher's exact test was used to compare diagnostic performances of IB and PCR as well as the serotype prevalence . Wilcoxon matched-pairs signed rank test was performed to compare IB patterns . Mann-Whitney test was used to compare intraocular parasite loads ( P values<0 . 05 were considered statistically significant . Kruskal-Wallis test followed by Dunn's Multiple Comparison test were applied for comparison of cytokine and chemokine levels in aqueous humor between control and OT populations in both countries ( P values<0 . 05 were considered statistically significant ) ( GraphPad Prism , La Jolla , CA , USA ) . The clinical findings for OT patients are summarized in Tables 1 and S1 . Statistically significant differences between groups were found for eight parameters , being higher in Colombian patients in all cases: i ) time between consultation and anterior chamber paracentesis ( p = 0 . 02 ) ; ii ) size of active lesions ( p = 0 . 04 ) ; iii ) unilateral macular involvement ( p = 0 . 001 ) ; iv ) unilateral visual impairment ( p = 0 . 04 ) ; v ) vitreous inflammation ( p = 0 . 00001 ) ; vi ) percentage of patients with synechiae ( p = 0 . 04 ) ; vii ) vasculitis ( p = 0 . 04 ) and viii ) bilateral involvement ( p = 0 . 04 ) . In addition , there was a trend towards higher values for the Colombian patients regarding the number of lesions , number of recurrences , and intraocular pressure ( IOP ) , although these differences were not statistically significant . We conducted a stratified analysis in order to exclude the influence of time before anterior chamber paracentesis as a possible cause of the differences in clinical findings . We compared early ( <20 days after symptom onset ) and late consultations ( >20 days after symptom onset ) . As shown in Table 2 and supplementary figure 1 , most significant clinical differences between the populations were also visible when comparing only the early-consultant groups . In Colombians , aqueous humor samples revealed the presence of T . gondii DNA in 11 out of 23 samples ( 47 . 8% ) . In French patients , T . gondii DNA could be detected in aqueous humor samples of 7 out of 19 patients ( 36 . 8% ) . This difference was not statistically significant . In contrast , parasite loads in aqueous humor were significantly higher in Colombian patients , 4 . 53 parasites ± 2 per 100 µL versus 0 . 35±0 . 13 parasites per 100 µL ( p = 0 . 0006 ) ( Figure 1 ) . Aqueous humor samples from all French patients and 14 Colombian patients had an insufficient amount of T . gondii DNA for genotyping analysis . Only 9 Colombian ocular fluid samples were submitted for multilocus PCR-DNA sequence genotyping analysis . Six had unsuccessful PCR amplification for all 15 tested markers due to low T . gondii DNA concentration . The genotype of one clinical sample ( case COL-#6 ) was closely related to a Type I strain , but harboring unique alleles at three MS loci , M102 , N83 and AA , using 15 amplified markers ( Table 3 ) . Of note , the genotype of a reference Colombian isolate ( TgCtCo07 ) collected from a cat in 2005 was also Type I-like , suggesting that Type I-like strains may not be uncommon in animals and humans in Colombia . The genotypes of the other two clinical samples ( cases COL-#26 and COL-#38 ) could not be fully determined , with only four and five successfully amplified markers , respectively . However , the results of the amplified markers showed that both genotypes were different from the Type II or III strains , which are common in North America and Europe . They present a majority of Type I alleles ( case COL-#26 ) , like TgCtCo07 but distinct at the N61 marker , and a combination of Type I , III , and atypical alleles ( case COL-#38 ) , like TgCtCo02 and TgCtCo05 , but again distinct at the N60 and N82 genetic markers . IB detected local antibody production in 19/23 Colombian ( 82 . 6% ) and 13/19 French ( 68 . 4% ) patients ( not significant ) . However , a significant difference was observed in number of bands and their recognition pattern of Toxoplasma proteins ( p<0 . 0001 ) ( Figure 2 ) . Specific proteins were recognized in 3 . 3% to 63 . 3% of Colombian patients and 3 . 8% to 53 . 8% of French patients . Colombian patients recognized most frequently a 62 kDa protein , observed in 63 . 3% of patients . In French patients , the most frequently detected protein was at 34 . 2 kDa , found in 53 . 8% of patients . As the amount of aqueous humor was insufficient for Toxoplasma strain typing using an ELISA peptide-based assay , we decided to serotype these patients using their sera . Ten OT patients from each center were assessed , all from the early consultation group . Among the Colombian patients , no Type II serotype was detected . We found 4 I/III , one atypical and 5 non reactive ( NR ) serotypes ( Table 4 ) . In contrast , all tested French OT patients showed Type II serotypes except one patient with an atypical serotype . These patterns were significantly different between the two groups ( p<0 . 0001 ) . The two cases COL#26 and COL#38 , found as suspected Type I and Type I/III by genotyping , were serotyped as NR and type I/III , respectively ( Table 4 ) . To test if certain T . gondii strains are associated with OT , we determined the overall distribution of serotypes in infected non-OT control populations from both countries . Among the 45 Colombian control patients , only 6 subjects ( 13 . 3% ) had a type II whereas 39 ( 86 . 6% ) had NE-II serotypes , which were subdivided in 6 NR , 29 type I/III and 4 atypical serotypes . Of 100 French control patients , we found 64 ( 64% ) type II , and 36 ( 36% ) with NE-II; 10 NR , 2 type I/III and 24 atypical serotypes . No statistically significant differences were observed between the control and OT groups in Colombian patients , however we found a significant difference ( P = 0 . 02 ) between the French control and OT populations , with respect to the proportion of the two types , II and NE-II . Cytokines patterns in aqueous humor of OT patients were compared to cataract controls ( Figure 3 and Table S2 in Text S1 ) . Several immune mediators were augmented in French , as well as in Colombian patients . In French patients , the Th1 type cytokines IFN-γ , IL-2 and IL-15 were expressed in all patients . This Th1 immune response was associated to a Th17 response with increased IL-17 production . Additionally , we observed a large proinflammatory response with increased levels of IL-6 , IL-1β , IL-8 , MIP-1β , MCP-1 and G-CSF . These patients also possessed a corresponding anti-inflammatory response was based on the presence of IL-4 , IL-10 , and IL-1RA . In contrast , Colombian patients had lower expression of major proinflammatory immune modulators , including IFN-γ , IL-15 , IL-17 , IL-2 , IL-10 , MIP-1β , GM-CSF and G-CSF , with the exception of elevated TNF-α and IL-6 levels . These patients also had elevated levels of the counterregulating Th2-type cytokine IL-13 . Previously published studies found differences between South American and European clinical case series on adult patients in terms of frequency of serological markers in OT [8] , probability of symptoms in congenital infection [7] , as well as inflammation levels and IOP [9] . However , these were mostly retrospective evaluations of multiple studies . Their main limitation is their inclusion of patients with “suspected” OT , rather than biologically confirmed cases . While the ocular signs of toxoplasmic retinochoroiditis are highly suggestive of this disease , they may be mimicked by other infections [22] , while in some cases , the symptoms may be atypical [19] , [29] . Therefore , we strengthened our evaluation by inclusion of biologically confirmed OT cases only , as well as by comparing the same bio-clinical data from two different populations of OT patients , located in South America and Europe in a cross sectional study . Among the 17 criteria analyzed in the two populations , the following were significantly higher in Colombian patients: macular involvement , vitreous inflammation , strabismus , bilateral involvement and synechiae . Our findings confirm and expand the data from the retrospective study of Dodds et al . from patients with biologically unconfirmed OT which found elevated IOP , increased presence of synechiae , AC cells , flare , and vitreous humor haze [9] . In our study , one key difference between the two patient populations was the date of consultation , as Colombian patients consulted later than the French . However , when our analysis was stratified regarding this aspect , the observed clinical differences remained significant . The main hypothesis for these clinical differences is based on the idea that severe disease in humans may result from poor host adaptation to neotropical zoonotic strains of T . gondii [11] . Our study accumulated some clues supporting this hypothesis . Central strain-specific parasite virulence factors in human infections were revealed in the last years [30] . Their role in the presence of more virulent parasite genotypes in South America [11] , [12] is not yet thoroughly studied . Theses strains are rarely found in Europe [31] where Type II genotypes predominate , including in OT patients [32] . In the three Colombian OT patients where we could detect Toxoplasma DNA , we found one Type I and two atypical strains . The fact that no patient of the French group had a sufficient ocular parasite load for genotyping clearly shows the difference in ocular virulence . Additionally , we noticed that intraocular antibodies responses showed major differences in Toxoplasma antigen recognition by an immunoblotting assay . Even if this could be partly due to better detection of Toxoplasma Type I antigens used in this assay by Colombian patients , other , host immune specific factors are certainly crucial such as local antibodies , whose exact role and function should be explored . Our serotyping assay confirmed that Colombian and French patients recognize different strain-specific epitopes . Colombian OT patients recognized a heterogeneous pattern of strain specific peptides , but none of them were from type II strains . The French OT patients recognized only Type II strain specific peptides , confirming the reliability of this test in a geographic region with predominant type II strains infections [33] . The corresponding control populations presented the same serological pattern for Colombia , but a slightly different pattern for France , where some sera were non reactive to Type II antigens . The difference may due to the unequal sample sizes , so this point needs further investigation using more samples and equilibrated OT and control population . However , these data indicate that type II and non-type II strains are able to cause ocular pathology , but with a markedly different clinical picture . Concerning the Colombian strains , current serotyping techniques might be not sensitive enough to distinguish the highly variable strains . When we looked at the patients' local immunological reaction , we observed clearly different cytokine signatures . In French patients , the host-parasite relationship seems to be equilibrated between protection and inflammation . The protective effect of IFN-γ is balanced by anti-inflammatory cytokines such as IL-2 and IL-10 . The role of IL-17 is controversial . We have previously observed an early pathologic and parasite promoting role for IL-17 in French patients and in an animal model infected by a Type II Toxoplasma strain [34] . In the intraocular ocular environment , IL-17 would attract neutrophils [35] and , accompanied by IL-15 and MIP-1β/CCL4 , activates and attracts NK cells [36] and monocytes [37] . All these innate immune cells might cause retinal inflammation , but then permit to control Toxoplasma proliferation [38] , [39] . As our recent findings implicate IL-27 and the Treg subset in counterbalancing deleterious inflammatory Th17 type responses [34] , the corresponding mediators deserve to be examined more closely in future studies . In contrast , in the clinically more severe Colombian cases , IFN-γ and other major immunomodulators such as IL-17 were barely detectable , while IL-6 and IL-13 were enhanced . Virulent strains encode virulence factors able to modulate multiple immune host cell signaling pathways through polymorphic effectors secreted into the host cells such as ROP16 and GRA15 [38] , [40] . The presence of Toxoplasma effector proteins from virulent strains could explain the down-regulation of ocular IFN-γ , leading to higher ocular parasite loads in Colombian patients . The IL-17 down-regulation remains to be explained , but decreased levels of IL-17 and other immune modulators , including proangiogenic factors , could lead to a defect in the migration of leukocytes to the eyes and be another explanation for impaired control of parasites in the context of virulent South American infections . IL-6 could also antagonize the anti-microbial properties of IFN-γ by sustained activation of STAT3 , a potent inhibitor of IL-12 and IFN-γ [41] . Down-regulation of IFN-γ and its anti-Toxoplasma activity was also observed for IL-13 in human fibroblasts [42] . It is important to note here that Type I strains express a ROP16 allele associated with prolonged activation of STAT3 and STAT6 signaling , which may in part contribute to the increased IL-13 levels , whereas Type II strains activate this pathway only transiently , allowing the establishment of an inflammatory reaction [43] . This may constitute the fundamental basis for the differential cytokine response observed in our study . The theory of local T cell exhaustion may be also of interest in the settings of Colombian patients . Immune exhaustion is characterized by the modification of the CD8+ functions by reducing their polyfunctionality and their efficacy [44] . Indeed , high Toxoplasma loads associated with a decreased IFN-γ and IL-15 production and enhancement of TNF-α could be one aspect of this loss of CD8+ T cell polyfunctionality . In contrast , in French patients , elevated IL-15 is critical for homeostasis of memory CD8 T cells , and may lead to a better control of parasite proliferation and subsequent parasite latency in the retina . Taken together , our results indicate that virulent strains observed in South America may suppress host-protective pathways , opening the way to multiplication and cytolytic activity of the parasite in retinal tissues including blood vessels . The presence of TNF-α in most of these patients could also contribute by enhancing an ongoing immunopathological retinal process [45] . In contrast , in French patients , the cytokinic environment may lead to the encystation of the parasite in the retinal tissues , leading to subsequent recurrences . Of course , for ethical reasons , we were only able to take one time-point . Our results represent thus a snapshot of a developing immune response . Additionally , a multifactorial origin of the observed clinical and biological differences could not be excluded . In our study , the source of contamination may have been drinking water collected from surface water sources ( i . e . , rivers , lakes ) [46] , [47] , [48] , [49] . The more common macular involvement in Colombian patients is often associated with congenital toxoplasmosis [6] , [15] , [50] , [51] . Even if we studied adult populations , we cannot exclude a congenital origin of infection in some Colombian patients . Moreover , acute toxoplasmosis was only diagnosed in 2 Colombian and 1 French case . The remaining population was considered to exhibit chronic toxoplasmosis . Finally , individual susceptibility was previously related to variations in various genes encoding immune response players , such as IFN-γ , IL-1α , IL-10 , TLR-9 or ABCA4 , COL2A1 , and P2X7-R [52] , [53] , [54] , [55] . These genetically susceptible patients are possibly less able to cope with a more virulent strain . Further investigations with larger cohorts including an evaluation of their immunological response and their individual susceptibility to Toxoplasma are needed to address these topics .
Ocular toxoplasmosis ( OT ) , due to protozoan parasite Toxoplasma gondii , is a potential complication of both acquired and congenital infection , leading to visual impairment in numerous countries and being responsible for 30 to 50% of uveitis cases in immunocompetent individuals . In this study we confirmed the presence of more severe ocular toxoplasmosis in a tropical setting of Colombia , when compared to France . The main hypothesis for these clinical differences is based on the idea that severe disease in humans may result from poor host adaptation to neotropical zoonotic strains of T . gondii Indeed , our results are consistent with the hypothesis that South American strains may cause more severe OT due to an inhibition of the intraocular protective immune response .
You are an expert at summarizing long articles. Proceed to summarize the following text: Breast cancer is the most common malignancy in women and is responsible for hundreds of thousands of deaths annually . As with most cancers , it is a heterogeneous disease and different breast cancer subtypes are treated differently . Understanding the difference in prognosis for breast cancer based on its molecular and phenotypic features is one avenue for improving treatment by matching the proper treatment with molecular subtypes of the disease . In this work , we employed a competition-based approach to modeling breast cancer prognosis using large datasets containing genomic and clinical information and an online real-time leaderboard program used to speed feedback to the modeling team and to encourage each modeler to work towards achieving a higher ranked submission . We find that machine learning methods combined with molecular features selected based on expert prior knowledge can improve survival predictions compared to current best-in-class methodologies and that ensemble models trained across multiple user submissions systematically outperform individual models within the ensemble . We also find that model scores are highly consistent across multiple independent evaluations . This study serves as the pilot phase of a much larger competition open to the whole research community , with the goal of understanding general strategies for model optimization using clinical and molecular profiling data and providing an objective , transparent system for assessing prognostic models . Breast cancer remains the most common malignancy in females , with more than 200 , 000 cases of invasive breast cancer diagnosed in the United States annually [1] . Molecular profiling research in the last decade has revealed breast cancer to be a heterogeneous disease [2]–[4] , motivating the development of molecular classifiers of breast cancer sub-types to influence diagnosis , prognosis , and treatment . In 2002 , a research study reported a molecular predictor of breast cancer survival [5] based on analysis of gene expression profiles from 295 breast cancer patients with 5 year clinical follow-up . Based on these results , two independent companies developed the commercially available MammaPrint [6] and Oncotype DX [7] assays , which have both been promising in augmenting risk prediction compared to models based only on clinical data . However , their role in clinical decision-making is still being debated . Based on the success of these initial molecular profiles , a large number of additional signatures have been proposed to identify markers of breast cancer tumor biology that may affect clinical outcome [8]–[13] . Meta-analyses indicate that many of them perform very similarly in terms of risk prediction , and can often be correlated with markers of cell proliferation [14] , a well-known predictor of patient outcome [15] , especially for ER+ tumors [16] , [17] . Therefore , it is much more challenging to identify signatures that provide additional independent and more specific risk prediction performance once accounting for proliferation and clinical factors . Recent studies have even suggested that most random subsets of genes are significantly associated with breast cancer survival , and that the majority ( 60% ) of 48 published signatures did not perform significantly better than models built from the random subsets of genes [18] . Correcting for the confounding effect of proliferation based on an expression marker of cell proliferation removes most of the signal from the 48 published signatures [18] . The difficulties in reaching community consensus regarding the best breast cancer prognosis signatures illustrates a more intrinsic problem whereby researchers are responsible for both developing a model and comparing its performance against alternatives [19] . This phenomenon has been deemed the “self-assessment trap” , referring to the tendency of researchers to unintentionally or intentionally report results favorable to their model . Such self-assessment bias may arise , for example , by choosing assessment statistics for which their model is likely to perform well , selective reporting of performance in the modeling niche where their method is superior , or increased care or expertise in optimizing performance of their method compared to others . In this work , we explore the use of a research strategy of collaborative competitions as a way to overcome the self-assessment trap . In particular , the competitive component formally separates model development from model evaluation and provides a transparent and objective mechanism for ranking models . The collaborative component allows models to evolve and improve through knowledge sharing , and thereby emphasizes correct and insightful science as the primary objective of the study . The concept of collaborative competitions is not without precedent and is most evident in crowd-sourcing efforts for harnessing the competitive instincts of a community . Netflix [20] and X-Prize [21] were two early successes in online hosting of data challenges . Commercial initiatives such as Kaggle [22] and Innocentive [23] have hosted many successful online modeling competitions in astronomy , insurance , medicine , and other data-rich disciplines . The MAQC-II project [24] employed blinded evaluations and standardized datasets in the context of a large consortium-based research study to assess modeling factors related to prediction accuracy across 13 different phenotypic endpoints . Efforts such as CASP [25] , DREAM [26] , and CAFA [27] have created communities around key scientific challenges in structural biology , systems biology , and protein function prediction , respectively . In all cases it has been observed that the best crowd-sourced models usually outperform state-of-the-art off-the-shelf methods . Despite their success in achieving models with improved performance , existing resources do not provide a general solution for hosting open-access crowd-sourced collaborative competitions due to two primary factors . First , most systems provide participants with a training dataset and require them to submit a vector of predictions for evaluation in the held-out dataset [20] , [22] , [24] , [26] , often requiring ( only ) the winning team to submit a description of their method and sometimes source code to verify reproducibility . While this achieves the goal of objectively assessing models , we believe it fails to achieve an equally important goal of developing a transparent community resource where participants work openly to collaboratively share and evolve models . We overcome this problem by developing a system where participants submit models as re-runnable source code by implementing a simple programmatic API consisting of a train and predict method . Second , some existing systems are designed primarily to leverage crowd-sourcing to develop models for a commercial partner [22] , [23] who pays to run the competition and provides a prize to the developer of the best-performing model . Although we support this approach as a creative and powerful method for advancing commercial applications , such a system imposes limitations on the ability of participants to share models openly as well as intellectual property restrictions on the use of models . We overcome this problem by making all models available to the community through an open source license . In this study , we formed a research group consisting of scientists from 5 institutions across the United States and conducted a collaborative competition to assess the accuracy of prognostic models of breast cancer survival . This research group , called the Federation , was set up as a mechanism for advancing collaborative research projects designed to demonstrate the benefit of team-oriented science . The rest of our group consisted of the organizers of the DREAM project , the Oslo team from the Norwegian Breast Cancer study , and leaders of the Molecular Taxonomy of Breast Cancer International Consortium ( METABRIC ) , who provided a novel dataset consisting of nearly 2 , 000 breast cancer samples with median 10-year follow-up , detailed clinical information , and genome-wide gene expression and copy number profiling data . In order to create an independent dataset for assessing model consistency , the Oslo team generated novel copy number data on an additional 102 samples ( the MicMa cohort ) , which was combined with gene expression and clinical data for the same samples that was previously put in the public domain by the same research group [4] , [28] . The initial study using the METABRIC data focused on unsupervised molecular sub-class discovery [29] . Although some of the reported sub-classes do correlate with survival , the goal of this initial work was not to build prognostic models . Indeed , the models developed in the current study provide more accurate survival predictions than those trained using molecular sub-classes reported in the original work . Therefore , the current study represents the first large-scale attempt to assess prognostic models based on a dataset of this scale and quality of clinical information . The contributions of this work are two-fold . First , we conducted a detailed post-hoc analysis of all submitted models to determine model characteristics related to prognostic accuracy . Second , we report the development of a novel computational system for hosting community-based collaborative competitions , providing a generalizable framework for participants to build and evaluate transparent , re-runnable , and extensible models . Further , we suggest elements of study design , dataset characteristics , and evaluation criteria used to assess whether the results of a competition-style research study improve on standard approaches . We stress that the transparency enabled by making source code available and providing objective pre-defined scoring criteria allow researchers in future studies to verify reproducibility , improve on our findings , and assess their generalizability in future applications . Thus the results and computational system developed in this work serve as a pilot study for an open community-based competition on prognostic models of breast cancer survival . More generally , we believe this study will serve as the basis for additional competition-based research projects in the future , with the goal of promoting increased transparency and objectivity in genomics research ( and other applications ) and providing an open framework to collaboratively evolve complex models leading to patient benefit , beyond the sum of the individual efforts , by leveraging the wisdom of crowds . We used the METABRIC dataset as the basis of evaluating prognostic models in this study . This dataset contains a total of nearly 2 , 000 breast cancer samples . 980 of these samples ( excluding those with missing survival information ) were available for the duration of the collaborative competition phase of this study . An additional 988 samples became available after we had concluded our evaluation in the initial dataset and , fortunately , served as a large additional dataset for assessing the consistency of our findings . For each sample , the dataset contains median 10 year follow-up , 16 clinical covariates ( Table 1 ) , and genome-wide gene expression and copy number profiling data , normalized as described in [29] , resulting in 48 , 803 gene expression features and 31 , 685 copy number features summarized at the gene level ( see Methods ) . Initial analysis was performed to confirm that the data employed in the competition were consistent with previously published datasets and to identify potential confounding factors such as internal subclasses . Data-driven , unsupervised hierarchical clustering of gene expression levels revealed the heterogeneity of the data and suggested that multiple subclasses do exist ( not shown ) [29] . However , for the current analysis we decided to focus on the well established separation into basal , luminal , and HER2 positive subclasses , as previously defined [2] , [30] . These subclasses are known to closely match clinical data in the following way: most triple-negative samples belong to the basal subclass; most ER positive samples belong to the luminal subclass; and most ER negative HER2 positive samples belong to the HER2 subclass . To ensure that this holds in the current dataset , the 50 genes that best separate the molecular subclasses in the Perou dataset [31] ( PAM50 ) were used for hierarchical clustering of the METABRIC data and compared with a similar clustering of the Perou dataset ( Figure 1A ) . The results of the supervised clustering reveal similar subclasses with similar gene expression signatures as those presented by Perou et al , and were also consistent with the clinical definitions as presented above . Finally , the 3 subclasses show a distinct separation in their Kaplan-Meier overall survival plots for the three subtypes defined by the clinical data , where the HER2 subclass has the worst prognosis , followed by the basal subclass , and the luminal subclass has the best prognosis , as expected ( Figure 1B ) . This analysis shows that sub-classification based on ER ( IHC ) , PR ( gene expression ) , and HER2 ( copy number ) should capture the major confounding factors that may be introduced by the heterogeneity of the disease . Multiple individual clinical features exhibit high correlation with survival for non-censored patients , and have well documented prognostic power ( Table 1 , Figure 1C ) , while others have little prognostic power ( Figure 1D ) . To demonstrate that the competition data is consistent in this respect , a Cox proportional hazard model was fit to the overall survival ( OS ) of all patients using each one of the clinical covariates individually . As expected , the most predictive single clinical features are the tumor size , age at diagnosis , PR status , and presence of lymph node metastases ( Table 1 ) . To assess the redundancy of the clinical variables , an additional multivariable Cox proportional hazard model was fit to the overall survival ( OS ) of all patients using all clinical features . The remaining statistically significant covariates were patient age at diagnosis ( the most predictive feature ) , followed by tumor size , presence of lymph node metastases , and whether the patient received hormone therapy . Participants from our 5 research groups were provided data from 500 patient samples used to train prognostic models . These models were submitted as re-runnable source code and participants were provided real-time feedback in the form of a “leaderboard” based on the concordance index of predicted survival versus the observed survival in the 480 held-out samples . Participants independently submitted 110 models to predict survival from the supplied clinical and molecular data ( Table S1 ) , showing a wide variability in their performance , which was expected since there were no constraints on the submissions . Post-hoc analysis of submitted models revealed 5 broad classes of modeling strategies based on if the model was trained using: only clinical features ( C ) ; only molecular features ( M ) ; molecular and clinical features ( MC ) ; molecular features selected using prior knowledge ( MP ) ; molecular features selected using prior knowledge combined with clinical features ( MPC ) ( Table 2 ) . The complete distribution of the performance of all the models , evaluated using concordance index , and classified into these categories is shown in Figure 2 . Analysis of the relative performance among model categories suggested interesting patterns related to criteria influencing model performance . The traditional method for predicting outcome is Cox regression on the clinical features [32] . This model , which used only clinical features , served as our baseline , and obtained a concordance index of 0 . 6347 on the validation set . Models trained on the clinical covariates using state-of-the-art machine learning methods ( elastic net , lasso , random forest , boosting ) achieved notable performance improvements over the baseline Cox regression model ( Figure 2 , category ‘C’ ) . Two submitted models were built by naively inputting all molecular features into machine learning algorithms ( i . e . using all gene expression and CNA features and no clinical features ) . These models ( our category ‘M’ ) both performed significantly worse than the baseline clinical model ( median concordance index of 0 . 5906 ) . Given that our training set contains over 80 , 000 molecular features and only 500 training samples , this result highlights the challenges related to overfitting due to the imbalance between the number of features and number of samples , also known as the curse of dimensionality [33] , [34] . Models trained using molecular feature data combined with clinical data ( category ‘MC’ ) outperformed the baseline clinical model in 10 out of 28 ( 36% ) submissions , suggesting there is some difficulty in the naïve incorporation of molecular feature data compared to using only clinical information . In fact , the best MC model attributed lower weights to molecular compared to clinical features by rank-transforming all the features ( molecular and clinical ) and training an elastic net model , imposing a penalty only on the molecular features and not on the clinical ones , such that the clinical features are always included in the trained model . This model achieved a concordance index of 0 . 6593 , slightly better than the best-performing clinical only model . One of the most successful approaches to addressing the curse of dimensionality in genomics problems has been to utilize domain-specific prior knowledge to pre-select features more likely to be associated with the phenotype of interest [35] . Indeed , the majority of submitted models ( 66 of 110 , 60% ) utilized a strategy of pre-selecting features based on external prior knowledge . Interestingly , analysis of model submission dates indicates that participants first attempted naïve models incorporating all molecular features , and after achieving small performance improvements over clinical only models , evolved to incorporate prior information as the dominant modeling strategy in the later phase of the competition ( Figure 2B ) . This observation is consistent with previous reports highlighting the importance of real-time feedback in motivating participants to build continuously improving models [36] . All models trained on only the molecular features ( i . e . excluding the clinical features ) and incorporating prior knowledge ( MP category ) performed worse than the baseline model , with the highest concordance index being 0 . 5947 , further highlighting the difficultly in using molecular information alone to improve prognostic accuracy compared to clinical data . Twenty-four models outperformed the baseline by combining clinical features with molecular features selected by prior knowledge ( MPC category ) . The overall best-performing model attained a concordance index of 0 . 6707 by training a machine learning method ( boosted regression ) on a combination of: 1 ) clinical features; 2 ) expression levels of genes selected based on both data driven criteria and prior knowledge of their involvement in breast cancer ( the MASP feature selection strategy , as described in Methods ) ; 3 ) an aggregated “genomic instability” index calculated from the copy number data ( see Methods ) . The wide range of concordance index scores for models in the MPC category raises the question of whether the improved performance of the best MPC models are explained by the biological relevance of the selected features or simply by random fluctuations in model scores when testing many feature sets . Due to the uncontrolled experimental design inherent in accepting unconstrained model submissions , additional evaluations are needed to assess the impact of different modeling choices in a controlled experimental design . We describe the results of this experiment next . We analyzed the modeling strategies utilized in the original “uncontrolled” model submission phase and designed a “controlled” experiment to assess the associations of different modeling choices with model performance . We determined that most models developed in the uncontrolled experiment could be described as the combination of a machine learning method with a feature selection strategy . We therefore tested models trained using combinations of a discrete set of machine learning methods crossed with feature selection strategies using the following experimental design: This experiment design resulted in a total of 60 models based on combinations of modeling strategies from the uncontrolled experiment ( Table S4 ) , plus 20 models using ensemble strategies . This controlled experimental design allowed us to assess the effect of different modeling choices while holding other factors constant . Following an approach suggested in the MAQC-II study [24] , we designed negative and positive control experiments to infer bounds on model performance in prediction problems for which models should perform poorly and well , respectively . As a negative control , we randomly permuted the sample labels of the survival data , for both the training and test datasets , and computed the concordance index of each model trained and tested on the permuted data . To evaluate how the models would perform on a relatively easy prediction task , we conducted a positive control experiment in which all models were used to predict the ER status of the patients based on selected molecular features ( excluding the ER expression measurement ) . We found that all negative control models scored within a relatively tight range of concordance indices centered around 0 . 5 ( minimum: 0 . 468 , maximum: 0 . 551 ) , significantly lower than the lowest concordance index ( 0 . 575 ) of any model trained on the real data in this experiment . Conversely , all ER-prediction models scored highly ( minimum: 0 . 79 , maximum: 0 . 969 ) , suggesting that the scores achieved by our survival models ( maximum: 0 . 6707 ) are not due to a general limitation of the selected modeling strategies but rather the difficulty of modeling breast cancer survival . Overall , we found that the predictive performance of the controlled experiment models ( Figure 3A ) was significantly dependent on the individual feature sets ( P = 1 . 02e-09 , F-test ) , and less dependent on the choice of the statistical learning algorithm ( P = 0 . 23 , F-test ) . All model categories using clinical covariates outperformed all model categories trained excluding clinical covariates , based on the average score across the 4 learning algorithms . The best-performing model category selected features based on marginal correlation with survival , further highlighting the difficulty in purely data-driven approaches , and the need to incorporate prior knowledge to overcome the curse of dimensionality . The best-performing model used a random survival forest algorithm trained by combining the clinical covariates with a single additional aggregate feature , called the genomic instability index ( GII ) , calculated as the proportion of amplified or deleted sites based on the copy number data . This result highlights the importance of evaluating models using a controlled experimental design , as the best-performing method in the uncontrolled experiment combined clinical variables with GII in addition to selected gene expression features ( clinical variables plus only GII was not evaluated ) , and the controlled experiment pointed to isolating GII as the modeling insight associated with high prediction accuracy . The random survival forest trained using clinical covariates and GII was significantly better than a random survival forest trained using clinical covariates alone ( P = 2e-12 by paired Wilcoxon signed rank test based on 100 bootstrap samples with replacement from the test dataset ) . We also tested if inclusion of the GII feature improved model performance beyond a score that could be obtained by chance based on random selection of features . We trained 100 random survival forest models and 100 boosting models , each utilizing clinical information in addition to random selections of 50 molecular features ( corresponding to the number of features used based on the MASP strategy , which achieved the highest score of all feature selection methods ) . The best-performing model from our competition ( trained using clinical covariates and GII ) achieved a higher score than each of these 100 models for both learning algorithms ( P< = . 01 ) . The use of the aggregate GII feature was based on previous reports demonstrating the association between GII and poor prognosis breast cancer subtypes like Luminal B , HER2+ and Basal-like tumors [37] . We found that HER2+ tumors had the strongest association with the GII score ( P = 1 . 65e-12 , t-test ) which partly explains why it performs so well considering none of the patients were treated with compounds that target the HER2 pathway ( e . g . Herceptin ) . Samples with high GII scores were also associated with high-grade tumors ( P = 7 . 13e-13 , t-test ) , further strengthening its credential as a good survival predictor . However , despite these strong associations , the genomic instability index provided an added value to the strength of predictions even as clinical covariates histologic grade and HER2 status are used in the models . Boosting was the best-performing method on average . Elastic net and lasso exhibited stable performance across many feature sets . Random survival forests performed very well when trained on a small number of features based on clinical information and the genomic instability index . However , their performance decreased substantially with the inclusion of large molecular feature sets . Ensemble methods trained by averaging predicted ranks across multiple methods systematically performed better than the average concordance index scores of the models contained in the ensemble , consistent with previously reported results [38] . Strikingly , an ensemble method aggregating all 60 models achieved a concordance index score of . 654 , significantly greater than the average of all model scores ( . 623 ) ( Figure 3B ) . The ensemble performed better than the average model score for each of 100 resampled collections of 60 models each , using bootstrapping to sample with replacement from all 60 models ( P< = . 01 ) . The ensemble model scored better than 52 of the 60 ( 87% ) models that constituted the ensemble . We note that 2 of the algorithms ( boosting and random forests ) utilize ensemble learning strategies on their own . For both of the other 2 algorithms ( lasso and elastic net ) the method trained on an ensemble of the 15 feature sets scored higher than each of the 15 models trained on the individual feature sets ( Figure 3B ) . Consistent with previous reports , the systematic outperformance of ensemble models compared to their constituent parts suggests that ensemble approaches effectively create a consensus that enhances the biologically meaningful signals captured by multiple modeling approaches . As previously suggested in the context of the DREAM project [38]–[41] , our finding further reinforces the notion that crowd-sourced collaborative competitions are a powerful framework for developing robust predictive models by training an ensemble model aggregated across diverse strategies employed by participants . In the first round of the competition , we did not restrict the number of models a participant could submit . This raises the possibility of model overfitting to the test set used to provide real-time feedback . We therefore used 2 additional datasets to evaluate the consistency of our findings . The first dataset , which we called METABRIC2 , consisted of the 988 samples ( excluding those with missing survival data ) from the METABRIC cohort that were not used in either the training dataset or the test dataset used for real-time evaluation . The second dataset , called MicMa , consisted of 102 samples with gene expression , clinical covariates , and survival data available [4] , [28] and copy number data presented in the current study ( see Methods ) . We used the models from our controlled experiment , which were trained on the original 500 METABRIC samples , and evaluated the concordance index of the survival predictions of each model compared to observed survival in both METABRIC2 and MicMa . The concordance index scores across models from the original evaluation were highly consistent in both METABRIC2 and MicMa . The 60 models evaluated in the controlled experiment ( 15 feature sets used in 4 learning algorithms ) had Pearson correlations of . 87 ( P<1e-10 ) compared to METABRIC2 ( Figure 4A ) and . 76 ( P<1e-10 ) compared to MicMa ( Figure 4C ) , although we note that p-values may be over-estimated due to smaller effective sample sizes due to non-independence of modeling strategies . Model performance was also strongly correlated for each different algorithm across the feature sets for both METABRIC2 ( Figure 4B ) and MicMa ( Figure 4D ) . Consistent with results from the original experiment , the top scoring model , based on average concordance index of the METABRIC2 and MicMa scores , was a random survival forest trained using clinical features in combination with the GII . The second best model corresponded to the best model from the uncontrolled experiment ( 3rd best model in the controlled experiment ) , and used clinical data in combination with GII and the MASP feature selection strategy , and was trained using a boosting algorithm . A random forest trained using only clinical data achieve the 3rd highest score . The top 39 models all incorporated clinical data . As an additional comparison , we generated survival predictions based on published procedures used in the clinically approved MammaPrint [6] and Oncotype DX [7] assays . We note that these assays are designed specifically for early stage , invasive , lymph node negative breast cancers ( in addition ER+ in the case of Oncotype DX ) and use different scores calculated from gene expression data measured on distinct platforms . It is thus difficult to reproduce exactly the predictions provided by these assays or to perform a fair comparison to the present methods on a dataset that includes samples from the whole spectrum of breast tumors . The actual Oncotype DX score is calculated from RT-PCR measurements of the mRNA levels of 21 genes . Using z-score normalized gene expression values from METABRIC2 and MicMa datasets , together with their published weights , we recalculated Oncotype DX scores in an attempt to reproduce the actual scores as closely as possible . We then scored the resulting predictions against the two datasets and obtained concordance indices of 0 . 6064 for METABRIC2 and 0 . 5828 for MicMa , corresponding to the 81st ranked model based on average concordance index out of all 97 models tested , including ensemble models and Oncotype DX and MammaPrint feature sets incorporated in all learning algorithms ( see Table S5 ) . Similarly , the actual MammaPrint score is calculated based on microarray gene expression measurements , with each patient's score determined by the correlation of the expression of 70 specific genes to the average expression of these genes in patients with good prognosis ( defined as those who have no distant metastases for more than five years , ER+ tumors , age less than 55 years old , tumor size less than 5 cm , and are lymph node negative ) . Because of limitations in the data , we were not able to compute this score in exactly the same manner as the original assay ( we did not have the metastases free survival time , and some of the other clinical features were not present in the validation datasets ) . We estimated the average gene expression profile for the 70 MammaPrint genes based on all patients who lived longer than five years ( with standardized gene expression data ) , then computed each patient's score as their correlation to this average good prognosis profile . We scored the predictions against the two validation datasets and observed concordance indices of 0 . 602 in METABRIC2 and 0 . 598 in MicMa , corresponding to the 78th ranked out of 97 models based on average concordance index . We were able to significantly improve the scores associated with both MammaPrint and Oncotype DX by incorporating the gene expression features utilized by each assay as feature selection criteria in our prediction pipelines . We trained each of the 4 machine learning algorithms with clinical features in addition to gene lists from MammaPrint and Oncotype DX . The best-performing models would have achieved the 8th and 26th best scores , respectively , based on average concordance index in METABRIC2 and MicMa . We note that using the ensemble strategy of combining the 4 algorithms , the model trained using Mammaprint genes and clinical data performed better than clinical data alone , and achieved the 5th highest average model score , including the top score in METABRIC2 , slightly ( . 005 concordance index difference ) better than the random forest model using clinical data combined with GII , though only the 17st ranked score in MicMa . This result suggests that incorporating the gene expression features identified by these clinically implemented assays into the prediction pipeline described here may improve prediction accuracy compared to current analysis protocols . An ensemble method , aggregating results across all learning algorithms and feature sets , performed better than 71 of the 76 models ( 93% ) that constituted the ensemble , consistent with our finding that the ensemble strategy achieves performance among the top individual approaches . For the 19 feature selection strategies used in the METABRIC2 and MicMa evaluations , an ensemble model combining the results of the 4 learning algorithms performed better than the average of the 4 learning algorithms in 36 out of 38 cases ( 95% ) . Also consistent with our previous result , for both algorithms that did not use ensemble strategies themselves ( elastic net and lasso ) , an ensemble model aggregating results across the 19 feature sets performed better than each of the individual 19 feature sets for both METABRIC2 and MicMa . Taken together , the independent evaluations in 2 additional datasets are consistent with the conclusions drawn from the original real-time feedback phase of the completion , regarding improvements gained from ensemble strategies and the relative performance of models . “Precision Medicine” , as defined by the Institute of Medicine Report last year , proposes a world where medical decisions will be guided by molecular markers that ensure therapies are tailored to the patients who receive them [42] . Moving towards this futuristic vision of cancer medicine requires systematic approaches that will help ensure that predictive models of cancer phenotypes are both clinically meaningful and robust to technical and biological sources of variation . Despite isolated successful developments of molecular diagnostic and personalized medicine applications , such approaches have not translated to routine adoption in standard-of-care protocols . Even in applications where successful molecular tests have been developed , such as breast cancer prognosis [5] , [6] , a plethora of research studies have claimed to develop models with improved predictive performance . Much of this failure has been attributed to “difficulties in reproducibility , expense , standardization and proof of significance beyond current protocols” [43] . The propensity of researchers to over-report the performance of their own approaches has been deemed the “self-assessment trap” [19] . We propose community-based collaborative competitions [43]–[49] as a general framework to develop and evaluate predictive models of cancer phenotypes from high-throughput molecular profiling data . This approach overcomes limitations associated with the design of typical research studies , which may conflate self-assessment with methodology development or , even more problematic , with data generation . Thus competition-style research may promote transparency and objective assessment of methodologies , promoting the emergence of community standards of methodologies most likely to yield translational clinical benefit . The primary challenge of any competition framework is to ensure that mechanisms are in place to prevent overfitting and fairly assess model performance , since performance is only meaningful if models are ranked based on their ability to capture some underlying signal in the data . For example , such an approach requires datasets affording sufficient sample sizes and statistical power to make meaningful comparisons of many models across multiple training and testing data subsets . We propose several strategies for assessing if the results obtained from a collaborative competition are likely to generalize to future applications and improve on state-of-the art methodologies that would be employed by an expert analyst . First , baseline methods should be provided as examples of approaches an experienced analyst may apply to the problem . In our study , we employed a number of such methods for comparison , including methodologies used in clinical diagnostic tests and multiple state-of-the-art machine learning methods trained using only clinical covariates . Second , performance of models should be evaluated in multiple rounds of independent validation . In this study , we employed a multi-phase strategy suggested by previous researchers [50] in which a portion of the dataset is held back to provide real-time feedback to participants on model performance and another portion of the dataset is held back and used to score the performance of all models , such that participants cannot overfit their models to the test set . If possible , we recommend an additional round of validation using a dataset different from the one used in previous rounds , in order to test against the possibility that good performance is due to modeling confounding variables in the original dataset . This experimental design provides 3 independent rounds of model performance assessment , and consistent results across these multiple evaluations provides strong evidence that performance of the best approaches discovered in this experimental design are likely to generalize in additional datasets . Finally , statistical permutation tests can provide useful safeguards against the possibility that improved model performance is attributable to random fluctuations based on evaluation of many models . Such tests should be designed carefully based on the appropriate null hypothesis . A useful , though often insufficient , test is to utilize a negative control null model , for example by permuting the sample labels of the response variable . We suggest that additional tests may be employed as post-hoc procedures designed specifically to provide falsifiable hypotheses that may provide alternative explanations of model performance . For example , in this study we assessed the performance of many models trained using the same learning algorithm ( random survival forest ) and the same clinical features as used in the top scoring model , but using random selections of molecular features instead of the GII feature . This test was designed to falsify the hypothesis that model performance is within the range of likely values based on random selection of features , as has been a criticism of previously reported models [18] . We suggest that the guidelines listed above provide a useful framework in reporting the results of a collaborate competition , and may even be considered necessary criteria to establish the likelihood that findings will generalize to future applications . As with most research studies , a single competition cannot comprehensively assess the full extent to which findings may generalize to all potentially related future applications . Accordingly , we suggest that a collaborative competition should indeed report the best forming model , provided it meets the criteria listed above , but need not focus on declaring a single methodology as conclusively better than all others . By analogy to athletic competitions such as an Olympic track race , a gold medal is given to the runner with the fastest time , even if by a fraction of a second . Judgments of superior athletes emerge through integrating multiple such data points across many races against different opponents , distances , weather conditions , etc . , and active debate among the community . A research study framed as a collaborative competition may facilitate the transparency , reproducibility , and objective evaluation criteria that provide the framework on which future studies may build and iterate towards increasingly refined assessments through a continuous community-based effort . Within several months we developed and evaluated several hundred modeling approaches . Our research group consisted of experienced analysts trained as both data scientists and clinicians , resulting in models representing state-of-the art approaches employed in both machine learning and clinical cancer research ( Table 3 ) . By conducting detailed post-hoc analysis of approaches developed by this group , we were able to design a controlled experiment to isolate the performance improvements attributable to different strategies , and to potentially combine aspects of different approaches into a new method with improved performance . The design of our controlled experiment builds off pioneering work by the MAQC-II consortium , which compiled 6 microarray datasets from the public domain and assessed modeling factors related to the ability to predict 13 different phenotypic endpoints . MAQC-II classified each model based on several factors ( type of algorithm , normalization procedure , etc ) , allowing analysis of the effect of each modeling factor on performance . Our controlled experiment follows this general strategy , and extends it in several ways . First , MAQC-II , and most competition-base studies [20] , [22] , [26] , accept submissions in the form of prediction vectors . We developed a computational system that accepts models as re-runnable source code implementing a simple train and predict API . Source code for all submitted models are stored in the Synapse compute system [51] and are freely available to the community . Thus researchers may reproduce reported results , verify fair play and lack of cheating , learn from the best-performing models , reuse submitted models in related applications ( e . g . building prognostic models in other datasets ) , build ensemble models by combining results of submitted models , and combine and extend innovative ideas to develop novel approaches . Moreover , storing models as re-runnable source code is important in assessing the generalizability and robustness of models , as we are able to re-train models using different splits or subsets of the data to evaluate robustness , and we ( or any researcher ) can evaluate generalizability by assessing the accuracy of a model's predictions in an independent dataset , such as existing related studies [5] or emerging clinical trial data [52] . We believe this software system will serve as a general resource that is extended and re-used in many future competition-based studies . Second , MAQC-II conducted analysis across multiple phenotypic endpoints , which allowed models to be re-evaluated in the context of many prediction problems . However , this design required models to be standardized across all prediction problems and did not allow domain-specific insights to be assessed for each prediction problem . By contrast , our study focused on the single biomedical problem of breast cancer prognosis , and allowed clinical research specialists to incorporate expert knowledge into modeling approaches . In fact , we observed that feature selection strategies based on prior domain-specific knowledge had a greater effect on model performance than the choice of learning algorithm , and learning algorithms that did not incorporate prior knowledge were unable to overcome challenges with incorporating high-dimensional feature data . In contrast to previous reports that have emphasized abstracting away domain-specific aspects of a competition in order to attract a broader set of analysis [50] , in real-word problems , we emphasize the benefit of allowing researchers to apply domain-specific expertise and objectively test the performance of such approaches against those of analysts employing a different toolbox of approaches . Finally , whereas MAQC-II employed training and testing splits of datasets for model evaluation , our study provides an additional level of evaluation in a separate , independent dataset generated on a different cohort and using different gene expression and copy number profiling technology . Consistent with findings reported by MAQC-II , our study demonstrates strong consistency of model performance across independent evaluations and provides an important additional test of model generalizability that more closely simulates real-world clinical applications , in which data is generated separately from the data used to construct models . More generally , whereas MAQC-II evaluated multiple prediction problems in numerous datasets with gene expression data and samples numbers from 70 to 340 , our study went deeper into a evaluating a single prediction problem , utilizing copy number and clinical information in addition to gene expression , and with a dataset of 2 , 000 samples in addition to an independently-generated dataset with 102 samples . The model achieving top performance in both the initial evaluation phase and the evaluation in additional datasets combined a state-of-the-art machine learning approach ( random survival forest ) with a clinically motivated feature selection strategy that used all clinical features together with an aggregate genomic instability index . Interestingly , this specific model was not tested in the uncontrolled phase , and was the result of the attempt to isolate and combine aspects of different modeling approaches in a controlled experiment . The genomic instability index measure may serve as a proxy for the degree to which DNA damage repair pathways ( including , for instance , housekeeping genes like p53 and RB ) have become dysregulated [37] . Beyond the specifics of the top performing models , we believe the more significant contribution of this work is as a building block , providing a set of baseline findings , computational infrastructure , and proposed research methodologies used to assess breast cancer prognosis models , and extending in the future to additional phenotype prediction problems . Towards this end , we have recently extended this work into an open collaborative competition through which any researcher can freely register and evaluate the performance of submitted models against all others submitted throughout the competition . Though this expanded breast cancer competition , and future phenotype prediction competitions to be hosted as extensions of the current work , we invite researchers to improve , refute , and extend our findings and research methodologies to accelerate the long arc of cumulative progress made by the community through a more transparent and objectively assessed process . Our competition was designed to assess the accuracy of predicting patient survival ( using the overall survival metric , median 10 year follow-up ) based on feature data measured in the METABRIC cohort of 980 patients , including gene expression and copy number profiles and 16 clinical covariates ( Table 1 ) . Participants were given a training dataset consisting of data from 500 samples , and data from the remaining 480 were hidden from participants and used as a validation dataset to evaluate submitted models . We developed the computational infrastructure to support the competition within the open-source Sage Synapse software platform . Detailed documentation is available on the public competition website: https://sagebionetworks . jira . com/wiki/display/BCC/Home . The system is designed to generalize to support additional community-based competitions and consists of the following components ( Figure 5 ) : All models are available with downloadable source code using the Synapse IDs displayed in Table S1 and Table S4 . An automated script continuously monitored for new submissions , which were sent to worker nodes in a computational cluster for scoring . Each worker node ran an evaluation script , which called the submitted model's customPredict method with arguments corresponding to the gene expression , copy number , and clinical covariate values in the held-out validation dataset . This function returns a vector of predicted survival times in the validation dataset , which were used to calculate the concordance index as a measure of accuracy compared to the measured survival times for the same samples . Concordance index scores were shown in a real-time leaderboard , similar to the leaderboards displaying the models scores shown in Table S1 and Table S4 . Concordance index ( c-index ) is the standard metric for evaluation of survival models [53] . The concordance index ranges from 0 in the case of perfect anti-correlation between the rank of predictions and the rank of actual survival time through 0 . 5 in the case of predictions uncorrelated with survival time to 1 in the case of exact agreement with rank of actual survival time . We implemented a method to compute the exact value of the concordance index by exhaustively sampling all pairwise combinations of samples rather than the usual method of stochastically sampling pairwise samples . This method overcomes the stochastic sampling used in standard packages for concordance index calculation and provides a deterministic , exact statistic used to compare models . Data on the original 980 samples were obtained for this study in early January , 2012 . Study design and computational infrastructure were developed from then until March 14th , at which point participants were given access to the 500 training samples and given 1 month to develop models in the “uncontrolled experiment” phase . During this time , participants were given real-time feedback on model performance evaluated against the held-out test set of 480 samples . After this 1-month model development phase , all models were frozen and inspected by the group to conduct post-hoc model evaluation and identify modeling strategies used to design the controlled evaluation . All models in the controlled evaluation were re-trained on the 500 training samples and re-evaluated on the 480 test samples . After all evaluation was completed based on the original 980 samples , the METABRIC2 and MicMa datasets became available , and were used to perform additional evaluations of all models , which was conducted between January 2013–March 2013 . For the new evaluation , all data was renormalized to the gene level , as described below , in order to allow comparison of models across datasets performed on different platforms . Models were retrained using the re-normalized data for the same 500 samples in the original training set . All model source code is available in the subfolders of Synapse ID syn160764 , and specific Synapse IDs for each model are listed in Table S1 and Table S4 . Data stored in Synapse may be accessed using the Synapse R client ( https://sagebionetworks . jira . com/wiki/display/SYNR/Home ) or by clicking the download icon on the web page corresponding to each model , allowing the user to download a Zip archive containing the source files contained in the submission . The METABRIC dataset used in the competition contains gene expression data from the Illumina HT 12v3 platform and copy number data derived from experiments performed on the Affymetrix SNP 6 . 0 platform . In the initial round of analysis , the first 980 samples data was normalized as described in [29] , corresponding to the data available in the European Genome-Phenome Archive ( http://www . ebi . ac . uk/ega ) , accession number EGAS00000000083 . Copy number data was summarized to the gene level by calculating the mean value of the segmented regions overlapping a gene . Data for use in our study are available in the Synapse software system ( synapse . sagebase . org ) within the folder with accession number syn160764 ( https://synapse . prod . sagebase . org/#Synapse:syn160764 ) , subject to terms of use agreements described below . Data may be loaded directly in R using the Synapse R client or downloaded from the Synapse web site . Patients treated for localized breast cancer from 1995 to 1998 at Oslo University Hospital were included in the MicMa cohort , and 123 of these had available fresh frozen tumor material [4] , [28] . Gene expression data for 115 cases obtained from an Agilent whole human genome 4×44 K one color oligo array was available ( GSE19783 ) [54] . Novel SNP-CGH data from 102 of the MicMa samples were obtained using the Illumina Human 660k Quad BeadChips according to standard protocol . Normalized LogR values summarized to gene level were made available and are accessible in Synapse ( syn1588686 ) . All data used for the METABRIC2 and MicMa analyses are available as subfolders of Synapse ID syn1588445 . For comparison of METABRIC2 and MicMa , we standardized all clinical variables , copy number , and gene expression data across both datasets . Clinical variables were filtered out that were not available in both datasets . Data on clinical variables used in this comparison are available in Synapse . All gene expression datasets were normalized according the supervised normalization of microarrays ( snm ) framework and Bioconductor package [55] , [56] . Following this framework we devised models for each dataset that express the raw data as functions of biological and adjustment variables . The models were built and implemented through an iterative process designed to learn the identity of important variables . Once these variables were identified we used the snm R package to remove the effects of the adjustment variables while controlling for the effects of the biological variables of interest . SNP6 . 0 copy number data was also normalized using the snm framework , and summarization of probes to genes was done as follows . First , probes were mapped to genes using information obtained from the pd . genomewidesnp . 6 Bioconductor package [57] . For genes measured by two probes we define the gene-level values as an unweighted average of the probes' data . For genes measured by a single probe we define the gene-level values as the data for the corresponding probe . For those measured by more than 2 probes we devised an approach that weights probes based upon their similarity to the first eigengene . This is accomplished by taking a singular value decomposition of the probe-level data for each gene . The percent variance explained by the first eigengene is then calculated for each probe . The summarized values for each gene are then defined as the weighted mean with the weights corresponding to the percent variance explained . For Illumina 660k data we processed the raw files using the crlmm bioconductor R package [58] . The output of this method produces copy number estimates for more than 600k probes . Next , we summarized probes to Entrez gene ids using a mapping file obtained from the Illumina web site . For genes measured by more than two probes we selected the probe with the largest variance . Feature selection strategies used in the controlled experiment ( identified through post-hoc analysis of the uncontrolled experiment ) are described briefly in Table 3 . Specific genes used in each category are available within Synapse ID syn1643406 and can be downloaded as R binaries via the Synapse web client or directly loaded in R using the Synapse R client . Most feature selection strategies are sufficiently described in Table 3 , and we provide additional details on 2 methods below . The MASP ( Marginal Association with Subsampling and Prior Knowledge ) algorithm employs the following procedure: all genes were first scored for association with survival ( using Cox regression ) in chunks of 50 randomly selected gene expression samples . This process was repeated 100 times which resulted in an overall survival association score where is the p-value associated with the Cox regression on the expression of gene i in sample set j . All genes were sorted in descending order by their survival association score and the top 50 oncogenes and transcription factors were kept . A list of human transcription factors was obtained from [59] and a list of oncogenes was compiled by searching for relevant keywords against the Entrez gene database . GII is a measure of the proportion of amplified or deleted genomic loci , calculated from the copy number data . Copy number values are presented as segmented log-ratios with respect to normal controls . Amplifications and deletions are thus counted when or and devided by the total number of loci . The data used in this study were collected and analyzed under approval of an IRB [29] . The MicMa study was approved by the Norwegian Regional Committee for medical research ethics , Health region II ( reference number S-97103 ) . All patients have given written consent for the use of material to research purposes .
We developed an extensible software framework for sharing molecular prognostic models of breast cancer survival in a transparent collaborative environment and subjecting each model to automated evaluation using objective metrics . The computational framework presented in this study , our detailed post-hoc analysis of hundreds of modeling approaches , and the use of a novel cutting-edge data resource together represents one of the largest-scale systematic studies to date assessing the factors influencing accuracy of molecular-based prognostic models in breast cancer . Our results demonstrate the ability to infer prognostic models with accuracy on par or greater than previously reported studies , with significant performance improvements by using state-of-the-art machine learning approaches trained on clinical covariates . Our results also demonstrate the difficultly in incorporating molecular data to achieve substantial performance improvements over clinical covariates alone . However , improvement was achieved by combining clinical feature data with intelligent selection of important molecular features based on domain-specific prior knowledge . We observe that ensemble models aggregating the information across many diverse models achieve among the highest scores of all models and systematically out-perform individual models within the ensemble , suggesting a general strategy for leveraging the wisdom of crowds to develop robust predictive models .
You are an expert at summarizing long articles. Proceed to summarize the following text: Listeria monocytogenes is an opportunistic Gram-positive bacterial pathogen responsible for listeriosis , a human foodborne disease . Its cell wall is densely decorated with wall teichoic acids ( WTAs ) , a class of anionic glycopolymers that play key roles in bacterial physiology , including protection against the activity of antimicrobial peptides ( AMPs ) . In other Gram-positive pathogens , WTA modification by amine-containing groups such as D-alanine was largely correlated with resistance to AMPs . However , in L . monocytogenes , where WTA modification is achieved solely via glycosylation , WTA-associated mechanisms of AMP resistance were unknown . Here , we show that the L-rhamnosylation of L . monocytogenes WTAs relies not only on the rmlACBD locus , which encodes the biosynthetic pathway for L-rhamnose , but also on rmlT encoding a putative rhamnosyltransferase . We demonstrate that this WTA tailoring mechanism promotes resistance to AMPs , unveiling a novel link between WTA glycosylation and bacterial resistance to host defense peptides . Using in vitro binding assays , fluorescence-based techniques and electron microscopy , we show that the presence of L-rhamnosylated WTAs at the surface of L . monocytogenes delays the crossing of the cell wall by AMPs and postpones their contact with the listerial membrane . We propose that WTA L-rhamnosylation promotes L . monocytogenes survival by decreasing the cell wall permeability to AMPs , thus hindering their access and detrimental interaction with the plasma membrane . Strikingly , we reveal a key contribution of WTA L-rhamnosylation for L . monocytogenes virulence in a mouse model of infection . Listeria monocytogenes ( Lm ) is a ubiquitous Gram-positive bacterium and the causative agent of listeriosis , a human foodborne disease with high incidence and morbidity in immunocompromised hosts and other risk groups , such as pregnant women , neonates and the elderly . Clinical manifestations range from febrile gastroenteritis to septicemia , meningitis and encephalitis , as well as fetal infections that can result in abortion or postnatal health complications [1] . The most invasive and severe forms of the disease are a consequence of the ability of this pathogen to overcome important physiological barriers ( intestinal epithelium , blood-brain barrier and placenta ) by triggering its internalization and promoting its intracellular survival into phagocytic and non-phagocytic cells . Once inside a host cell , a tightly coordinated life cycle , whose progression is mediated by several specialized bacterial factors , enables Lm to proliferate and spread to neighboring cells and tissues [2 , 3] . The Lm cell wall is composed of a thick peptidoglycan multilayer that serves as a scaffold for the anchoring of proteins , among which are several virulence factors [4] , and of glycopolymers such as teichoic acids , which account for up to 70% of the protein-free cell wall mass [5 , 6] . These anionic polymers are divided into membrane-anchored teichoic acids ( lipoteichoic acids , LTAs ) and peptidoglycan-attached teichoic acids ( wall teichoic acids , WTAs ) . In Listeria , WTAs are mainly composed of repeated ribitol-phosphate subunits , whose hydroxyl groups can be substituted with a diversity of monosaccharides [5] . While the polymer structure and the chemical identity of the substituent groups of LTAs are rather conserved across listeriae [7 , 8] , they display a high variability in WTAs , even within the same species [9] . Specific WTA substitution patterns are characteristic of particular Lm serotypes: N-acetylglucosamine is common to serogroups 1/2 and 3 , and to serotype 4b , but serogroup 1/2 also contains l-rhamnose , whereas serotype 4b displays d-glucose and d-galactose [10] . The broad structural and chemical similarity of LTAs and WTAs results in a considerable degree of functional redundancy , which has complicated the characterization of these macromolecules and the assignment of specific biological roles . However , studies on Gram-positive bacteria have revealed their contribution to important physiological functions ( e . g . cell envelope cationic homeostasis [11] , regulation of autolysin activity [12] , assembly of cell elongation and division machineries [13] , defense against antimicrobial peptides [14] ) and to virulence-promoting processes , such as adhesion and colonization of host tissues [15 , 16] . Antimicrobial peptides ( AMPs ) are a large family of small peptides ( <10 kDa ) produced by all forms of living organisms [17] , which constitute a major player of the innate immune response against microbial pathogens . Despite their structural diversity , the majority of AMPs share both cationic and amphipathic properties that favor respectively their interaction with the negatively charged prokaryotic surface and insertion into the plasma membrane [17 , 18] . Subsequent pore formation or other AMP-mediated membrane-disrupting mechanisms induce bacterial death through direct cell lysis or deleterious interaction with intracellular targets [19] . Bacteria have evolved multiple strategies to avert killing by AMPs [20 , 21] . One strategy consists in the modification of their cell surface charge , a process achieved mainly by masking anionic glycopolymers with positively charged groups , thus decreasing their affinity to AMPs . In Gram-positive pathogens , d-alanylation of teichoic acids is a well-characterized mechanism and was demonstrated to be important for bacterial resistance to host-secreted AMPs [22 , 23] . In contrast , the contribution of WTA glycosylation mechanisms in AMP resistance has not yet been investigated . We have previously reported genome-wide transcriptional changes occurring in Lm strain EGD-e during mouse infection [24] . Our analysis revealed an elevated in vivo expression of the lmo1081-1084 genes , here renamed as rmlACBD because of the high homology of the corresponding proteins with enzymes of the l-rhamnose biosynthesis pathway . In this work , we show that the decoration of Lm WTAs with l-rhamnose requires the expression of not only the rmlACBD locus but also of rmlT , an upstream-flanking gene encoding a putative rhamnosyltransferase . We also demonstrate that Lm becomes more susceptible to AMPs in the absence of WTA l-rhamnosylation and predict that this effect is due to an increase of the Lm cell wall permeability to these bactericides , which results in a faster disruption of the plasma membrane integrity with lethal consequences for the bacterial cell . Importantly , we present evidence that this WTA tailoring process is required for full-scale Lm virulence in the mouse model of infection . To identify new Lm genes potentially critical for the infectious process , we previously performed the first in vivo transcriptional profiling of Lm EGD-e . Among the Lm genes displaying the largest increase in transcription throughout infection , we identified a set of previously uncharacterized genes that are included in a pentacistronic operon ( lmo1080 to lmo1084 ) [25] . This operon is found in L . monocytogenes strains belonging to serogroups 1/2 , 3 and 7 , and is absent from serogroup 4 strains [26] ( Fig 1 ) . Interestingly , aside from Listeria seeligeri 1/2b strains , this locus is not found in any other Listeria spp . , such as the nonpathogenic Listeria innocua or the ruminant pathogen Listeria ivanovii , which pinpoints it as a genetic feature of a particular subset of pathogenic Listeria strains and suggests that its expression may be important to Listeria pathogenesis in humans . The four proteins encoded by the lmo1081-lmo1084 genes share a high amino acid sequence homology with the products of the rmlABCD gene cluster . These genes are widely distributed among Gram-negative ( e . g . Salmonella enterica [27] , Shigella flexneri [28] , Vibrio cholerae [29] , Pseudomonas aeruginosa [30] ) and Gram-positive species ( e . g . Mycobacterium tuberculosis [31] , Streptococcus mutans [32] , Geobacillus tepidamans [33] , Lactobacillus rhamnosus [34] ) ( Fig 1 ) , the majority of which being known pathogens or potentially pathogenic . Despite the inter-species variability observed in the genetic organization of the rml genes , the respective proteins exhibit a remarkable degree of conservation ( S1 Table in S1 Text ) . In light of this , we renamed the lmo1081-lmo1084 genes to rmlACBD , respectively ( Fig 1 ) . The RmlABCD proteins catalyze the conversion of glucose-1-phosphate to a thymidine-diphosphate ( dTDP ) -linked form of l-rhamnose [35] ( S1A Fig in S1 Text ) , which is a component of the WTAs from most Listeria strains possessing the rml genes [6] . To address the role of rmlACBD in Lm WTA glycosylation with l-rhamnose , we constructed an Lm EGD-e derivative mutant strain lacking the rmlACBD locus ( ΔrmlACBD ) ( S2A Fig in S1 Text ) and investigated if the absence of these genes could affect the WTA l-rhamnosylation status . We prepared WTA hydrolysates from exponential phase cultures of wild type ( EGD-e ) , ΔrmlACBD and a complemented ΔrmlACBD strain expressing rmlACBD from its native promoter within an integrative plasmid ( ΔrmlACBD+rmlACBD ) . Samples were resolved by native PAGE and the gel stained with Alcian blue to visualize WTA polymer species . A mutant strain unable to synthesize WTAs ( ΔtagO1ΔtagO2 ) [36] was used to confirm that the detected signal corresponds to WTAs . Compared to the wild type sample , the ΔrmlACBD WTAs displayed a shift in migration , which was reverted to a wild type-like profile in WTAs from the ΔrmlACBD+rmlACBD sample ( Fig 2A ) , indicating that the native WTA composition requires the presence of the rmlACBD genes . To confirm this , we investigated the WTA carbohydrate composition from these strains . WTA polymers were isolated from cell walls purified from bacteria in exponential growth phase , hydrolyzed and analyzed by high-performance anion exchange chromatography coupled with pulsed amperometric detection ( HPAEC-PAD ) to detect monosaccharide species . WTA extracts obtained from ΔrmlACBD bacteria completely lacked l-rhamnose , in contrast to those isolated from the parental wild type strain ( Fig 2B ) . The role of rmlACBD in Lm WTA l-rhamnosylation was definitely confirmed by the analysis of WTAs from ΔrmlACBD+rmlACBD bacteria , in which l-rhamnose was detected at levels similar to those observed in the wild type sample ( Fig 2B ) . Similar observations were made with purified cell wall samples that contain WTAs still attached to the peptidoglycan matrix ( S3A Fig in S1 Text ) . The absence of muramic acid , one of the peptidoglycan building blocks , from WTA extracts ( Fig 2B ) indicates that l-rhamnose is specifically associated with WTAs and is not a putative peptidoglycan contaminant . This is corroborated by the absence of l-rhamnose in purified peptidoglycan samples ( Fig 2C ) . WTAs have been identified as important regulators of peptidoglycan cross-linking and maturation [37] . To investigate if l-rhamnose decoration of WTAs has any involvement in the maturation of the Lm peptidoglycan , we performed HPLC analysis of the muropeptide composition of mutanolysin-digested peptidoglycan samples from wild type , ΔrmlACBD and ΔrmlACBD+rmlACBD bacteria . No differences in the nature and relative amount of muropeptide species were observed between strains ( S3B Fig in S1 Text ) , ruling out a role for WTA l-rhamnosylation in the consolidation of the peptidoglycan architecture . Overall , these results confirm that a functional rmlACBD locus is required for the association of l-rhamnose with Lm WTAs , likely by providing the molecular machinery responsible for the synthesis of l-rhamnose . The rml operon in Lm includes a fifth gene , lmo1080 , located upstream of rmlA ( Fig 1 ) , which codes for a protein similar to the B . subtilis minor teichoic acid biosynthesis protein GgaB , shown to possess sugar transferase activity [38] . Conserved domain analysis of the translated Lmo1080 amino acid sequence revealed that its N-terminal region is highly similar ( e-value 10–22 ) to a GT-A family glycosyltransferase domain ( S1B Fig in S1 Text ) . In GT-A enzymes , this domain forms a pocket that accommodates the nucleotide donor substrate for the glycosyl transfer reaction , and contains a signature DxD motif necessary to coordinate a catalytic divalent cation [39] . This motif is also found within the predicted glycosyltransferase domain sequence of Lmo1080 as a DHD tripeptide ( S1B Fig in S1 Text ) . For these reasons , we investigated whether Lmo1080 , which we renamed here RmlT ( for l-rhamnose transferase ) , was involved in the l-rhamnosylation of Lm WTAs . We constructed an Lm EGD-e mutant strain lacking rmlT ( S2A Fig in S1 Text ) and analyzed the structure and sugar composition of its WTAs as described above . WTAs isolated from ΔrmlT bacteria displayed a faster migration in gel ( Fig 2A ) and did not contain any trace of l-rhamnose ( Fig 2B ) , fully recapitulating the ΔrmlACBD phenotype . Reintroduction of a wild type copy of rmlT into the mutant strain ( ΔrmlT+rmlT ) resulted in a phenotype that resembles that of the wild type strain , with regards to WTA gel migration profile ( Fig 2A ) and presence of l-rhamnose in the WTA fraction ( Fig 2B ) . To discard the possibility that the deletion of rmlT exerted a negative polar effect on the downstream expression of rmlACBD , potentially disrupting the synthesis of l-rhamnose used for WTA glycosylation , we compared the transcription of the rmlACBD genes in the wild type and ΔrmlT Lm strains by quantitative real-time PCR . Transcript levels were unchanged in the ΔrmlT background as compared to the wild type strain ( S2B Fig in S1 Text ) , indicating that the deletion of rmlT did not interfere with the transcription of rmlACBD . To definitely confirm that Lm ΔrmlT still holds the capacity to synthesize l-rhamnose , being only incapable to incorporate it in nascent WTA polymers , we evaluated the presence of l-rhamnose in the cytoplasmic compartment of this strain . The intracellular content of early exponential-phase bacteria from the wild type , ΔrmlACBD and ΔrmlT strains was extracted , hydrolyzed and analyzed by HPAEC-PAD to compare the sugar composition of cytoplasmic extracts . As shown in Fig 2D , a peak corresponding to l-rhamnose was detected in the cytoplasmic samples from the wild type and ΔrmlT strains , but not from the ΔrmlACBD strain , clearly demonstrating that , as opposed to ΔrmlACBD bacteria , ΔrmlT bacteria retain a functional l-rhamnose biosynthesis pathway . These results indicate that the depletion of l-rhamnose observed in ΔrmlT WTAs is a consequence of the absence of the WTA l-rhamnosyltransferase activity performed by RmlT . Therefore , we propose RmlT as the glycosyltransferase in charge of decorating Lm WTAs with l-rhamnose . WTAs were previously associated with bacterial resistance against salt stress [40] and host defense effectors , such as lysozyme [37 , 41] . We thus investigated the potential involvement of WTA l-rhamnosylation in these processes by assessing the growth of the ΔrmlACBD and ΔrmlT strains in the presence of high concentrations of either NaCl or lysozyme . As shown in Fig 3A , no significant difference was observed between the growth of the wild type and the two mutant strains in BHI broth containing 5% NaCl . Similarly , no difference was detected between the growth behavior of these strains after the addition of different concentrations of lysozyme ( 50 μg/ml and 1 mg/ml ) to bacterial cultures in the exponential phase ( Fig 3B ) . As expected , we observed an immediate and significant decrease in the survival of the lysozyme-hypersensitive ΔpgdA mutant [42] ( Fig 3B ) . These data demonstrate that Lm does not require l-rhamnosylated WTAs to grow under conditions of high osmolarity nor to resist the cell wall-degrading activity of lysozyme . WTAs were also found to be involved in bacterial resistance to host-secreted defense peptides [14 , 43] . To investigate the role of WTA l-rhamnosylation in Lm resistance to AMPs , we evaluated the in vitro survival of wild type , ΔrmlACBD and ΔrmlT Lm , as well as of the respective complemented strains , in the presence of biologically active synthetic forms of AMPs produced by distinct organisms: gallidermin , a bacteriocin from the Gram-positive bacterium Staphylococcus gallinarum [44]; CRAMP , a mouse cathelicidin [45] , or its human homolog LL-37 [46] . After two hours of co-incubation with different AMP concentrations , surviving bacteria were enumerated by plating in solid media . The overall survival levels of Lm varied with each AMP , evidencing their distinct antimicrobial effectiveness ( S4 Fig in S1 Text ) . However , when compared to the wild type strain , the ΔrmlACBD and ΔrmlT mutants displayed a consistent decrease in their survival levels in the presence of any of the three AMPs ( Fig 3C ) , in a dose-dependent manner ( S4 Fig in S1 Text ) . Restoring WTA l-rhamnosylation through genetic complementation of the mutant strains resulted in an increase of the survival rate to wild type levels . This result demonstrated the important contribution of l-rhamnosylated WTAs towards Lm resistance against AMPs , pointing to a role for WTA glycosylation in bacterial immune evasion mechanisms . The increased AMP susceptibility of Lm strains defective in WTA l-rhamnosylation suggests that this process is required to hinder the bactericidal activity of AMPs . Since AMPs generally induce bacterial death by disrupting the integrity of the plasma membrane , we hypothesized that the higher susceptibility of the ΔrmlACBD and ΔrmlT mutant strains resulted from an increased AMP-mediated destabilization of the Lm membrane . In this context , two scenarios were envisioned: i ) AMPs could be binding with higher affinity to the l-rhamnose-deficient Lm cell wall , or ii ) they could be crossing it at a faster pace , thus reaching the membrane more quickly than in wild type Lm . To explore these possibilities , we first investigated the binding affinity of the mouse cathelicidin CRAMP towards Lm cell walls depleted of l-rhamnose . For this , we incubated the different Lm strains with CRAMP for a short period and analyzed by flow cytometry the amount of Lm-bound peptide exposed at the cell surface and accessible for antibody recognition . We detected fluorescence associated with surface-exposed CRAMP in all strains ( Fig 4A ) . However , the mean fluorescence intensity ( MFI ) values were significantly reduced in both ΔrmlACBD and ΔrmlT mutants , in comparison to wild type Lm and the complemented strains ( Fig 4A and 4B ) . This suggests that CRAMP was less accessible to immunolabeling at the cell surface of Lm lacking l-rhamnosylated WTAs . The affinity of AMPs towards the bacterial surface is driven by electrostatic forces between positively charged peptides and the anionic cell envelope [23] . To determine if variations of the Lm surface charge contributed to the reduced amount of CRAMP exposed at the surface of ΔrmlACBD and ΔrmlT bacteria , we compared the surface charge of Lm with or without l-rhamnosylated WTAs . For this , we analyzed the binding of cytochrome c , a small protein with positive charge at physiological conditions ( isoelectric point ~10 ) , to the wild type and mutant Lm strains . As positive control , we used a mutant strain that cannot modify its LTAs with d-alanine ( ΔdltA ) and , as a result , displays a higher surface electronegativity and a concomitant higher affinity for positively charged compounds [14 , 47] . As expected , the level of cytochrome c binding was higher with the ΔdltA strain than with the respective wild type strain , as illustrated by a decreased percentage of unbound cytochrome c ( Fig 4C ) . However , no significant difference in cytochrome c binding levels was observed between ΔrmlACBD , ΔrmlT and wild type EGD-e strains ( Fig 4C ) , indicating that the absence of l-rhamnose in WTAs does not affect the Lm surface charge . This was further corroborated by zeta potential measurements showing similar pH-dependent variations for both wild type and mutant strains ( S5 Fig in S1 Text ) . Overall , these results allowed us to discard electrostatic changes as a reason behind the difference in the levels of CRAMP detected at the Lm cell surface . To further explore the decreased levels of surface-exposed CRAMP in Lm strains lacking l-rhamnosylated WTAs , we compared total levels of bacterium-associated CRAMP in the different strains by flow cytometry , following a short incubation with a fluorescently labeled form of this AMP . The intensity of Lm-associated CRAMP fluorescence was comparable for the wild type EGD-e , ΔrmlACBD and ΔrmlT strains ( Fig 4D and 4E ) , indicating that the overall peptide levels associated to Lm cells were similar between the different strains . Accordingly , the residual fluorescence in the supernatants obtained by centrifugation of the bacteria-peptide suspensions was also similar ( Fig 4F ) . As positive control we used the ΔdltA strain , which displayed a significantly stronger peptide binding than its parental wild type strain ( Fig 4D–4F ) . These data strongly suggest that the increased CRAMP susceptibility of Lm strains lacking l-rhamnosylated WTAs results from an improved penetration of CRAMP through their cell walls . Altogether , these results showed that l-rhamnosylated WTAs do not interfere with the Lm surface charge or with the binding efficiency of AMPs , but likely promote Lm survival by hindering the crossing of its cell wall by these bactericidal molecules . In light of these results , we then examined whether WTA l-rhamnosylation interfered with the dynamics of AMP interaction with the Lm plasma membrane . We performed a time-course study to follow Lm membrane potential changes induced by CRAMP . In live bacteria , the membrane potential is an electric potential generated across the plasma membrane by the concentration gradients of sodium , potassium and chloride ions . Physical or chemical disruption of the plasma membrane integrity leads to the suppression of this potential ( depolarization ) [48] . Lm strains were incubated with DiOC2 ( 3 ) , a green fluorescent voltage-sensitive dye that readily enters into bacterial cells . As the intracellular dye concentration increases with higher membrane potential , it favors the formation of dye aggregates that shift the fluorescence emission to red . After stabilization of the DiOC2 ( 3 ) fluorescence , CRAMP was added to bacterial samples and the rate of Lm depolarization was immediately analyzed by measuring the red fluorescence emission decline in a flow cytometer . The decrease in the membrane potential was consistently greater in the ΔrmlACBD and ΔrmlT strains as compared to wild type Lm , particularly in the first 10–15 min ( Fig 5A ) , indicating that the Lm plasma membrane integrity is compromised faster by the action of CRAMP in the absence of l-rhamnosylated WTAs . To investigate if increased CRAMP-mediated disruption of the Lm membrane integrity was associated with increased permeabilization , we monitored in real time the entry of the fluorescent probe SYTOX Green into the different Lm strains , following the addition of CRAMP . This probe only enters into bacterial cells with a compromised membrane and displays a strong green fluorescence emission after binding to nucleic acids . As expected , when CRAMP was omitted from the bacterial suspensions , any increase in SYTOX Green-associated fluorescence was detected ( Fig 5B ) . However , in the presence of the peptide , the green fluorescence intensity of samples containing the ΔrmlACBD or ΔrmlT mutants increased earlier than in samples containing wild type Lm ( Fig 5B ) , eventually reaching similar steady-state levels at later time points ( S7 Fig in S1 Text ) . These observations indicate that the CRAMP-mediated permeability increase of the Lm membrane to SYTOX Green occurs faster in strains lacking l-rhamnosylated WTAs . To investigate the ultrastructural localization of the peptide , we performed immunoelectron microscopy on CRAMP-treated wild type and ΔrmlACBD Lm strains . Interestingly , CRAMP-specific labeling was not only detected in the Lm cell envelope , as expected , but also in the cytoplasm ( Fig 5C ) , suggesting that this AMP may additionally target components or processes inside Lm . Comparison of the subcellular distribution of CRAMP between these two bacterial compartments revealed a preferential cell envelope localization in wild type Lm , which contrasted with the slight but significantly higher cytoplasmic localization of the peptide in the ΔrmlACBD strain ( Fig 5D ) . These observations are in agreement with a model in which CRAMP crosses the Lm cell wall more efficiently in the absence of WTA l-rhamnosylation , therefore reaching the bacterial membrane and the cytoplasm comparatively faster . Finally , to confirm that the presence of l-rhamnosylated WTAs hinders the capacity of AMPs to flow through the Lm cell wall , we assessed levels of CRAMP retained in purified cell wall samples from the wild type , ΔrmlACBD and ΔrmlT strains by Western blot . After incubation with CRAMP , peptides trapped within the peptidoglycan matrix were released by mutanolysin treatment of the cell wall and quantitatively resolved by SDS-PAGE . Immunoblotting revealed a small but consistent decrease in the amount of peptide associated with the cell wall from the two mutant strains in comparison with wild type Lm ( Fig 5E and 5F ) . This result indicates that the lack of l-rhamnose in WTAs results in a partial loss of the AMP retention capacity of the Lm cell wall , which induces an enhanced AMP targeting of the Lm plasma membrane and consequent bacterial killing . All combined , these data support a model where the l-rhamnosylation of WTAs alters the Lm cell wall permeability to favor the entrapment of AMPs . This obstructive effect hinders AMP progression through the cell wall and delays their lethal interaction with the plasma membrane . To evaluate the importance of WTA l-rhamnosylation in Lm pathogenicity , we assessed the in vivo virulence of Lm strains lacking l-rhamnosylated WTAs . BALB/c mice were inoculated orally with wild type , ΔrmlACBD or ΔrmlT strains , and the bacterial load in the spleen and liver of each animal was quantified three days later . The proliferative capacity of both ΔrmlACBD and ΔrmlT mutant strains was similarly reduced in both organs , although more significantly in the liver ( Fig 6A and 6B ) . To determine if the decreased virulence of the mutant strains was due to a specific defect in the crossing of the intestinal epithelium , BALB/c mice were challenged intravenously , bypassing the intestinal barrier . Three days post-infection , the differences between mutant and wild type strains , in both organs , were similar to those observed in orally infected animals ( Fig 6C and 6D ) , thus discarding any sieving effect of the intestinal epithelium on the decreased splenic and hepatic colonization by both ΔrmlACBD and ΔrmlT . Importantly , organs of mice infected intravenously with the complemented strains ( ΔrmlACBD+rmlACBD and ΔrmlT+rmlT ) displayed bacterial loads comparable to wild type Lm-infected organs ( Fig 6C and 6D ) . The attenuated in vivo phenotype of the ΔrmlACBD and ΔrmlT strains was not caused by an intrinsic growth defect , as demonstrated by their wild type-like growth profiles in broth or inside eukaryotic cells ( S8 Fig in S1 Text ) . These results confirmed the involvement of the rml operon in virulence , revealing a significant contribution of WTA l-rhamnosylation to Lm pathogenesis . Importantly , the in vivo attenuation of the ΔrmlT strain , which is unable to append l-rhamnose to its WTAs but is able to synthesize the l-rhamnose precursor , showed that although l-rhamnose biosynthesis is required to achieve optimal levels of virulence it is its covalent linkage to the WTA backbone that is crucial for the successful Lm host infection . To evaluate the protective role of WTA l-rhamnosylation against AMPs in vivo , we performed virulence studies in a CRAMP-deficient mouse model . To determine the influence of WTA l-rhamnosylation in Lm intestinal persistence , we performed oral infections of adult CRAMP knockout 129/SvJ mice ( cramp-/- , KO ) [49] and of age- and background-matched wild type mice ( cramp+/+ , WT ) , with the wild type or ΔrmlACBD Lm strains and monitored the respective fecal carriage . In both WT and KO mice , we observed comparable dynamics of fecal shedding of the wild type and ΔrmlACBD strains ( Fig 6E and 6F ) . In agreement with the comparable virulence defects observed for WTA l-rhamnosylation-deficient bacteria , following oral or intravenous inoculation of BALB/c mice ( Fig 6A–6D ) , these results suggest a minor role for CRAMP in the control of Lm during the intestinal phase of the infection . We then inoculated WT and KO mice intravenously and quantified bacterial numbers in the spleen and liver , three days post-infection . In line with what was observed in BALB/c mice ( Fig 6C ) , the ΔrmlACBD strain showed significant virulence attenuation in both organs of WT mice ( Fig 6G ) . Interestingly , this virulence defect was nearly abolished in KO animals , with the ΔrmlACBD strain displaying an organ-colonizing capacity similar to wild type bacteria ( Fig 6H ) . In addition , bacterial loads were higher in the organs of KO mice than in those of WT animals ( Fig 6G and 6H ) . These data indicate that , in comparison to their WT congeners , KO mice are more susceptible to Lm infection , and confirm the in vivo listericidal activity of CRAMP . Altogether , these results highlight a key role for host-produced CRAMP in restraining Lm infection and demonstrate that WTA l-rhamnosylation also promotes resistance to AMPs in an in vivo context . Teichoic acids are key players in the maintenance of the Gram-positive cell envelope integrity and functionality . They are typically decorated with d-alanine and/or a variety of glycosyl groups , which influence the overall properties of these polymers [9] . Whereas d-alanylation of WTAs has been demonstrated to contribute towards bacterial defense against AMPs [14 , 23] , the involvement of glycosylation in this process has never been investigated . In this study , we show for the first time that the glycosylation of Lm WTAs with l-rhamnose is mediated by the WTA l-rhamnosyltransferase RmlT and confers protection against AMPs in vitro and during mouse infection . Based on our data , we propose that this protection results from a delayed traversal of the Lm cell envelope by AMPs in the presence of l-rhamnose-decorated WTAs . Most importantly , we reveal a key role for l-rhamnosylated WTAs in the processes underlying Lm pathogenesis . Unlike S . aureus or B . subtilis [22] , WTAs in Listeria are not decorated with d-alanine , undergoing only glycosylation with a small pool of monosaccharides [6 , 10] . Among these is l-rhamnose , which is the product of a remarkably conserved biosynthetic pathway that is encoded by the rmlABCD genes [35] . Interestingly , a significant number of bacteria harboring these genes are commonly pathogenic [27–32] and have l-rhamnose in close association with surface components [50 , 51] . In Listeria , the rmlACBD locus is only found in certain serotypes of Lm ( 1/2a , 1/2b , 1/2c , 3c and 7 ) and L . seeligeri ( 1/2b ) . These serotypes were all shown to have l-rhamnose in their WTAs , except for Lm serotypes 3c and 7 [6] , which appear to be unable to produce this sugar because of mutations within rmlA and rmlB , respectively ( Fig 1 ) . Our results confirmed that the appendage of l-rhamnose to Lm WTAs requires the products of the rmlACBD locus . Ultimately , WTA glycosylation is catalyzed by glycosyltransferases , a class of enzymes that recognize nucleotide-sugar substrates and transfer the glycosyl moiety to a WTA subunit [52] . In silico analysis of lmo1080 , the first gene of the operon including rmlACBD ( Fig 1 ) showed that it encodes a protein with putative glycosyltransferase activity . The genomic location and predicted protein function were strong indicators that this gene might encode the transferase involved in the l-rhamnosylation of Lm WTAs . Our data demonstrated that whereas lmo1080 , that we renamed rmlT , is dispensable for rhamnose biosynthesis , it is required for the addition of l-rhamnose to WTAs in Lm strains with a functional l-rhamnose pathway , thus validating RmlT as the l-rhamnose-specific WTA glycosyltransferase in Lm . WTAs are associated with the natural resistance of S . aureus to peptidoglycan-degrading enzymes , such as lysozyme [37 , 41] . In contrast , absence of WTA decoration , but not of the polymers , was shown to induce an increase of the staphylococcal susceptibility to lysostaphin [53] . Modifications of the Lm peptidoglycan , such as N-deacetylation [42] , were found to contribute to protection against lysozyme , but the role of WTAs and in particular their decoration , was never addressed . Our results discard WTA l-rhamnosylation as a component of the Lm resistance mechanism to this host immune defense protein , as well as its involvement in the promotion of growth under osmotic conditions . Other innate immune effectors , such as antimicrobial peptides ( AMPs ) , also target bacterial organisms [54] that in turn have developed resistance strategies to avoid injury and killing induced by AMPs . Among these strategies is the reshaping and fine-tuning of cell envelope components to lower AMP affinity to the bacterial surface [21] . Previous studies showed a clear link between the d-alanylation of WTAs and AMP resistance [14 , 43] . In this context , we found here a similar role for WTA l-rhamnosylation , showing that , in the absence of l-rhamnosylated WTAs , bacteria exhibit an increased susceptibility to AMPs produced by bacteria , mice and importantly by humans . Although from such distinct sources , AMPs used here share a cationic nature that supports their activity . However , while teichoic acid d-alanylation is known to reduce the cell wall electronegativity [14] , glycosyl substituents of Lm WTAs are neutrally charged and WTA glycosylation should thus promote AMP resistance through a different mechanism . It is well established that AMPs induce bacterial death mainly by tampering with the integrity of the plasma membrane . This can be achieved through multiple ways , all of which are driven by the intrinsic amphipathic properties of this class of peptides [55] . Nonetheless , the initial interaction of AMPs with bacterial surfaces is mediated by electrostatic forces between their positive net charge and the anionic cell envelope [23] . Our data show that , unlike d-alanylation [56] , WTA l-rhamnosylation does not interfere with the Lm cell surface charge , in agreement with l-rhamnose being an electrostatically neutral monosaccharide . Importantly , the reduced levels of surface-exposed CRAMP in Lm strains lacking l-rhamnosylated WTAs suggested instead that their increased susceptibility to this peptide was correlated with its improved penetration of the l-rhamnose-depleted Lm cell wall . We confirmed this premise with data showing that CRAMP-mediated cell depolarization and plasma membrane permeabilization events occur earlier in WTA l-rhamnosylation-deficient Lm strains . In addition , we also observed a predominant cytoplasmic presence of CRAMP in these mutant strains , in contrast to the preferential cell envelope localization in wild type Lm , further suggesting a WTA l-rhamnosylation-dependent kinetic discrepancy in the progression of CRAMP through the Lm cell envelope . Saar-Dover et al . demonstrated in the WTA-lacking Streptococcus agalactiae ( GBS ) that LTA d-alanylation promoted resistance to the human cathelicidin LL-37 by hindering cell wall crossing and plasma membrane disturbance [57] . They proposed that the underlying mechanism does not rely on modulation of the surface charge but on LTA conformation-associated alterations of the cell wall packing density [57] . Our data are in line with these observations and although we did not detect changes in the cell wall cross-linking status , we cannot ignore a possible impact of l-rhamnosylation on WTA polymer conformation accounting for changes in cell wall permeability . If one considers that the peptidoglycan , a multi-layered and compact structure , is densely populated with WTA polymers decorated with multiple units of the rather bulky l-rhamnose molecule , spatial constraints and increased cell wall density need to be accounted . In fact , we showed that purified Lm cell wall depleted of l-rhamnose does not retain CRAMP in its peptidoglycan matrix as effectively as cell wall containing l-rhamnosylated WTAs . In addition , we have indications that soluble l-rhamnose interferes with CRAMP activity , improving the survival of WTA l-rhamnosylation mutants of Lm . These observations suggest a potential interaction between l-rhamnose and AMPs , which could favor the “retardation effect” that ultimately promotes Lm survival . We previously reported a significantly increased transcription of rmlACBD during mouse spleen infection [24] , which suggested that WTA l-rhamnosylation is highly activated by Lm to successfully infect this host organ . Our infection studies in mice confirmed the importance of this mechanism for Lm pathogenesis by revealing a significant virulence attenuation of WTA l-rhamnosylation-deficient Lm strains . Surprisingly , the expression of rmlT appeared unchanged during mouse spleen infection as compared to growth in BHI [24] , suggesting that an increased L-rhamnose biosynthesis could be sufficient to induce an increased WTA l-rhamnosylation and AMP resistance . Faith et al . also observed a decreased bacterial burden of a serotype 4b Lm strain lacking the gtcA gene [58] , a mutation that resulted in complete loss of galactose decoration of its WTAs [59] . Interestingly , gtcA is also present in Lm EGD-e , where it appears to be involved in WTA substitution with N-acetylglucosamine [60] , and was shown to contribute to the colonization of the mouse spleen , liver and brain [61] . However the mechanism through which this occurs remains unclear . Virulence studies in mice lacking the CRAMP gene corroborated our in vitro susceptibility data and revealed the importance of WTA l-rhamnosylation-promoted resistance to AMPs for Listeria virulence . In vivo data also provided a strong insight into the protective role of CRAMP against systemic infection by Lm , as had been previously observed with other bacterial pathogens [49 , 62 , 63] . Our results on fecal shedding dynamics suggest that the contribution of CRAMP to the control of Lm during the intestinal phase of infection is minimal . A previous report showed a negligible enteric secretion of CRAMP in normal adult mice [64] , which may explain the similar shedding behavior of the wild type and ΔrmlACBD strains that were observed in both mouse strains . In this scenario , infection studies in newborn animals , whose enterocytes actively express CRAMP [45 , 64] , may provide conclusive information regarding the role of WTA l-rhamnosylation in the Lm resistance to CRAMP during the intestinal phase of the infection . Notwithstanding , CRAMP is actively produced by phagocytes in adult mice [65] . As a major target for Lm colonization , the spleen is also an important reservoir of phagocytic cells . We can speculate that WTA l-rhamnosylation is particularly important to increase the chances of Lm surviving CRAMP-mediated killing during spleen infection . Considering our data on the Lm susceptibility to LL-37 , the human homolog of CRAMP , we can also envisage this scenario in the context of human infection . In conclusion , our work has unveiled for the first time a role for WTA glycosylation in bacterial resistance to AMPs . We propose that WTA l-rhamnosylation reduces the cell wall permeability to AMPs , promoting a delay in the crossing of this barrier and in the disruption of the plasma membrane , thus favoring Lm survival and virulence in vivo . Our findings reveal a novel facet in the contribution of WTA modifications towards AMP resistance , reinforcing the crucial role of these Gram-positive surface glycopolymers in host defense evasion . Bacterial strains used in this study are listed in Table 1 . Lm and E . coli strains were routinely cultured aerobically at 37°C in brain heart infusion ( BHI , Difco ) and Lysogeny Broth ( LB ) media , respectively , with shaking . For experiments involving the Lm ΔtagO1ΔtagO2 strain , bacteria were first cultured overnight at 30°C with shaking in the presence of 1 mM IPTG ( isopropyl-β-d-thiogalactopyranoside ) , washed and diluted ( 1:100 ) in fresh BHI and cultured overnight at 30°C with shaking [36] . When appropriate , the following antibiotics were included in culture media as selective agents: ampicilin ( Amp ) , 100 μg/ml; chloramphenicol ( Cm ) , 7 μg/ml ( Lm ) or 20 μg/ml ( E . coli ) ; erythromycin ( Ery ) , 5 μg/ml . For genetic complementation purposes , colistin sulfate ( Col ) and nalidixic acid ( Nax ) were used at 10 and 50 μg/ml , respectively . Lm mutant strains were constructed in the EGD-e background through a process of double homologous recombination mediated by the suicide plasmid pMAD [66] . DNA fragments corresponding to the 5’- and 3’-flanking regions of the rmlACBD locus ( lmo1081—4 ) were amplified by PCR from Lm EGD-e chromosomal DNA with primers 1–2 and 3–4 ( S2 Table in S1 Text ) , and cloned between the SalI—MluI and MluI—BglII sites of pMAD , yielding pDC303 . Similarly , DNA fragments corresponding to the 5’- and 3’-flanking regions of rmlT ( lmo1080 ) were amplified with primers 15–16 and 17–18 ( S2 Table in S1 Text ) , and cloned between the SalI—EcoRI and EcoRI—BglII sites of pMAD , yielding pDC491 . The plasmid constructs were introduced in Lm EGD-e by electroporation and transformants selected at 30°C in BHI—Ery . Positive clones were re-isolated in the same medium and grown overnight at 43°C . Integrant clones were inoculated in BHI broth and grown overnight at 30°C , after which the cultures were serially diluted , plated in BHI agar and incubated overnight at 37°C . Individual colonies were tested for growth in BHI—Ery at 30°C and antibiotic-sensitive clones were screened by PCR for deletion of rmlACBD ( primers 5–6 , 7–8 , 9–10 and 11–12 ) and rmlT ( primers 19–20 ) ( S2 Table in S1 Text ) . Genetic complementation of the deletion mutant strains was performed as described [24] . DNA fragments containing either the rmlACBD or rmlT loci were amplified from Lm EGD-e chromosomal DNA with primers 13–14 and 21–22 ( S2 Table in S1 Text ) , respectively , and cloned between the SalI—PstI sites of the phage-derived integrative plasmid pPL2 [67] , generating pDC313 and pDC550 . The plasmid constructs were introduced in the E . coli strain S17-1 and transferred , respectively , to the ΔrmlACBD and ΔrmlT strains by conjugation on BHI agar . Transconjugant clones were selected in BHI—Cm/Col/Nax and chromosomal integration of the plasmids confirmed by PCR with primers 23 and 24 ( S2 Table in S1 Text ) . All plasmid constructs and gene deletions were confirmed by DNA sequencing . Total bacterial RNA was isolated from 10 ml of exponential cultures ( OD600 = 0 . 6 ) by the phenol-chloroform extraction method , as previously described [68] , and treated with DNase I ( Turbo DNA-free , Ambion ) , as recommended by the manufacturer . Purified RNAs ( 1 μg ) were reverse-transcribed with random hexamers , using iScript cDNA Synthesis kit ( Bio-Rad Laboratories ) . Quantitative real-time PCR ( qPCR ) was performed in 20-μl reactions containing 2 μl of cDNA , 10 μl of SYBR Green Supermix ( Bio-Rad Laboratories ) and 0 . 25 μM of forward and reverse primers ( S2 Table in S1 Text ) , using the following cycling protocol: 1cycle at 95°C ( 3 min ) and 40 cycles at 95°C ( 30 s ) , 55°C ( 30 s ) and 72°C ( 30 s ) . Each target gene was analyzed in triplicate and blank ( water ) and DNA contamination controls ( unconverted DNase I-treated RNA ) were included for each primer pair . Amplification data were analyzed by the comparative threshold ( ΔΔCt ) method , after normalization of the test and control sample expression values to a housekeeping gene ( 16S rRNA ) . For qualitative analysis , PCR was performed in 20-μl reactions containing 2 μl of cDNA , 10 μl of MangoMix 2× reaction mix ( Bioline ) and 0 . 5 μM of forward and reverse qPCR primers , using the following protocol: 1 cycle at 95°C ( 5 min ) , 25 cycles at 95°C ( 30 s ) , 55°C ( 30 s ) and 72°C ( 20 s ) , and 1 cycle at 72°C ( 5 min ) . Amplification products were resolved in 1% ( w/v ) agarose gel and analyzed in a GelDoc XR+ System ( Bio-Rad Laboratories ) . Extraction and analysis of Lm WTAs by polyacrylamide gel electrophoresis was performed essentially as described [69] , with the exception that WTAs extracts were obtained from exponential-phase cultures . Sedimented bacteria were washed ( buffer 1: 50 mM MES buffer , pH 6 . 5 ) and boiled for 1 h ( buffer 2: 4% SDS in buffer 1 ) . After centrifugation , the pellet was serially washed with buffer 2 , buffer 3 ( 2% NaCl in buffer 1 ) and buffer 1 , before treatment with 20 μg/ml proteinase K ( 20 mM Tris-HCl , pH 8; 0 . 5% SDS ) at 50°C for 4 h . The digested samples were thoroughly washed with buffer 3 and distilled water and incubated overnight ( 16 h ) with 0 . 1 M NaOH , under vigorous agitation . Cell wall debris were removed by centrifugation ( 10 , 000 rpm , 10 min ) and the hydrolyzed WTAs present in the supernatant were directly analyzed by native PAGE in a Tris-tricine buffer system . WTA extracts were resolved through a vertical ( 20 cm ) polyacrylamide ( 20% ) gel at 20 mA for 18 h ( 4°C ) . To visualize WTAs , the gel was stained in 0 . 1% Alcian blue ( 40% ethanol; 5% acetic acid ) for 30 min and washed ( 40% ethanol; 10% acetic acid ) until the background is fully cleared . Optionally , for increased contrasting , silver staining can be performed on top of the Alcian blue staining . Cell walls of Lm strains were purified as described before [70] , with modifications . Overnight cultures were subcultured into 1–2 liters of BHI broth ( initial OD600 = 0 . 005 ) and bacteria grown until exponential phase ( OD600 = 1 . 0–1 . 5 ) . Cultures were rapidly cooled in an ice/ethanol bath and bacteria harvested by centrifugation ( 7 , 500 rpm , 15 min , 4°C ) . The pellet was resuspended in cold ultrapure water and boiled for 30 min with 4% SDS to kill bacteria and inactivate cell wall-modifying enzymes . The samples were cleared of SDS by successive cycles of centrifugation ( 12 , 000 rpm , 10 min ) and washing with warm ultrapure water until no detergent was detected [71] . SDS-free samples were resuspended in 2 ml of ultrapure water and cell walls disrupted with glass beads in a homogenizer ( FastPrep , Thermo Savant ) . Fully broken cell walls were separated from glass beads by filtration ( glass filters , pore size: 16–40 μm ) and from unbroken cell walls and other debris by low-speed centrifugation ( 2 , 000 rpm , 15 min ) . Nucleic acids were degraded after incubation ( 2 h ) at 37°C with DNase ( 10 μg/ml ) and RNase ( 50 μg/ml ) in a buffer containing 50 mM Tris-HCl , pH 7 . 0 , and 20 mM MgSO4 . Proteins were then digested overnight at 37°C with trypsin ( 100 μg/ml ) in the presence of 10 mM CaCl2 . Nuclease and proteases were inactivated by boiling in 1% SDS , and samples were centrifuged ( 17 , 000 rpm , 15 min ) and washed twice with ultrapure water . Cell walls were resuspended and incubated ( 37°C , 15 min ) in 8 M LiCl and then in 100 mM EDTA , pH 7 . 0 , after which they were washed twice with water . After resuspension in acetone and sonication ( 15 min ) , cell walls were washed and resuspended in ultrapure water before undergoing lyophilization . To obtain purified peptidoglycan , cell walls ( 20 mg ) were incubated for 48 h with 4 ml of 46% hydrofluoric acid ( HF ) , under agitation at 4°C . Samples were washed with 100 mM Tris-HCl , pH 7 . 0 , and centrifuged ( 17 , 000 rpm , 30 min , 4°C ) as many times as necessary to neutralize the pH . The pellet was finally washed twice with water prior to lyophilization . WTA extracts were obtained by incubating 1 mg of cell wall with 300 μl of 46% HF ( 18 h , 4°C ) . After centrifugation ( 13 , 200 rpm , 15 min , 4°C ) , the supernatant was recovered and evaporated under a stream of compressed air . The dried WTA residue was resuspended in water and lyophilized . The intracellular content of Lm strains was isolated according to a modified version of the protocol by Ornelas-Soares et al . [72] . Bacterial cultures ( 200 ml ) were grown until early exponential phase ( OD600 = 0 . 3 ) , and vancomycin was added at 7 . 5 μg/ml ( 5×MIC value [73] ) to induce the cytoplasmic accumulation of the peptidoglycan precursor UDP-MurNAc-pentapeptide . Cultures were grown for another 45 min and chilled in an ice-ethanol bath for 10 min . Bacteria were then harvested by centrifugation ( 12 , 000 rpm , 10 min , 4°C ) , washed with cold 0 . 9% NaCl , resuspended in 5 ml of cold 5% trichloroacetic acid and incubated for 30 min on ice . Cells and other debris were separated by centrifugation ( 4 , 000 rpm , 15 min , 4°C ) and the supernatant was extracted with 1–2 volumes of diethyl ether as many times as necessary to remove TCA ( sample pH should rise to at least 6 . 0 ) . The aqueous fraction containing the cytoplasmic material was lyophilized and the dried residue resuspended in ultrapure water . To analyze their sugar composition , purified cell wall and peptidoglycan ( 200 μg each ) , as well as cytoplasmic ( 500 μg ) and WTA extracts were hydrolyzed in 3 M HCl for 2 h at 95°C . After vacuum evaporation , the samples were washed with water and lyophilized . The hydrolyzed material was then resuspended in 150 μl of water and resolved by high-performance anion-exchange chromatography coupled with pulsed amperometric detection ( HPAEC-PAD ) . Ten microliters were injected into a CarboPac PA10 column ( Dionex , Thermo Fisher Scientific ) and eluted at 1 ml/min ( 30°C ) with 18 mM NaOH , followed by a gradient of NaCH3COO: 0–20 mM ( t = 25–30 min ) , 20–80 mM ( t = 30–35 min ) , 80–0 mM ( t = 40–45 min ) . Standards for glucosamine , muramic acid , l-rhamnose and ribitol ( Sigma-Aldrich ) were eluted under the same conditions to enable identification of chromatogram peaks . Data were acquired and analyzed with the Chromeleon software ( Dionex , Thermo Fisher Scientific ) . Muropeptide samples were prepared and analyzed as described [74] , with minor changes . Purified peptidoglycan was digested with 200 μg/ml mutanolysin ( Sigma-Aldrich ) in 12 . 5 mM sodium phosphate , pH 5 . 5 , for 16 h at 37°C . Enzymatic activity was halted by heating at 100°C for 5 min , after which the digested sample was reduced for 2 h with 2 . 5 mg/ml of sodium borohydride ( NaBH4 ) in 0 . 25 M borate buffer , pH 9 . 0 . The reaction was stopped by lowering the sample pH to 2 with ortho-phosphoric acid . After centrifugation , the supernatant was analyzed by reverse phase HPLC . Fifty microliters were injected into a Hypersil ODS ( C18 ) column ( Thermo Fisher Scientific ) and muropeptide species eluted ( 0 . 5 ml/min , 52°C ) in 0 . 1 M sodium phosphate , pH 2 . 0 , with a gradient of 5–30% methanol and detected at 206 nm . Mouse macrophage-like J774A . 1 cells ( ATCC , TIB-67 ) were propagated in Dulbecco’s modified Eagle’s medium ( DMEM ) containing 10% fetal bovine serum and infection assays were performed as described [24] . Briefly , cells ( ~2×105/well ) were infected for 45 min with exponential-phase bacteria at a multiplicity of infection of ~10 and treated afterwards with 20 μg/ml gentamicin for 75 min . At several time-points post-infection , cells were washed with PBS and lysed in cold 0 . 2% Triton X-100 for quantification of viable intracellular bacteria in BHI agar . One experiment was performed with triplicates for each strain and time-point . Lm cultures grown overnight were appropriately diluted in BHI broth and their growth under the presence of stressful stimuli was monitored by optical density measurement at 600 nm ( OD600 ) . For comparative analysis of Lm resistance to salt stress , bacterial cultures were diluted 100-fold in BHI alone ( control ) or BHI containing 5% NaCl . To assess the Lm resistance to lysozyme , exponential-phase cultures ( OD600 ≈ 1 . 0 ) were challenged with different doses of chicken egg white lysozyme ( Sigma ) . A mutant Lm strain hypersensitive to lysozyme ( ΔpgdA ) was used as a positive control for susceptibility . Bacteria in the exponential phase of growth ( OD600 = 0 . 7–0 . 8 ) were diluted ( 104 CFU/ml ) in sterile PB medium ( 10 mM phosphate buffer , pH 7 . 4; 1% BHI ) and mixed in a 96-well microplate with increasing concentrations of gallidermin ( Santa Cruz Biotechnology ) , CRAMP or LL-37 ( AnaSpec ) . Bacterial suspensions without AMPs were used as reference controls for optimal growth/survival . After incubation for 2 h at 37°C , the mixtures were serially diluted in sterile PBS and plated in BHI agar for quantification of viable bacteria . Each condition was analyzed in duplicate in three independent assays . Cytochrome c binding assays were performed as described [56] . Bacteria from mid-exponential-phase cultures ( OD600 = 0 . 6–0 . 7 ) were washed in 20 mM MOPS buffer , pH 7 . 0 , and resuspended in ½ volume of 0 . 5 mg/ml equine cytochrome c ( Sigma-Aldrich ) in 20 mM MOPS buffer , pH 7 . 0 . After 10 min of incubation , bacteria were pelleted and the supernatant collected for quantification of the absorbance at 530 nm . The mean absorbance values from replicate samples containing bacteria were subtracted to the mean value of a reference sample lacking bacteria , and the results were presented for each strain as percentage of unbound cytochrome c . Bacteria ( 1 ml ) from mid-exponential-phase cultures were washed twice with deionized water and diluted ( 107 CFU/ml ) in 15 mM NaCl solutions adjusted to different pH values ( 1 to 7 ) with nitric acid . Bacterial suspensions ( 750 μl ) were injected into a disposable capillary cell cuvette ( DTS1061 , Malvern Instruments ) and the zeta potential was measured at 37°C in a ZetaSizer Nano ZS ( Malvern Instruments ) , under an automated field voltage . Samples were measured in triplicate in three independent assays . Bacteria from 500 μl of mid-exponential-phase cultures were washed twice with PBS and treated for 5 min with 5 μg/ml CRAMP or PBS ( untreated control ) . After centrifugation , the supernatant was removed and PBS-washed bacteria were incubated for 1 h with rabbit anti-CRAMP ( 1:100 , Innovagen ) , followed by 1 h with Alexa Fluor 488-conjugated anti-rabbit IgG ( 1:200 , Molecular Probes ) . Finally , bacteria were fixed with 3% paraformaldehyde for 15 min , washed and resuspended in PBS . Alternatively , bacteria were similarly treated with an N-terminally 5-FAM-labeled synthetic form of CRAMP ( 95% purity , Innovagen ) , washed and resuspended in PBS . Samples were acquired in a FACSCalibur flow cytometer equipped with CellQuest software ( BD Biosciences ) and data were analyzed with FlowJo ( TreeStar Inc . ) . Green fluorescence was collected from at least 50 , 000 FSC/SSC-gated bacterial events in the FL1 channel ( 530 nm/20 nm bandpass filter ) . Fluorescence intensities were plotted in single-parameter histograms and results were presented as the average mean fluorescence intensity ( MFI ) value from three independent analyses . For bacterial membrane potential studies , the lipophilic fluorescent probe DiOC2 ( 3 ) ( 3 , 3-diethyloxacarbocyanine , Santa Cruz Biotechnology ) was used as a membrane potential indicator [48 , 75] . Mid-logarithmic phase bacteria were diluted ( 106 CFU/ml ) in PBS with 30 μM DiOC2 ( 3 ) and incubated for 15 min in the dark . CRAMP was added to a final concentration of 50 μg/ml and the sample was immediately injected in the flow cytometer . Control samples treated with PBS or with 1 . 5 mM sodium azide ( uncoupling agent ) were analyzed to determine the fluorescence values corresponding to basal ( 100% ) and null ( 0% ) membrane potential ( S6 Fig in S1 Text ) . Green and red ( FL3 , 670 nm/long bandpass filter ) fluorescence emissions were continuously collected from FSC/SSC-gated bacteria for 30 min . After acquisition , a ratio of red over green fluorescence ( R/G ) was calculated per event and plotted in the y-axis versus time . A series of consecutive one-minute-wide gates was applied to the plot and the mean R/G value per gate was determined . The mean R/G values from uncoupler-treated samples were deducted from the corresponding values from the untreated and CRAMP-treated samples , and the resulting values for each condition were normalized as percentage of the initial value ( t = 1 min ) . Finally , the temporal variation of the Lm membrane potential was represented graphically as the ratio of the normalized values from CRAMP-treated over untreated samples . Bacterial uptake of the cell-impermeable SYTOX Green dye was used to study membrane permeabilization induced by CRAMP [57] . Exponential-phase bacteria were washed and resuspended ( 107 CFU/ml ) in sterile PBS containing 1 μM SYTOX Green ( Molecular Probes ) . After 20 min of incubation in the dark , bacterial suspensions were mixed in PCR microplate wells with 50 μg/ml CRAMP or PBS ( negative control ) for a total volume of 100 μl . The mixtures were immediately placed at 37°C in a real-time PCR detection system ( iQ5 , Bio-Rad Laboratories ) and fluorescence emission at 530 nm was recorded every minute following excitation at 488 nm . One-hundred micrograms of purified cell wall were resuspended in 50 μl of 5 μg/ml CRAMP or PBS ( negative control ) and gently shaken for 5 min . Samples were centrifuged ( 16 , 000 × g , 1 min ) , washed in PBS and in TM buffer ( 10 mM Tris-HCl , 10 mM MgCl2 , pH 7 . 4 ) before overnight incubation at 37°C with mutanolysin ( 400 U/ml ) in TM buffer ( 50 μl ) . Supernatants were resolved by tricine-SDS-PAGE in a 16% gel , transferred onto nitrocellulose membrane and blotted with rabbit anti-CRAMP ( 1:1000 ) or mouse anti-InlA ( L7 . 7; 1:1000 ) , followed by HRP-conjugated goat anti-rabbit or anti-mouse IgG ( 1:2000 , P . A . R . I . S ) . Immunolabeled bands were visualized using SuperSignal West Dura Extended Duration Substrate ( Pierce ) and digitally acquired in a ChemiDoc XRS+ system ( Bio-Rad Laboratories ) . Exponential-phase bacteria treated with 50 μg/ml CRAMP for 15 min at 37°C were fixed for 1 h at room temperature ( 4% paraformaldehyde , 2 . 5% glutaraldehyde , 0 . 1 M sodium cacodylate , pH 7 . 2 ) , stained with 1% osmium tetroxide for 2 h and resuspended in 30% BSA ( high-purity grade ) . Bacterial pellets obtained after centrifugation in microhematocrit tubes were fixed overnight in 1% glutaraldehyde , dehydrated in increasing ethanol concentrations , and embedded in Epon 812 . Ultrathin sections ( 40–50 nm ) were placed on 400-mesh Formvar-coated copper grids and treated with 4% sodium metaperiodate and 1% periodic acid ( 10 min each ) for antigen retrieval . For immunogold labeling of CRAMP , sections were blocked for 10 min with 1% BSA and incubated overnight ( 4°C ) with rabbit anti-CRAMP ( 1:100 in 1% BSA ) . After extensive washing , sections were labeled with 10-nm gold complex-conjugated anti-rabbit IgG ( 1:200 in 1% BSA ) for 2 h , washed and contrasted with 4% uranyl acetate and 1% lead citrate . Images were acquired in a Jeol JEM-1400 transmission electron microscope equipped with a Gatan Orius SC1000 CCD camera and analyzed using ImageJ software . Virulence studies were done in mouse models of the following strains: wild type BALB/c and 129/SvJ ( Charles River Laboratories ) ; and CRAMP-deficient ( cramp-/- ) 129/SvJ , which was bred in our facilities from a breeding pair provided by Dr . Richard L . Gallo ( University of California , USA ) [49] . Infections were performed in six-to-eight week-old specific-pathogen-free females as described [76] . Briefly , for oral infections , 12-h starved animals were inoculated by gavage with 109 CFU in PBS containing 150 mg/ml CaCO3 , while intravenous infections were performed through the tail vein with 104 CFU in PBS . In both cases , the infection was carried out for 72 h , at which point the animals were euthanatized by general anesthesia . The spleen and liver were aseptically collected , homogenized in sterile PBS , and serial dilutions of the organ homogenates plated in BHI agar . For analysis of Lm fecal carriage , total feces produced by each infected animal ( n = 5 per strain ) up to a given time-point were collected , homogenized in PBS and serial dilutions plated in Listeria selective media ( Oxoid ) for bacterial enumeration . Mice were maintained at the IBMC animal facilities , in high efficiency particulate air ( HEPA ) filter-bearing cages under 12 h light cycles , and were given sterile chow and autoclaved water ad libitum . All the animal procedures were in agreement with the guidelines of the European Commission for the handling of laboratory animals ( directive 2010/63/EU ) , with the Portuguese legislation for the use of animals for scientific purposes ( Decreto-Lei 113/2013 ) , and were approved by the IBMC Animal Ethics Committee , as well as by the Direcção Geral de Veterinária , the Portuguese authority for animal protection , under license PTDC/SAU-MIC/111581/2009 . Statistical analyses were performed with Prism 6 ( GraphPad Software ) . Unpaired two-tailed Student’s t-test was used to compare the means of two groups; one-way ANOVA was used with Tukey’s post-hoc test for pairwise comparison of means from more than two groups , or with Dunnett’s post-hoc test for comparison of means relative to the mean of a control group . Mean differences were considered statistically non-significant ( ns ) when p value was above 0 . 05 . For statistically significant differences: * , p≤0 . 05; ** , p≤0 . 01; *** , p≤0 . 001 .
Listeria monocytogenes is a foodborne bacterial pathogen that preferentially infects immunocompromised hosts , eliciting a severe and often lethal disease . In humans , clinical manifestations range from asymptomatic intestinal carriage and gastroenteritis to harsher systemic states of the disease such as sepsis , meningitis or encephalitis , and fetal infections . The surface of L . monocytogenes is decorated with wall teichoic acids ( WTAs ) , a class of carbohydrate-based polymers that contributes to cell surface-related events with implications in physiological processes , such as bacterial division or resistance to antimicrobial peptides ( AMPs ) . The addition of other molecules to the backbone of WTAs modulates their chemical properties and consequently their functionality . In this context , we studied the role of WTA tailoring mechanisms in L . monocytogenes , whose WTAs are strictly decorated with monosaccharides . For the first time , we link WTA glycosylation with AMP resistance by showing that the decoration of L . monocytogenes WTAs with l-rhamnose confers resistance to host defense peptides . We suggest that this resistance is based on changes in the permeability of the cell wall that delay its crossing by AMPs and therefore promote the protection of the bacterial membrane integrity . Importantly , we also demonstrate the significance of this WTA modification in L . monocytogenes virulence .
You are an expert at summarizing long articles. Proceed to summarize the following text: The African trypanosome Trypanosoma brucei , which persists within the bloodstream of the mammalian host , has evolved potent mechanisms for immune evasion . Specifically , antigenic variation of the variant-specific surface glycoprotein ( VSG ) and a highly active endocytosis and recycling of the surface coat efficiently delay killing mediated by anti-VSG antibodies . Consequently , conventional VSG-specific intact immunoglobulins are non-trypanocidal in the absence of complement . In sharp contrast , monovalent antigen-binding fragments , including 15 kDa nanobodies ( Nb ) derived from camelid heavy-chain antibodies ( HCAbs ) recognizing variant-specific VSG epitopes , efficiently lyse trypanosomes both in vitro and in vivo . This Nb-mediated lysis is preceded by very rapid immobilisation of the parasites , massive enlargement of the flagellar pocket and major blockade of endocytosis . This is accompanied by severe metabolic perturbations reflected by reduced intracellular ATP-levels and loss of mitochondrial membrane potential , culminating in cell death . Modification of anti-VSG Nbs through site-directed mutagenesis and by reconstitution into HCAbs , combined with unveiling of trypanolytic activity from intact immunoglobulins by papain proteolysis , demonstrates that the trypanolytic activity of Nbs and Fabs requires low molecular weight , monovalency and high affinity . We propose that the generation of low molecular weight VSG-specific trypanolytic nanobodies that impede endocytosis offers a new opportunity for developing novel trypanosomiasis therapeutics . In addition , these data suggest that the antigen-binding domain of an anti-microbial antibody harbours biological functionality that is latent in the intact immunoglobulin and is revealed only upon release of the antigen-binding fragment . Trypanosomatid protozoan parasites cause many important diseases , including African sleeping sickness in humans and Nagana in domestic livestock in sub-Saharan Africa [1] , [2] . These organisms , like many other successful pathogens , have evolved sophisticated mechanisms for immune evasion [3] . A prominent strategy among African trypanosomes , facilitating chronic persistence in the host bloodstream and lymphatic system , relies on antigenic variation [4] . The major trypanosome surface antigen is the immunogenic variant-specific surface glycoprotein ( VSG ) present at ∼107 copies per cell and representing ∼90% of the total cell surface proteins [5] . This dense VSG coat is envisaged as functioning as a physical barrier , impeding antibody recognition of invariant surface epitopes . By repeatedly switching the VSG coat , antibodies that would recognise trypanosomes , leading to their elimination , are evaded [4] , [6] . Further , trypanosomes can reverse antibody-mediated agglutination in a protein synthesis-dependent manner [7] , and also defend themselves by efficient internalisation of antibody-VSG complexes [8] , [9] , [10] , [11] , delaying elimination by antibody-dependent complement lysis [12] , [13] . Furthermore , several groups have reported that antibody-induced VSG shedding may contribute to protection against antibody-mediated removal [7] , [14] , [15] . Trypanosoma brucei , in common with other trypanosomatids , restricts membrane exchange between the surface and endomembrane compartments to an invagination of the plasma membrane , the flagellar pocket ( FP ) , which is contiguous with the pellicular and flagellar membranes [13] , [16] . The FP comprises ∼5% of the total cellular surface and lacks the subpellicular microtubules [17] , [18] . The lumen of the FP contains an electron dense carbohydrate rich matrix and is bounded by a hemidesmosome-like zone around the neck of the pocket . Solution macromolecules such as antibodies have to transit the hemidesmosomal zone and the matrix to enter the FP . The VSG density is similar at the luminal face of the FP membrane and the bulk plasma membrane , but many other proteins such as macromolecular receptors are enriched within the FP membrane and virtually absent from the cell surface ( Field et al . [13] ) . In the bloodstream stage of the trypanosome , the cell surface turnover is exceptionally high [12]; exceeding rates reported for macrophages and fibroblasts [19] and is sufficient to cycle the entire surface in under 15 minutes [20] , [21] . While the biological significance of this high and developmental-stage specific activity is unclear , it likely contributes to a mechanism for recovering VSG and/or eliminating anti-VSG immunoglobulins bound to the surface of living parasites [12] . The difference in recycling efficiency of VSG and other surface proteins is due in part to differential trafficking through the endocytic pathway . For instance , transferrin is liberated from the parasite transferrin receptor in an acidic compartment , possibly the late endosome or the lysosome , whereas VSG is recycled via early and recycling endosomes [8] , [20] . Following clathrin-dependent endocytosis at the FP , VSG is separated from bound antibodies in sorting endosomes and recycled to the parasite surface while the antibody is directed to a distinct pathway for degradation [8] , [20] , [22] , [23] , [24] . These distinct endosomal populations have been classified depending on the presence of several key components of the vesicle transport system i . e . Rab GTPases and other markers [25] . Specifically , early/sorting endosomes play a role in fluid-phase and transporter-mediated endocytosis and contain Rab5A or 5B [26] , whereas the recycling endosomes mainly contain Rab11 [24] . The dense packing of VSG at the parasites' surface prohibits recognition of conserved membrane proximal VSG epitopes by antibodies . To potentially circumvent this we introduced camelid IgG-derived 15 kDa nanobodies ( Nb ) , representing the intact antigen-binding domains of the unique camelid IgG2 or IgG3 90 kDa heavy-chain antibodies that are devoid of light chains ( HCAb ) [27] . The monomeric Nbs have dimensions of ∼4×2 . 2 nm and offer several advantages over antigen-binding fragments derived from classical antibodies [28] . High-affinity antigen-specific Nbs can be readily obtained from an immunised camelid and selected by phage display . They are also very robust and can be engineered efficiently into larger constructs to confer novel functionality and broaden their utility [29] . Moreover , several VSG-specific Nbs , directed towards distinct regions of the VSG molecule have already been identified , of which one targets a conserved VSG epitope that is on live trypanosomes inaccessible for larger antibodies [30] , [31] . We now report that Nbs recognizing VSG isotype-specific epitopes are competent in lysing parasites both in vitro and in vivo . These trypanolytic Nbs rapidly arrest cell motility , block endocytosis , cause FP swelling , collapse mitochondrial membrane potential and exhaust ATP , ultimately leading to parasite death . However , the Nbs become non-lytic when reconstituted into HCAbs in the absence of complement . Further , polyclonal antibodies directed against VSG recognise live trypanosomes without any detectable toxicity in the absence of complement , whereas proteolytically derived monovalent Fab or Nb antigen-binding fragments are trypanolytic . These data suggest that the antigen-binding fragment of an antibody harbours biological functions which remain latent in the intact immunoglobulin . Previously several monoclonal Nbs against Trypanosoma brucei AnTat1 . 1 were isolated by panning of a phage-displayed nanobody ( Nb ) library from lymphocytes of a VSG-immunised camelid [30] . Some of these Nbs appear to be highly specific for the AnTat1 . 1 VSG , while others exhibit cross-reactivity towards a wide variety of distinct VSGs . Surprisingly , the AnTat1 . 1 VSG-specific Nbs ( i . e . Nb_An05 , Nb_An06 and Nb_An46 ) provoke efficient lysis of AnTat1 . 1 parasites within five hours ( Fig . 1A ) . The specificity of this Nb-mediated trypanolysis is further confirmed , firstly by demonstrating that Trypanosoma brucei strains expressing MiTat1 . 1 , MiTat1 . 2 , MiTat1 . 5 and MiTat1 . 6 VSGs are not lysed ( Fig . 1B ) and secondly , via inhibiting trypanolytic activity by pre-incubation with a three-fold molar excess of purified soluble AnTat1 . 1 VSG prior to parasite challenge ( Fig . 1C ) . Furthermore , using the most potent trypanolytic Nb , i . e . Nb_An05 , it is noted that this lysis is dose-dependent ( Fig . 1D ) . Similar observations were obtained for Nb_An06 and Nb_An46 , but with different kinetics . Under identical assay conditions , Nb_An33 , which cross-reacts with multiple T . brucei VSGs , had no significant effect on parasite viability . To evaluate the therapeutic potential for Nbs in vivo , mice were infected with virulent monomorphic AnTat1 . 1A parasites and treated with lytic or non-lytic Nbs at daily intervals , starting at day one and progressing to day four post-infection . Untreated mice and those treated with the non-lytic Nb_An33 reached extremely high levels of parasitaemia within four days ( Fig . 1E ) . In contrast , mice treated with trypanolytic Nb_An05 , 06 or 46 had no detectable parasites during the entire treatment period . However , upon interruption of the Nb treatment , parasites reappeared in the blood and proliferated to ∼2×108 parasites/ml by day seven post infection ( Fig . 1E ) . The progress of the Nb-mediated trypanolysis was followed by immuno-fluorescence . Addition of ALEXA-labelled Nb_An05 to AnTat1 . 1 trypanosomes , maintained at 4°C , stained the parasites over their entire surface ( Fig . 2A upper left panel ) , whereas at 37°C , the stain concentrated rapidly in the flagellar pocket ( FP ) ( Fig . 2A , upper middle panel ) . Monitoring trypanosomes at 37°C revealed that the parasites were rapidly hampered in their mobility , within minutes following the addition of lytic Nbs , and finally became immobile . Subsequently , morphological abnormalities became noticeable whereby a gradual swelling takes place until a globular shape is adopted was attained . These parasites exhibited progressively weaker Nb_An05 surface staining ( Fig . 2A ) , and eventually lysed . Although the kinetics are slightly distinct , highly similar observations were obtained for Nb_An06 and Nb_An46 . Transmission electron microscopy on ultrathin sections was used to investigate these morphological changes further ( Fig . 2B ) . The early and prominent feature was emergence of a large vacuole , determined to be the FP on account of morphology , presence of a flagellum , and kinetoplast , matrix material in the lumen and position within the cell [13] , [32] . No FP enlargement was observed in the absence of Nbs ( Fig . 2B ) . The FP surface area after one hour incubation was significantly increased in the presence of trypanolytic Nb_An05 as compared to control cells either treated with non-lytic Nb_An33 or no Nb ( Fig . 2C ) . Interestingly , a small but significant increase in FP surface area was observed between parasites incubated with non-lytic Nb_An33 compared to untreated parasites . Nb_An05 and Nb_An46 stain the FP more intensively when compared to the non-lytic Nb_An33 ( Fig . 3 ) . Furthermore , in the presence of lytic Nbs , especially Nb_An05 , the FP is greatly enlarged as compared to the control Nb_An33 where the FP size remains essentially unaltered . Both , enlargement of the FP and failure to traffic the Nbs into internal compartments suggest that endocytosis is blocked at the FP . A flow cytometry-based pulse-chase experiment indicated greatly impaired clearance of Nb-VSG complexes when parasites are incubated with trypanolytic Nbs compared to non-lytic or conventional anti-VSG antibodies ( Fig . 4A ) . Interestingly , clearance of the non-lytic Nb_An33 was slower than conventional anti-VSG IgGs . Since motility plays a key role in clearance of antibody-bound VSG , we evaluated the effect of trypanolytic Nbs on parasite motility . Within 10–60 minutes of exposure to lytic Nbs greatly reduced parasite motility occurs , which precedes cell lysis ( Fig . 4B and C ) . Endocytosis is temperature dependent [20] , [33] , and specifically at 4°C is fully arrested [34] . The Nb-mediated lysis gradually decreased at lowered temperature , reaching a minimum at 4°C ( Fig . 4D ) . We next investigated the possible influence of trypanolytic Nbs with transporter-mediated and fluid-phase endocytosis using FITC-labelled transferrin and dextran , respectively ( Fig . 4E & F ) . Clearly , both transporter-mediated and fluid-phase uptake were reduced rapidly following the addition of lytic Nbs and this suggests that the presence of these Nbs in some manner obstructs endocytosis . Furthermore , Nb_An05 or Nb_An46 greatly reduced 2-deoxy-D-[3H]glucose uptake , which relies primarily on facilitated diffusion through glucose transporters , rather than endocytic activity [35] , [36] ( Fig . 4G ) . The non-lytic Nb_An33 did not have this effect . This reduced accumulation of glucose , which provides the major carbon source for glycolysis , may underlie the decline in cellular ATP at later time points when parasites are incubated with trypanolytic Nbs . The ATP levels were unaffected by non-lytic Nb_An33 ( Fig . 4H ) . The arrest of facilitated diffusion and endocytosis occurs within a time span of ∼10 minutes , whereas the energetic crisis through ATP depletion and the loss of mitochondrial membrane potential as assayed with the cationic dye MitoPT JC-1 ( Fig . 4I ) are clearly a secondary effect of the presence of the trypanolytic Nbs . Internalisation of surface VSG and fluid-phase uptake are both clathrin-mediated [23] , [32] , while endocytosis and recycling is regulated by Rab5A and Rab11 [8] , [37] . The localization and expression of these endocytic markers was determined during Nb-induced trypanolysis . ALEXA-labelled Nb_An05 and Nb_An46 stained the entire parasite surface and accumulate in the FP , but no obvious co-localization with clathrin or Rab11 occurs after 30 or 60 minutes ( Fig . 5A ) . Surprisingly , there is also no co-localization observed for the ALEXA-labelled control Nb_An33 with clathrin and Rab11 , indicating that none of these Nbs are internalized to a detectable level . Next , protein levels of clathrin , Rab5A and Rab11 were determined after zero , one and two hours incubation with Nb_An05 , Nb_An46 or control Nb_An33 by Western blotting ( Fig . 5B ) . The protein levels of clathrin , Rab5A and Rab11 declined after incubation with Nb_An05 and Nb_An46 , but remain unaffected with Nb_An33 . While reduction of Rab5A , Rab11 and clathrin correlates with swelling of the FP and is in accordance with earlier data [38] , it is unlikely that this represents the direct mechanism whereby endocytosis is compromised , and may rather reflect macromolecular leakage from the cells during the lysis period . The observation of a decrease of detectable Rab5 and Rab11 , while BiP levels are unaffected may be due to the requirement of an extensive cell lysis; Rab5 and Rab11 are cytosolic proteins , but BiP is located within the lumen of the endoplasmic reticulum ( ER ) . Our experiments with murine infections revealed that trypanolytic Nbs are able to control trypanosome levels ( see Fig . 1E ) . However , camelids that produce anti-VSG HCAbs do suffer from trypanosomiasis , suggesting that the presence of anti-VSG antibody alone is insufficient for parasite control [39] . To resolve this potential contradiction we reconstituted the Nbs into a monoclonal anti-VSG HCAb molecule . The coding sequence for the AnTat1 . 1-specific and trypanolytic Nb_An05 was fused to the Fc-domain , including the hinge , of human IgG1 , and this construct was transfected into NSO cells . The transfectants secrete 90 kDa Nb-Fc homodimers that lack both the CH1 domain and the light chain , and are similar to naturally occurring camelid HCAbs ( see Fig . 6A upper panel ( 2 ) ) . Surprisingly , addition of the purified chimeric Nb_An05-Fc HCAb to AnTat1 . 1 trypanosomes fails to induce lysis ( Fig . 6B ) . Nevertheless , the synthetic HCAb was perfectly functional in terms of antigen binding , bivalency and effector function as ( i ) Nb_An05-Fc HCAb recognised the VSG antigen by ELISA and surface plasmon resonance , ( ii ) addition of larger amounts of HCAb led to parasite aggregation , and ( iii ) addition of guinea pig complement to the trypanosomes exposed to the HCAb elicited complement-mediated parasite lysis ( Fig . 6B ) . To confirm that the presence of the Fc-domain in the reconstituted HCAbs abolished the Nb trypanolytic activity , the monoclonal chimeric Nb_An05-Fc HCAb protein was digested with pepsin and papain to release ( Nb ) ′2 and Nb , respectively ( see Fig . 6A upper panel ( 3 ) and ( 4 ) ) . Remarkably , these proteolytic fragments regained the lytic activity towards AnTat1 . 1 trypanosomes , especially the monovalent Nb obtained by papain digestion ( Fig . 6C ) . The data above suggested that intact immunoglobulins may possess latent functions that become apparent once the Fc and antigen-binding domains are separated . Therefore , we tested the trypanolytic activity of intact camelid serum antibodies from animals immunised with AnTat1 . 1 sVSG and from which the cloned Nbs were derived . Camelid serum contains two classes of IgG [27]; conventional 150 kDa antibodies consisting of light and heavy chains , and 90 kDa HCAb consisting of heavy chains only . The conventional subclass i . e . IgG1 and the HCAb subclasses , i . e . IgG2 and IgG3 , of the immunised camelid were purified by differential adsorption on Protein-A and Protein-G . Antibodies in these fractions recognize purified AnTat1 . 1 VSG in ELISA and Western blot and also stain living T . brucei parasites by flow cytometry and immunofluorescence [30] . Addition of the purified camelid IgG1 fraction to trypanosomes , in absence of complement , did not lyse parasites ( Fig . 7A ) . However , proteolysis of the camelid IgG1 by pepsin and papain resulting in 100 kDa Fab′2 and 50 kDa Fab fragments respectively , of which only the latter demonstrated trypanolytic activity ( inset Fig . 7A , right panel ) . Similarly , protease digestion of camelid polyclonal HCAb IgG2 and IgG3 yields bivalent 35 kDa Nb′2 and monovalent 15 kDa Nb antigen-binding fragments ( inset Fig . 7B and C , respectively ) . While incubation of AnTat1 . 1 trypanosomes with camelid HCAbs did not result in lysis , the ( Nb ) ′2 fragments elicited moderate lysis following prolonged incubation periods , while the corresponding Nb fragments provoke significant lysis ( Fig . 7B and C ) . These results are consistent with previous observations for bivalent Nb′2 and monovalent Nbs derived from reconstituted Nb-Fc HCAbs ( Fig . 6B ) . To assess whether trypanolysis could be achieved by non-camelid antibodies , we immunized a rabbit with AnTat1 . 1 sVSG . The IgG fraction contained antibodies that recognized purified VSG by ELISA and Western blot ( see Fig . 1 in [30] ) , and stained the entire surface of parasites expressing AnTat1 . 1 VSG ( data not shown ) . Similarly , pepsin and papain digestions of the rabbit IgG were performed , generating Fab′2 and Fab , respectively ( Fig . 7D , inset ) . The effect of the pools of polyclonal rabbit IgG , Fab′2 and Fab fragments in absence of complement was tested on trypanosomes in vitro . Only the Fab fragments lysed parasites significantly over a four hour incubation period ( Fig . 7D ) . Collectively , these data demonstrate that generation of bivalent antigen-binding fragments suppresses the trypanolytic property of a monomeric Nb or Fab . Besides the molecular weight and monovalency , we considered that the antigen binding properties may contribute to the trypanolytic activity . Therefore , the affinity and epitope specificity of the Nbs were analysed by surface plasmon resonance ( SPR ) and flow cytometry . The competitive or cumulative binding of Nbs to the AnTat1 . 1 antigen on intact parasites ( Fig . 8A ) revealed that Nb_An05 and Nb_An06 share overlapping VSG epitopes , which are distinct from the epitopes recognized by Nb_An46 and Nb_An33 . The binding of the two distinct lytic Nbs , Nb_An05 and Nb_An46 , to immobilised VSG occurs with comparable kinetic on-rates of 3 . 5 and 7 . 4×105 M−1 s−1 respectively and more distinct off-rates of 2 . 3×10−3 and 3 . 25×10−2 s−1 . Equilibrium dissociation constants ( KD = koff/kon ) of 6 . 6 , 18 and 44 nM were calculated for Nb_An05 , Nb_An46 and Nb_An33 ( Fig . 9 and Table 1 ) . Interestingly , the trypanolytic Nbs exhibited smaller koff values than the non-lytic Nb_An33 . The SPR measurements suggest that the Nb-mediated trypanolysis may only occur above a critical threshold koff value ( Fig . 9 ) . To test this , the trypanolytic Nb_An05 was subjected to randomization of select tyrosine residues in its complementarity determining regions 1 and 3 ( Fig . 10A ) . This resulted in the identification of several paratopic variants that retained trypanosome-binding , but with greatly reduced affinity ranging from 120 nM to 2 µM as compared to the 6 . 6 nM affinity for the wild type Nb_An05 ( Fig . 10B , Table 1 ) . Relative to the wild type Nb_An05 , these variants had kon rates reduced by 2 to ∼50-fold , whereas the koff rates are ∼1 . 5 to ∼25-fold faster . Therefore the variants exhibit a wide diversity in KD , kon and koff constants . When tested against live trypanosomes , two Nb_An05 mutants , Nb_An05-04 and Nb_An05-12 , retained some lytic activity against the parasite ( Table 1 ) . Remarkably , these two Nbs had the slowest koff rates of all the variants . Overall , this experiment indicates that a koff rate slower than 10−2 s−1 is required to detect trypanosome lysis under our current in vitro test conditions . The correlation between KD or kon and trypanolysis is less clear . Trypanosoma brucei has evolved very efficient systems for immune evasion , which include antigenic variation and mechanisms for removal of antibody-VSG complexes from the surface by endocytosis and proteolysis of the immunoglobulin . Hereby , the VSG is efficiently recycled [8] . It is conceivable that uptake of antibody-VSG complexes and subsequent trafficking is influenced by the antibody valency and molecular weight . Exposing trypanosomes to the antigen binding domain ( Fab or Nb ) of an immunoglobulin alone represents a non-physiological circumstance , and the evidence presented here suggests that this presents a challenge which the parasite may be unable to circumvent . We show that small , monovalent VSG-specific antibody fragments , Fabs or Nbs , efficiently lyse trypanosomes both in vitro and in vivo . Hereby , the monovalency of these fragments is pivotal for trypanolysis as bivalent Nb′2 are significantly less lytic than monomeric forms . Reconstitution of monovalent Nbs into an HCAb ( increasing valency , molecular weight and incorporating an Fc-domain ) abolished the trypanolytic activity in vitro , whereas remarkably , releasing the Nb domain via proteolysis of the recombinant HCAb restored the trypanolytic activity . This suggests that intact , bivalent monoclonal or polyclonal immunoglobulins , including rabbit and camelid classical antibodies and camelid HCAbs , are essentially harmless to trypanosomes in the absence of complement or any other bystander effector . In contrast , polyclonal Fabs or Nbs derived from the serum antibodies and deprived of classical effector Fc-domains , acquire trypanolytic activity . It should be emphasized that the trypanolytic potency of recombinant Nbs , Nbs from polyclonal HCAbs ( IgG2 or IgG3 ) , and Fabs from polyclonal IgG are not directly comparable as the exact titre of VSG-specific antigen-binding fragments within the polyclonal pool is unknown and the efficiency of lysis is clearly concentration dependent ( Fig . 1D ) . Immunoglobulins evolved with the antigen binding site ( Fab ) at one pole and with Fc effector functions that trigger complement-mediated killing and receptor-mediated phagocytosis at the other . Normally these effector functions are exerted only following antigen binding and are mediated by the Fc-domain . Nevertheless , intrinsic activities within antigen-binding domains may be present . For example Nbs with competitive enzyme inhibiting capacity [40] , [41] or Fabs with catalytic activity , ‘Abzymes’ , have been described [42] , [43] . In addition , Fabs can exhibit an intrinsic ability to convert molecular oxygen into hydrogen peroxide , which may contribute to destruction of the bound antigen [44] , [45] . This latter activity cannot be at the origin of the trypanolytic activity described here , as hydrogen peroxide formation occurs in the hydrophobic cavity between the VH and VL domains and reduction of singlet oxygen is catalysed by VH residues Trp-36 and Trp-47 [46] . These conditions are absent in Nbs , which lack a VL-domain , while Trp47 is also substituted . Moreover , we were unable to detect hydrogen peroxide formation during trypanolysis ( data not shown ) . Despite the highly similar phenotypes following trypanolytic Nb exposure and RNAi of specific endocytic factors [32] , [38] , [47] , two very important differences suggest distinct mechanism . Firstly , the Nbs elicit FP enlargement much more rapidly than RNAi , and too quickly for this to be possible via turnover of critical proteins . Secondly , the Nb is an exogenous agent . Other small exogenously delivered molecules , including aptamers [48] , cathelicidins [49] , neuropeptides [50] and a modified bovine host defence peptide ( BMAP-18 ) [51] can also elicit trypanolysis in the absence of any bystander or toxin , but their modes of action are clearly distinct from the Nbs . For example cathelicidins disrupt surface membrane integrity , which is preceded by immobilisation and rapid swelling of the parasite . Although immobilisation and swelling of the parasites also occurs with Nbs , the outer membrane integrity is not significantly affected as evidenced flow-cytometrically by the lack of leakage of FITC-labelled Dextran ( 4 kDa ) when parasites are incubated with trypanolytic Nbs . Therefore , with Nbs it seems that reduction in ATP-levels , with effects on motility , endocytosis and morphology , rather than direct surface membrane disruption , is a crucial step in parasite killing . The rapid and very severe block to endocytosis is remarkable and multiple lines of evidence demonstrate this; including accumulation of lytic Nbs in the FP and impairment to removal of the VSG-bound Nb from the parasite surface compared with non-lytic Nbs and IgG . The absence of intracellular staining or co-localisation with clathrin or Rab11 with any trypanolytic Nb strongly suggests that Nbs are not internalized to any significant degree , but temperature dependence suggests that this is an active process . It is possible that the protection accorded by lower temperature is due to inhibition of membrane transport , so preventing the FP enlargement . Drastic swelling of the FP is indicative of a block to bulk membrane endocytosis , in the presence of ongoing exocytosis , and has been reported previously in energy-depleted cells [52] . Furthermore , MitoPT™ JC-1 staining detected depolarization of the mitochondrial membrane at later times after trypanolytic Nbs exposure , but given an absence of intracellular Nbs , no direct effect on the mitochondrial membrane can be assumed . It is difficult to pinpoint the critical parameter responsible for the intrinsic destructive capacity of the monovalent , antigen-binding fragments and where a bivalent character or the presence of the Fc-domain is counterproductive for trypanolysis . Several factors are likely important , although they probably act synergistically in attaining lytic activity . Firstly , to be trypanolytic Nbs must bind VSG with high affinity . Mutagenesis-derived trypanolytic Nb_An05 variants that recognize the same epitope with modified binding kinetics indicate that toxicity requires slow release kinetics ( low koff ) , suggesting that prolonged interaction with VSG is beneficial to lysis . However , as monovalency dominates the binding parameters it is not possible to increase trypanolytic potency with bivalent constructs . Secondly , the Nb-VSG complex , unlike the IgG-VSG complex where the IgG potentially cross-links two VSG dimers , is not internalized and therefore remains at the surface . Engstler et al [12] found that smaller antibody fragments have reduced clearance from the parasite surface compared to intact antibodies and our results indeed confirm that there is greatly reduced VSG-Nb elimination from the surface; however in the case here we also find a failure to be internalized into the parasite cell . Third , the precise VSG epitope targeted by the Nb is likely important . Interestingly , some epitopes including the conserved N-glycan present on various VSG serotypes and recognized by Nb_An33 [30] , [31] failed to induce lysis , whereas Nb_An46 and Nb_An05 , targeting different epitopes , induced potent lysis . Remarkably , the competition binding experiments suggest that the most potent trypanolytic Nb has a binding site furthest from the membrane and may even occlude access of molecules to underlying epitopes ( see Fig . 8B ) . Fourth , the observation that parasites in presence of trypanolytic Nbs have reduced ATP levels suggests a correlation between energy-depletion and reduced endocytosis . Fifth , the observation that parasites in presence of trypanolytic Nbs exhibit a loss in mitochondrial membrane potential ( Δψm ) likely contributes to the observed reduced ATP levels . Sixth , the impaired flagellar motility observed very rapidly and only in presence of trypanolytic Nbs might be a crucial initiation step in the trypanolysis process [53] . Of the different mechanisms by which trypanolytic Nbs could cause lysis , the model we favour is that high affinity binding ( mediated by a low koff ) of trypanolytic Nbs to VSG impairs recycling of the surface , and within minutes this translates into impaired cellular motility . This rapidly blocks formation and/or budding of clathrin-coated pits , i . e . endocytosis . The swelling of the FP is likely to lead rapidly to cell lysis , which was also observed using clathrin RNAi . It is however unlikely that this process itself leads to decreased ATP , as ATP levels decrease more slowly than the onset of cellular defects . This slow loss of low molecular weight ATP ( 509 Da ) also effectively eliminates the possibility of rapid generation of pores or disruptions in the plasma membrane , although we cannot rule out possible smaller disruptions to the lipid bilayer that could result in ionic imbalance for example . Further , we also observed very rapid loss of glucose accumulation , but as glucose is mainly accumulated through GLUT channels in the bulk plasma membrane , it is unlikely that this is directly related to decreased endocytosis . One possible explanation for the decreased glucose transport across the plasma membrane is that lower endocytic activity and motility reduce the draw on ATP and hence glucose utilization , decreasing the concentration gradient for glucose transport into the cell , and hence lowering glucose uptake . It may also be that this reduced consumption masks an otherwise more prominent change to intracellular ATP levels . Therefore trypanolytic Nbs in some manner are able to compromise cellular energetics , but the connection between binding VSG , compromised endocytosis and lower cellular energy remains unclear . In contrast , Nb_An33 binds to a sugar epitope and therefore does not cause the above described phenotype . Furthermore , given that Nb_An33 has a higher koff-value means that it dissociates faster from the coat so that it can not exert a trypanolytic effect . In conclusion , the present work demonstrates firstly that high affinity antigen-binding antibody fragments can exert a direct biological “cytotoxic” function in the absence of the effector Fc-domain , and which is latent in intact immunoglobulins . Secondly , targeting the trypanosome surface with such small high affinity antigen-binding fragments is sufficient to efficiently kill the parasite . In addition , Nbs targeting various epitopes on the surface coat of trypanosomes offers possibilities for novel treatments for trypanosomiasis by developing small trypanotoxic compounds that compromise cell viability . Indeed , since the lytic Nb_An05 and Nb_An46 recognize distinct AnTat 1 . 1-specific peptidic epitopes , it seems that multiple sites on VSG could serve to target therapeutics . Moreover , the observation that Nb_An05 and Nb_An06 have overlapping epitopes that are not necessarily identical ( these Nbs have different CDR sequences ) suggests that the therapeutic epitope could be reduced in size to a small footprint of only a few hundred Å2 that might be conserved among VSGs of various serotypes . Although the specificity for a particular VSG , as is the case for the trypanolytic Nbs here imposes a limitation on therapeutic value , our data indicate that a cross-reactive therapeutic Nb recognizing many or all VSGs would require binding at a conserved VSG epitope with very high affinity . Under this assumption it might become feasible to design or select small organic compounds that would bind with high affinity to a VSG epitope , leading to trypanosome clearance , and as such be used as a novel therapeutical approach . The experiments , maintenance and care of mice complied with the guidelines of the European Convention for the Protection of Vertebrate Animals used for Experimental and other Scientific Purposes ( CETS n° 123 ) . The experiments for this study were approved by the Ethical Committee for Animal Experiments of the Vrije Universiteit Brussel , VUB , Brussels , Belgium ( Permit Number: 08-220-8 ) . Purification of Trypanosoma b . brucei ( AnTat1 . 1 , MiTat1 . 1 , MiTat1 . 2 , MiTat1 . 5 and MiTat1 . 6 ) bloodstream parasites , their soluble VSGs and the immunization of a camelid with AnTat1 . 1 sVSG was as described [30] . Camelid serum IgG fractionation , cloning , selection and purification of Nbs was according to published methods [30] , [54] , and reconstitution of HCAbs by fusing the Nb_An05 or Nb_An33 to the human Fc of IgG1 was as explained in [55] . Purified IgG was digested with 1% Hg-papain ( Sigma ) or porcine pepsin ( EC 3 . 4 . 23 . 1 , Sigma ) following the manufacturer's instructions . The digest was passed over Protein-G Sepharose and gel-permeation Superdex-200 ( 10/30 ) ( GE Healthcare ) in PBS ( pH 7 . 4 ) to purify Fab , Fab′2 , ( Nb ) ′2 or Nb . The protein concentration was assessed spectrophotometrically . From each antibody , or digested material , 130 pmole was loaded onto a 12% SDS-polyacrylamide gel ( under non-reducing conditions ) , and transferred to nitrocellulose . After blocking with 1% ( w/v ) bovine serum albumin , the membrane was incubated sequentially with a rabbit polyclonal anti-VHH IgG and a goat anti-rabbit-IgG antibody conjugated to horseradish peroxidase ( Sigma ) . In between the successive two hour incubations was a PBS-0 . 1% Tween 20 wash . Thirty minutes after adding chromogenic substrate ( methanol/4-chloro-1-nafthol in PBS/H2O2 ) the reaction was stopped by rinsing the membrane with water . The determination of the protein expression levels of Clathrin , Rab5A , Rab11 and BiP during the trypanolysis assay was performed as described elsewhere [56] . Chemiluminescense detection was by exposure to X-ray film ( Kodak BioMax MR ) , and ImageJ software used for quantification . For the affinity determination with Biacore 3000 , different concentrations , ranging from 500 nM to 7 . 5 nM , of Nb_An05 , Nb_An06 , Nb_An46 or Nb_An33 were added to a CM5 chip to which 500 RU of AnTat1 . 1 VSG had been coupled [57] . Sensograms were fitted for a 1∶1 binding model using the BIA-evaluation software version 4 . 1 ( GE Healthcare ) , resulting in kon , koff and KD values as output . The affinities of the Nb_An05 mutants were measured by surface plasmon resonance on a Biacore T100 system . Between 1000 and 1500 RU of soluble AnTat1 . 1 VSG was coupled onto a CM5 chip ( GE Healthcare ) via amine groups according to the manufacturer's descriptions using EDC and NHS as cross-linking agents and ethanolamine to block free esters . For the affinity determination , Nb concentrations ranging from 500 to 7 . 5 nM were added to the antigen-coated chip at a flow-rate of 30 µl/min in HBS buffer [10 mM Hepes ( pH 7 . 5 ) , 150 mM NaCl , 3 . 5 mM EDTA and 0 . 005% ( v/v ) Tween-20 ) ] . Bound Nbs were eluted with 10 mM glycine-HCl ( pH 2 . 5 ) . Sensograms were fitted for a 1∶1 binding model using the Biacore T100 Evaluation Software 2 . 0 . 2 ( GE Healthcare ) , calculating kon , koff and KD values . The different Nb clones were evaluated on live , bloodstream form AnTat1 . 1 trypanosomes through flow cytometry following a direct or three-step labeling procedure . The direct labeling required conjugation of Nbs with ALEXA Fluor 488 according to the manufacturer's instructions ( Molecular Probes ) . Hereby , parasites ( 2×105 in 100 µl PBS/10% FCS ) were cooled in an ice-bath ( 30 minutes ) before adding Nbs . After 10 minutes incubation with ALEXA-labelled Nbs ( 1 µg ) , cells were washed with ice-cold PBS/10% FCS and analyzed . The three-step labeling procedure relied on the detection of the surface-bound Nbs with a mouse anti-6⋅His IgG and a phycoerythrin-labeled rat anti-mouse IgG . Flow cytometry analyses were performed on a FACS Canto II and histograms were prepared using the FlowJo software ( Becton Dickinson , San Jose , CA ) . To evaluate the antibody-clearance rate by trypanosomes , a pulse-chase experiment was performed . This consisted of incubation of 2×105 parasites with 1 µg ALEXA-labelled Nbs ( Nb_An05 , Nb_An46 , Nb_An33 or irrelevant Nb ) or 10 µg rabbit polyclonal IgGs against VSG for 10 minutes on ice in HMI-9 medium/5% FCS . Next , the free antibodies were washed away by washing the parasites 2 times with ice cold HMI-9 medium . The parasites were resuspended at 5×106/ml in HMI-9/5% FCS in separate tubes and brought at 37°C . At different time-points ( 0-0 . 5-1-1 . 5-2 . 5-5-7 . 5-10-30-60-120 minutes ) , aliquots were washed with 2 ml HMI-9 to remove free antibodies . The parasites were resuspended in 100 µl HMI-9 followed by addition of 100 µl 4% paraformaldehyde/PBS to stop the metabolic activity . Following a 30 minutes fixation step , the cells were washed with ice-cold PBS/10% FCS and analyzed as described above . The mean-fluorescence intensity of parasites incubated with the antibody at time 0 was taken as the 100 percent signal . Nbs were labelled with ALEXA-488 ( Molecular Probes ) according to the manufacturer . Aliquots of 106 parasites were incubated with 10% normal rabbit serum in PBS for 30 min in an ice-bath before adding different ALEXA-labelled Nbs ( 1 µg ) , ALEXA-labelled rabbit polyclonal anti-VSG IgG ( 5 µg ) , camelid polyclonal anti-VSG IgG ( 5 µg ) or Nb_An-Fc chimer ( 6 µg ) . After 30 minutes the parasites were pelleted , washed with 10% normal rabbit serum in PBS , and analysed by fluorescence microscopy ( Nikon ECLIPSE E600 with phase contrast , 500×–1250× magnification ) . To assess the role of the membrane fluidity in uptake of Nbs , parasites were pre-incubated for 1 hour at 4°C or 37°C before adding ALEXA-labelled Nb_An05 or control Nb . After 30 minutes , parasites were washed 3 times with PBS/5% FCS and analysed by immuno-fluorescence microscopy . Individual samples were taken every 30 minutes and visualised by fluorescence microscopy to study the kinetics of Nb clearance by parasites . The co-localization experiments were performed as described [56] , [58] . Images were obtained using a Nikon ECLIPSE E600 epifluorescence microscope fitted with optically matched filter blocks and a Hamamatsu charge-coupled-device camera or a Leica confocal laser-scanning microscope . Images were false-coloured and assembled using Adobe Photoshop . In vitro: short term ex vivo trypanolysis was performed using 200 µl DEAE52-purified parasites ( stock: 106 parasites/ml HMI-9 medium/5% FCS ) which were incubated at 37°C at 5% CO2 in a humidified atmosphere with different antibodies ( rabbit polyclonal anti-VSG , Fab , Fab′2 , fractionated polyclonal camelid IgG , Nbs , ( Nb ) ′2 or Nb_An-Fc ) at a maximum concentration of 0 . 067 nmole . The surviving parasites were counted at regular intervals over a time period up to 5 hours using a Bürker hematocytometer . For the inhibition of the Nb-mediated trypanolytic activity , the Nbs were pre-incubated for 30 minutes with a 3-times molar excess of purified AnTat1 . 1 soluble VSG , prior to addition to the parasites . The percentage lysis was calculated relative to the condition with a non-trypanosome specific Nb or without Nb . For the site-directed mutagenized Nb_An05 trypanolysis experiments , 2×105 parasites in 200 µl HMI-9 medium supplemented with 10% decomplemented fetal bovine serum ( FBS ) were incubated with 1 µg Nb , followed by incubation at 37°C in a conditioned atmosphere with 5% CO2 for 5 hours . Lysis was quantified by parasite counting using a Bürker hematocytometer and the percentage lysis of the different paratope variants was calculated relative to that of wild type Nb_An05 ( i . e . 100 percent ) . In vivo: Eight-weeks old F1-mice were injected intra-peritoneally ( i . p . ) with 5000 virulent monomorphic AnTat1 . 1A parasites per mouse . Starting from day 1 till day 4 post infection , 100 µg Nb was i . p . injected . The parasitemia in 2 . 5 µl blood ( obtained from the tail vein of infected mice ) diluted in 500 µl PBS was monitored microscopically , and the survival of the mice was recorded . Parasites ( 2×105 in 200 µl HMI-9 medium/5% FCS ) were incubated for 1 hour at 37°C , 25°C , 15°C and 4°C to reduce or stop the membrane fluidity , before adding Nbs ( 1 µg ) and to monitor their survival over a 4–5 hour period . The interference from Nbs on the specific or non-specific uptake of nutrients by trypanosomes was assessed by adding FITC-labelled transferrin or dextran ( Sigma ) , respectively . After a total of 10 minutes incubation , parasites were pelleted , washed 3 times with HMI-9 medium/5% FCS , suspended in PBS and FITC-labelled nutrient uptake monitored by fluorescence readings ( Cytofluor II , PerSeptive Biosystems ) . The 2-deoxy-D-[1-3H]glucose ( 1 mM , 1 µCi , Perkin Elmer ) uptake by 2×106 parasites in presence of 2 µg Nbs was as described in [59] . Cells were lysed and the glucose concentration determined by triplicate measurements using a liquid scintillation beta-counter ( Perkin-Elmer Rackbeta , Boston USA ) . The total protein concentration was determined as described in [60] . The data are expressed as percentage of glucose uptake relative to the glucose-level of parasites incubated for the same time period without Nbs . To determine the effect of endocytosis disturbance by Nbs ( 10 µg ) on the internal ATP concentration , 107 parasites ( at 37°C ) were lysed after different time intervals by three freeze-thawing cycles . The ATP concentration of triplicate samples was quantified by the ATP-assay ( Molecular Probes ) . The Mitochondrial Permeability Potential ( Δψm ) was determined using the cationic dye MitoPT™ JC-1 ( Immunohistochemistry Technologies , Bloomington , MN ) , which exhibits potential-dependent accumulation in mitochondria . At low membrane potentials , JC-1 continues to exist as a monomer and produces a green fluorescence ( emission at 527 nm ) . At high membrane potentials or concentrations , JC-1 forms J aggregates ( emission at 590 nm ) and produces a red fluorescence . The staining procedure was as recommended by the suppliers and the trypanolysis assay was performed as described above . Briefly , after different time points of the trypanolysis assay a 1∶1 ratio of the MitoPT JC-1 staining solution was added and the cells incubated for an additional 15 minutes in a CO2 incubator at 37°C . Next , the cells were pelleted and washed twice with 2 ml assay buffer warmed at 37°C . Finally , the fluorescent signals were measured by flow cytometry . As positive control , the cells were incubated with a final concentration of 50 µM Carbonylcyanide m-chlorophenylhydrazone ( CCCP ) for 60 minutes in a CO2 incubator at 37°C . For electron microscopy cells were prepared as described elsewhere [38] . Observations were made on a Tecnai 10 electron microscope and images were captured with a MegaView II camera and processed with AnalySIS and Adobe Photoshop software . The co-localization experiments were performed as described in [56] . All manipulations were conducted using HMI-9/5% FCS . Parasites ( 107/ml ) were incubated with ALEXA-labelled Nbs ( 10 µg/ml ) at 37°C for 0–30 or 60 minutes followed by two washes with PBS and fixed with 4% paraformaldehyde ( PFA ) in ice-cold PBS . Immunofluorescence was performed as described in [58] with a few modifications . Using an ImmEdge pen ( Vector Laboratories , Burlingame , CA ) , compartments were drawn on a poly-lysine slide ( Polysine; VWR International , Leuven , Belgium ) and 200 µl of 4% PFA-fixed cells was placed in each compartment . The slides were incubated in a moist chamber , and the cells were allowed to settle on the slide followed by a permeabilisation with 0 . 1% Triton X-100 . Staining was performed as described previously [58] . The trypanosomal Golgi complex was stained using dapi ( Vectashield ) . Cells were observed either on a Nikon Microphot-FX epifluorescent microscope attached to a Photometrics CH350-CCD camera or with a Laser Scanning Microscope 510 ( Zeiss ) . Images were false-coloured and assembled using Adobe PhotoShop . To follow the kinetics of ALEXA-labeled Nb uptake , parasites were incubated ( 37°C ) in presence of ALEXA-labelled Nbs over a 2 hours time period . Next , parasites were washed twice with PBS/5% FCS , fixed with PFA in ice-cold PBS and processed as described above . Variants of Nb_An05 were generated by site-directed mutagenesis using degenerate primers ( 5′CCGGCCATGGCCGATGTGCAGCTGGTGGAGTCTGGGGGAGGCTCGGTA CTAACTGGAGGGTCTCTGAGACTCTCCTGTGCAGCCCCTGGAATCACCNHTCGTATGTACTGCATGGCC-3′ and 5′-GGAGACGGTGACCTGGGTCCCCCGGCCCCAGTAA GCAAACTCAGTTCCCTGAGCGGAGCCTGAADNGCAGCCADNGTCTCTTGCCG-3′ ) which allow replacement of tyrosine residues in complementarity determining regions ( CDR ) 1 and CDR3 that are anticipated to contribute in the interaction with the VSG-antigen . The PCR amplicons were subsequently cloned into pMES using PstI and BstEII restriction sites and individual mutant clones were screened for their functionality in a VSG-specific ELISA , using a peroxidase conjugated anti-6×His detection IgG ( Serotec ) . Nb_An05-02 ( HQ680967 ) , Nb_An05-04 ( HQ680968 ) , Nb_An05-05 ( HQ680969 ) , Nb_An05-06 ( HQ680970 ) , Nb_An05-12 ( HQ680971 ) , Nb_An05-17 ( HQ680972 ) , Nb_An05-19 ( HQ680973 ) .
Haemoparasites , such as African trypanosomes , have developed potent immune evasion mechanisms to avoid antibody-mediated elimination . Consequently , trypanosome surface antigen-specific immunoglobulins in the absence of complement are non-trypanocidal . In contrast , certain monovalent nanobodies ( Nb ) , monomeric antigen-binding domains derived from camelid Heavy-Chain Antibodies ( HCAb ) and which have a much lower molecular weight ( 15 kDa ) than classical antibodies ( 150 kDa ) , efficiently lyse trypanosomes both in vitro and in vivo . This is surprising as classically immunoglobulin effector functions are mediated via the Fc-domain , which is absent from the Nb . We demonstrate that the Nb-mediated trypanolysis depends on the low molecular weight , monovalency and high affinity and is associated with loss of motility , a major block to endocytosis , energy depletion and cell death . Overall , targeting the parasite surface with low molecular weight , high affinity Nbs is sufficient to exert a direct therapeutic action . Therefore , the exploitation of Nbs against African trypanosomiasis represents a novel therapeutic strategy . Furthermore , demonstration that a high affinity antigen-binding Nb or Fab fragment lacking an effector domain ( i . e . , Fc-domain or an attached toxin ) can exert a direct biological function , suggests that intact antibodies likely harbour latent functionality which only become revealed upon removal of the Fc-domain .
You are an expert at summarizing long articles. Proceed to summarize the following text: Spider silk fibers are produced from soluble proteins ( spidroins ) under ambient conditions in a complex but poorly understood process . Spidroins are highly repetitive in sequence but capped by nonrepetitive N- and C-terminal domains ( NT and CT ) that are suggested to regulate fiber conversion in similar manners . By using ion selective microelectrodes we found that the pH gradient in the silk gland is much broader than previously known . Surprisingly , the terminal domains respond in opposite ways when pH is decreased from 7 to 5: Urea denaturation and temperature stability assays show that NT dimers get significantly stabilized and then lock the spidroins into multimers , whereas CT on the other hand is destabilized and unfolds into ThT-positive β-sheet amyloid fibrils , which can trigger fiber formation . There is a high carbon dioxide pressure ( pCO2 ) in distal parts of the gland , and a CO2 analogue interacts with buried regions in CT as determined by nuclear magnetic resonance ( NMR ) spectroscopy . Activity staining of histological sections and inhibition experiments reveal that the pH gradient is created by carbonic anhydrase . Carbonic anhydrase activity emerges in the same region of the gland as the opposite effects on NT and CT stability occur . These synchronous events suggest a novel CO2 and proton-dependent lock and trigger mechanism of spider silk formation . Spider silk fibers contain regions of crystalline and noncrystalline β-sheets , which mediate mechanical stability [1] . In contrast , the soluble spidroins ( dope ) stored in the tail and sac of major and minor ampullate silk glands [2] exhibit unordered and helical conformations [3] . How spiders rapidly convert the dope into a solid fiber at a defined site of the S-shaped duct has been extensively studied [4]–[8] , but major questions are unresolved: First , how is the pH gradient in the gland generated and maintained ? Second , what is the pH at the phase transition in the duct ? The pH in the major ampullate gland has been shown to decrease from 7 . 2 in the proximal parts of the sac to 6 . 3 in the beginning of the duct [7] , but it has also been proposed that the gradient goes from 6 . 9 in the sac to 6 . 3 in the third limb of the duct [6] . Third , how are the terminal domains affected by the conditions in the duct at a molecular level , and in particular , do they , as proposed [4] , [5] , act in similar manners ? Documented pH-dependent effects at a molecular level include that the N-terminal domain ( NT ) dimerizes at pH 6 [9]–[11] , but pH-induced structural changes of the C-terminal domain ( CT ) have only been observed at pH 2 [4] . Here we address these questions and unravel novel physiological mechanisms for regulated spider silk formation . By use of concentric ion selective microelectrodes ( ISMs ) [12] we determined the pH in the major ampullate gland of Nephila clavipes , from the proximal part of the tail to the middle part of the second limb of the duct . Concentrations of CO32− were also determined at locations where pH was high enough to allow reliable measurements , and used to calculate HCO3− concentrations . We found that the pH decreases from 7 . 6±0 . 1 ( n = 11 ) in the proximal tail to 5 . 7±0 . 0 ( n = 6 ) in the second limb of the duct and that HCO3− concentration increases from 5 mM in the proximal tail to 21 mM in the distal part of the sac ( Figure 1 and Table 1 ) . With these values in the Henderson–Hasselbalch equation , the carbon dioxide pressure ( pCO2 ) could be calculated and was found to increase along the gland ( Figure 1 ) . We observed that the intraluminal pH at different locations did not change despite superfusion of the gland with an elevated pCO2 . This indicates that the epithelium of the major ampullate gland does not allow permeation of CO2 , a phenomenon previously described for parietal and chief cells in gastric glands [13] . The concentrations of K+ , Na+ , and Cl− in the sac were determined to be 6 , 192 , and 164 mM , respectively , using concentric ISMs ( Table 1 ) . The observation of simultaneously decreasing pH and increasing HCO3− and CO2 concentrations from the proximal to the distal parts of the gland ( Figure 1 ) suggested that carbonic anhydrase ( CA ) could be involved through catalysing the conversion of H2O + CO2 ↔H++ HCO3− . By use of a histochemical method [14] we could indeed identify abundant CA activity in intracellular vesicles and at the apical cell membrane of the epithelium in the distal part of the major and minor ampullate sacs and ducts , as well as in aggregate gland ducts and tubuliform glands ( Figure 2A–E ) . The site in the major ampullate epithelium where CA was found to emerge ( Figure 2A ) exactly coincides with the location where the glandular epithelium ceases to produce spidroins [15] . To investigate whether CA is responsible for generating and maintaining the pH gradient , we immersed freshly dissected N . clavipes major ampullate glands in buffers containing methazolamide , a membrane-permeable CA inhibitor [16] . Exposure to methazolamide collapsed the pH gradient , and pH levelled out to approximately 7 in the tail and sac . The gradient could subsequently be restored by removing the methazolamide ( Table 2 ) . Thus , the pH gradient in the major ampullate gland is dependent on active CA . Because CA activity was found in the epithelium of the distal major ampullate duct ( Figure 2E ) , where also proton pumps are present [17] , the pH may well continue to drop along the entire duct—that is , below pH 5 . 7 now measured half-way through the duct . This needs to be experimentally verified , as the extremely small inner diameter in the second half of the duct ( <20 µm ) did not allow measurements with the currently used ISMs . To address the third unresolved question—that is , how the terminal domains are affected by the conditions in the duct at a molecular level—we first compared the in vitro structural stability of NT and CT in the broad pH gradient now observed . We studied isolated domains , and it may be that these domains behave differently in their natural context of full-length spidroins . However , we have observed that NT followed by five repeats behaves as the isolated domain in terms of pH-dependent dimerization [11] . Urea and temperature denaturation studies at different pH values were performed for recombinant NT and CT ( Figures 3 and 4 ) . The stability of NT towards urea remained largely unchanged between pH 7 . 5 and 6 . 5 , but was significantly increased between 6 . 0 and 5 . 0 ( Figure 3 ) . We here analyzed a minor ampullate spidroin ( MiSp ) NT , which has not been studied before , but a similar pH effect was recently shown for a major ampullate spidroin ( MaSp ) NT [11] . This indicates that the structural effects now observed are applicable to spidroins from major and minor ampullate glands , in concordance with the observation of CA in major and minor ampullate , aggregate , and tubuliform glands ( Figure 2 ) . A similar effect as seen for stability towards urea was seen for NT thermal stability; that is , it was increased at lower pH ( Figure 4 ) . Dimerization of NT is completed at pH 6 [11] , and the subsequent stabilization of NT dimers between pH 6 and 5 ( Figure 3 ) may result in the firm locking of spidroins into multimers in the distal part of the duct ( cf . , Figure 1 ) . CT , in sharp contrast to NT , was gradually destabilized towards urea ( Figure 3 ) and temperature ( Figure 4 ) when pH was lowered from 7 . 5 to 5 . 0 . Heteronuclear single quantum coherence ( HSQC ) nuclear magnetic resonance ( NMR ) spectra of CT showed a folded structure at pH 6 . 8 , whereas a gradual conversion to an unfolded state was observed at a pH below 5 . 5 , and at pH 5 . 0 , it is completely unfolded ( Figure 5 ) . Moreover , we observed that CT irreversibly converted from α-helical to β-sheet structure upon thermal denaturation at pH 5 . 5 , but not at pH 6 . 5 or 7 . 5 ( Figure 6 and Table S1 ) . The fact that NMR spectroscopy of CT shows an unfolded state at pH 5 . 0 ( Figure 5 ) whereas circular dichroism ( CD ) spectroscopy and urea denaturation shows residual structure at pH 5 . 0 ( Figure 3 ) may be explained by the different CT concentrations ( 0 . 3 mM versus 5 µM ) and recording times ( hours versus minutes ) used . It should also be pointed out that unfolded species should have increased NMR intensities ( and may thus be overestimated relative to folded species ) due to favorable relaxation and dynamic properties and that helical structure ( which is observed by CD ) may be present in the species that are observed as random coil/unfolded by NMR . Denaturation of NT , in contrast , resulted in mainly unordered structure and was reversible at all three pH values ( Figure 6 and Table S1 ) . It may be worth noting that the structural conversion now observed for CT , but not for NT , resembles that seen for the spidroin dope [18] , which may be relevant for the trigger mechanism as discussed below . Next , we used hydrogen-deuterium exchange mass spectrometry ( HDX-MS ) to study the backbone conformational dynamics of CT at pH 7 . 5 to 5 . 5 . No major differences in HDX were seen between pH 7 . 5 and 6 . 5 , but helices 2 , 3 , and 5 showed increased HDX at pH 5 . 5 compared to at pH 6 . 5 ( Figure 7 ) , indicating increased structural flexibility at lower pH . Previous studies of CT [4] , [19] have identified a strictly conserved salt bridge between an Arg residue in helix 2 and a Glu residue in helix 4 . The NMR structure of Araneus ventricosus MiSp CT now studied ( Figure 8 and Table S2 ) is very similar to those of MiSp CT from Nephila antipodiana [19] and MaSp CT from A . diadematus [4] with backbone root-mean-square deviations ( RMSDs ) of 2 . 4 Å and 3 . 4 Å , respectively ( over 202 residues from both chains; see Figure 8 ) . Largest differences are observed for the N-terminal helix , which is shorter , and the C-terminal helix , which is kinked near the C-terminus in the A . ventricosus MiSp CT structure . A salt bridge between Arg38 in H2 and Glu82 in H4 is indeed found in A . ventricosus MiSp CT ( Figure 8 ) . Computational pKa predictions [20] of the available CT structures uniformly suggested that the Glu residue in H2 ( that participates in the saltbridge ) has a pKa ≥6 , making it possible to protonate in the pH interval now observed in the gland , and mutations interfering with this salt bridge greatly destabilize CT [4] , [19] . Our results suggest that protonation of the conserved Glu in H2 is involved in pH-dependent unfolding of CT in spider silk glands , and further experimental studies are warranted to determine exactly what residues are protonated in CT at low pH . Although the NMR structures of several CTs from different spidroins have been solved and their biochemical properties have been studied , the now observed pH responsive behavior of this domain has not been investigated in detail before [4] , [5] , [19] , [21]–[23] . The shared overall fold suggests a conserved function of CT , but the possibility that CT has diverse functions in different silks cannot be excluded and is an important topic for further studies . The conditions now determined for the distal parts of the gland—that is , low pH combined with increasing HCO3− concentration and low CO2 permeability of the gland—imply that pCO2 is elevated along the sac and duct . For MaSp CT , it has been shown that shear forces induce conformational changes that result in increased exposure of nonpolar surfaces [4] , and CO2 interacts mainly with nonpolar regions in proteins [24] , [25] . Therefore , we used the CO2 analogue CS2 [24] to identify potential interaction sites in the NMR structure of A . ventricosus MiSp CT . CS2 interacts specifically with a few , mainly hydrophobic , CT residues distributed in helices 2–4 , of which many are partly buried ( Figure 9A–D ) . NT on the other hand shows weak interactions with CS2 and only at conditions that favor the monomeric form , at pH 7 . 2 and 200 mM salt ( Figure 10 ) , which is characteristic to parts of the gland where pCO2 is low ( Figure 1 ) . In contrast to CT , no specific interactions between NT and CS2 were found at pH 5 . 5 ( Figure 10 ) , suggesting that NT stabilization at low pH ( Figure 3 and Figure 4 ) protects its hydrophobic , buried residues from interacting with CO2 . Amyloid fibrils are β-sheet polymers formed from ( partly ) unfolded proteins in a nucleation-dependent reaction and are found in tissue deposits associated with disease but also in some functional protein aggregates [26] . Amyloid fibrils share similarities with the β-sheets of spider silk and have been observed in the distal third of the spinning duct by electron microscopy ( EM ) , and it was proposed that the spidroin repetitive parts are responsible for the amyloidogenic behavior [27] . The poly-Ala segments of spidroins need to rapidly form β-sheet structure in silk formation , although Ala is highly prone to form α-helices [28] , raising the question , What nucleates this process ? We investigated whether CT may convert to amyloid-like fibrils at low pH by measuring Thioflavin T ( ThT ) fluorescence of CT over time at different pH values . When ThT binds to β-sheet polymers in amyloid-like fibrils , it gives an increased fluorescence [29] . At pH 5 . 5 and below , CT converted to a ThT-positive state , which was not observed at higher pH , or for NT at any pH tested ( Figure 11A ) . Analysis of the ThT-positive aggregates by transmission EM showed typical amyloid-like fibrils , 5–10 nm thick , elongated and nonbranched ( Figure 11B ) . Only samples of CT incubated at pH 5 . 5 showed the presence of amyloid-like fibrils . Furthermore , the CT fibrils were positive for Congo red staining and showed green birefringence under polarized light ( Figure 11C ) , another hallmark of an amyloid-like fiber [30] . The spidroins' terminal domains are highly conserved , both between species and between different types of silks [31] , which suggest that they play important roles in spider silk formation rather than for the silks' mechanical properties . Further supporting the hypothesis of general polymerization mechanisms between different types of silks , CA is found in the distal parts of several different spider silk glands and occur at the same location as the observed structural changes of NT and CT will take place provided that their behavior in vitro is recapitulated in vivo . NT and CT are unique to spidroins and there are no known homologues . The lock ( accomplished by NT ) and trigger ( accomplished by CT ) mechanism proposed herein is therefore likely unique for spider silk formation , in contrast to the previously identified shear-induced polymerization mechanism that also apply to , for example , silk worm silk formation [32] . A detailed understanding of the natural spinning process will be vital for the development of a spinning process capable of generating truly biomimetic spider silk fibers and may provide novel insights into Nature's way of confining amyloid fibril formation to a specific location . In summary , the spidroin N- and C-terminal domains show synchronous and opposite structural changes in response to the physiological conditions of the spinning duct . CT unfolds into β-sheet nuclei that can trigger rapid polymerization of the spidroins , whereas gradually locked NT dimers alleviate the need for rapid diffusion [11] , [33] , firmly interconnect the spidroins , and allow for propagation of pulling forces along the peptide chains . These events are driven by CO2 and proton gradients that ensure temporal and spatial confinement of the divergent structural changes of CT and NT . This novel lock and trigger mechanism elegantly explains how silk formation can occur at a very high speed , more than 1 m/s [34] , and at the same time be confined to the very distal part of the spinning duct . Concentric ISMs [12] were used to measure the concentrations of hydrogen , carbonate , sodium , potassium , and chloride ions . Thin-walled borosilicate glass capillaries of two different diameters were used for construction of concentric ISMs . The capillary forming the outer barrel ( outer diameter 2 . 0 mm , inner diameter 1 . 5 mm , A-M Systems 6185 ) was pulled to a tip diameter of 2–4 µm using a Flaming/Brown micropipette puller ( Sutter Instrument Co . US , Model P87 ) . The tip of the outer barrel was silanized by back-filling with N , N-dimethyltrimethylsilylamine ( Fluka 41716 ) , after which the barrel was mounted on a micromanipulator and heated using a hot air gun giving temperatures of 200–300°C for 60 s . Ion-selective cocktails for H+ ( Fluka 95291 ) , CO32− ( described by Chesler et al . ) [35] , Na+ , K+ , and Cl− were sucked into the tip to form a 100 to 200 µm long column , and a backfilling solution ( pH electrode , 150 mM NaCl pH 7 . 4; CO32− electrode , 10 mM NaHCO3 , 150 mM NaCl ) was added in the middle of the outer barrel . The inner barrel ( outer diameter of 1 . 2 mm and inner diameter of 0 . 9 mm , A-M Systems 6160 ) was pulled to a tip diameter of 1 µm and filled with 3 M KCl pH 7 . 4 . The inner barrel was then inserted into and secured in the outer barrel , the inner glass tip being positioned 4–10 µm away from the outer barrel tip . A silver wire was inserted into the inner barrel and connected to an amplifier . The ISMs were calibrated using pH 6 . 87 and pH 7 . 42 buffers , 50 , 100 , 200 , and 400 mM Na+ or Cl− , or using 1 , 2 , 4 , and 8 mM K+ , respectively . Carbonate electrodes were calibrated as described [35] . Adult female N . clavipes collected in Florida from September to November were kept in individual containers and fed water . Spiders were anaesthetized with CO2 gas before severing at the pedicle . Dissection of the major ampullate glands was carried out in a modified spider Ringer [36] ( with 2 mM MgCl2 , 2 mM CaCl2 , 3 mM KCl , and 10 mM glucose ) buffered with 26 mM bicarbonate and 5% CO2 , yielding a pH of 7 . 4 . Major ampullate glands were mounted in a submersion-style incubation chamber and superfused with HCO3− and CO2-buffered modified spider Ringer at room temperature . ISM measurements were performed in triplicates in different parts of the gland . The difference in potential between the bath and the inside of the gland was recorded on a chart recorder ( Zipp and Konnen ) and later translated into change in concentration of the ion of interest using the Nernst equation ( H+ , Na+ , K+ , Cl− ) or a modified Henderson–Hasselbalch equation [35] ( CO32− ) to get the concentration of HCO3− . Determined pH values and HCO3− concentrations were used to calculate pCO2 according to the Henderson–Hasselbalch equation , assuming equilibrium . To study the influence of CA activity on the pH gradient , some glands were incubated for 1 h in 0 . 1 mM methazolamide ( M4156 , Fluka ) , a membrane-permeable CA inhibitor , prior to pH measurements , after which the methazolamide was washed away for 30 min and pH measurements repeated . Some glands were subjected to CO2 permeability studies . Glands were dissected , mounted , and superfused with HCO3− and CO2-buffered spider Ringer at room temperature as described above . A pH electrode was inserted into the gland , after which the surrounding Ringer solution was buffered by 26 mM bicarbonate and 100% CO2 . pH measurements were continued up to 1 h to see if intraluminal pH changed in response to the elevated pCO2 surrounding the gland . The Ringer solution was then changed again , being buffered by 26 mM bicarbonate and 5% CO2 , yielding a pH of 7 . 4 , after which the pH electrode was removed from the gland and put in the Ringer and pH was recorded . This was made to ensure that the electrode had not been drifting . Spiders ( A . diadematus , N . clavipes , E . australis , and Tegenaria sp . ) were anesthetized and sacrificed as described above . Dissection was carried out in 67 mM sodium phosphate buffer at pH 7 . 2 or in a modified Spider Ringer ( see above ) . Some opisthosomas were fixed and embedded directly after removal of the exoskeleton , whereas others were dissected so that the major and minor ampullate glands could be isolated before fixation . Tissues for histochemical localization of CA activity were immersion fixed in 2 . 5% ( v/v ) glutaraldehyde in 67 mM phosphate buffer , pH 7 . 2 , for 24 h at 4°C and subsequently rinsed with phosphate buffer , pH 7 . 2 . After fixation , tissues were dehydrated using increasing concentrations of ethanol , infiltrated and embedded in a water-soluble glycol methacrylate ( Leica Historesin embedding kit ) . Historesin embedded major and minor ampullate glands and opisthosomas were sectioned at 2 µm in a microtome ( Leica RM 2165 ) and stained for CA activity using a histochemical method [14] . The method involves incubation of sections in a medium containing NaHCO3 , CoSO4 , H2SO4 , and KH2PO4 , whereby carbon dioxide leaves , pH increases , and a cobalt–phosphate–carbonate complex is formed at sites with CA activity . This complex is then converted into a black cobalt–sulphide precipitate . The sections were counterstained with Azure blue . For control of unspecific staining , the CA inhibitor acetazolamide was included in the incubation medium . A . ventricosus MiSp NT and CT coding gene fragments corresponding to ( NT: GSGNSQPIWT NPNAAMTMTN NLVQCASRSG VLTADQMDDM GMMADSVNSQ MQKMGPNPPQ HRLRAMNTAM AAEVAEVVAT SPPQSYSAVL NTIGACLRES MMQATGSVDN AFTNEVMQLV KMLSADSANE VST ) and ( CT: GSGNSTVAAY GGAGGVATSS SSATASGSRI VTSGGYGYGT SAAAGAGVAA GSYAGAVNRL SSAEAASRVS SNIAAIASGG ASALPSVISN IYSGVVASGV SSNEALIQAL LELLSALVHV LSSASIGNVS SVGVDSTLNV VQDSVGQYVG ) were amplified by PCR with the full-length MiSp gene as template [37] , cloned into a modified pET vector ( resulting in the target proteins being fused to His tag–Thioredoxin–His tag followed by a thrombin cleavage site ) and transformed into BL21 ( DE3 ) Escherichia coli . The E . coli were grown at 37°C in LB medium containing 70 mg/l kanamycin until OD600 was about 0 . 9 . The temperature was lowered to 30°C , IPTG was added to a final concentration of 0 . 3 mM , and the cells were incubated for about 4 h . The E . coli were then harvested by centrifugation at 6 , 400×g for 20 min at 4°C ( Sorvall RC 3BP+ , 500 ml flasks ) , after which the pellet was resuspended in 20 mM Tris pH 8 . 0 , 1 mg/ml lysozyme was added , and the solution was incubated on ice for 30 min . Next , DNase and MgCl2 were added and the mixture was kept on ice for 30 min . The cell lysate was centrifuged ( 27 , 000×g ) at 4°C for 20 min ( centrifuged as above , 50 ml tubes ) . For purification of CT , the supernatant was loaded on a Ni-NTA column and the fusion protein was eluted with 300 mM imidazole . For purification of NT , which is mainly found in the pellet after lysis , pellets were resuspended in 20 mM Tris pH 8 . 0 containing 2 M urea , sonicated for 2 min , and the supernatant was treated as for CT . The fusion proteins were then dialyzed against 20 mM Tris pH 8 . 0 overnight at 4°C , cleaved by 1/1 , 000 ( w/w ) thrombin , and run over a Ni-NTA column to remove the fusion tag . This resulted in essentially pure NT or CT ( >90% purity as determined by SDS PAGE gel electrophoresis and Coomassie staining ) . For NMR structure determination , we initially expressed a 150-amino-acid-residue-long C-terminal part of A . ventricosus MiSp ( full-length sequence above ) . The expressed protein was labeled with 15N , and the NMR spectrum showed that the first 25 residues adopt random coil fold . Therefore , A . ventricosus MiSp CT was truncated and residues 31–150 ( marked in bold in the sequence above ) were expressed in minimal medium and labeled by 15N and 13C/15N . The NMR sample was prepared by adding 8% ( v/v ) D2O and 0 . 02% ( w/v ) NaN3 to a 1 mM solution of uniformly 13C/15N-labelled protein in 20 mM sodium phosphate buffer ( pH 6 . 8 ) with 20 mM NaCl . All NMR experiments were carried out at 298 K on a 600-MHz Varian Unity Inova spectrometer equipped with an HCN triple-resonance pulsed-field-gradient cold probe . The following 2D and 3D spectra were acquired for backbone resonance assignment ( number of complex points given in parentheses ) : [15N-1H]-HSQC ( 1024×128 ) , HNCA ( 1024×24×40 ) , CBCA ( CO ) NH ( 2048×48×40 ) , HNCO ( 1024×24×40 ) , HN ( CA ) CO ( 1024×24×40 ) , and for side-chain assignment and NOE restraint collection ( mixing time given in parentheses ) : 15N-resolved NOESY-HSQC ( 1024×38×150 , 60 ms ) , 13C ( aliphatic ) -resolved NOESY-HSQC ( 768×52×150 , 60 ms ) , and 13C ( aromatic ) -resolved NOESY-HSQC ( 1024×16×150 , 60 ms ) . Additionally , in order to identify intermolecular NOEs , a 13C/15N-filtered 13C ( aliphatic ) -resolved NOESY-HSQC spectrum ( 768×34×70 , 60 ms ) was recorded on a sample containing 50% 13C/15N-labelled and 50% unlabelled proteins [38] that was prepared by mixing equal amounts of labeled and unlabelled proteins in 8 M urea followed by dialysis against the NMR sample buffer . The same sample was afterwards used to probe interactions with CS2 . Aliquots of 20% CS2 in DMSO were added in a stepwise manner to the NMR sample of CT , yielding CS2 concentrations of 50 mM , 100 mM , and 200 mM , and a 2D [15N-1H]-HSQC spectrum was recorded each time . To account for perturbations due to DMSO , a reference experiment was performed by adding DMSO only in the same amounts . CS2-induced chemical shift perturbations were calculated by comparing the spectrum at 200 mM CS2 with the spectrum at the end of the reference titration with DMSO , and using the formula ( ) [39] . All spectra were processed with Bruker TopSpin 3 . 1 and analyzed using CARA [40] . The assigned chemical shifts have been deposited in BioMagResBank ( accession number 19579 ) . To probe interactions between NT and CS2 , NT from MaSp1 from E . australis was expressed and purified as previously described [10] . Chemical shift perturbations of MaSp NT backbone amides were determined at pH 7 . 2 and 200 mM NaCl and at pH 5 . 5 upon addition of CS2 ( 0 to 200 mM ) as described for CT . For 2D [15N-1H]-HSQC NMR spectra of MiSp CT , samples at pH 5 . 0 , 5 . 3 , and 5 . 5 were prepared by diluting 50 µl of a concentrated stock solution of MiSp CT in 20 mM sodium phosphate buffer , 20 mM NaCl , 0 . 03% NaN3 , pH 6 . 8 with 200 µl of 100 mM CD3COOD/CD3COONa , 20 mM NaCl , 0 . 03% NaN3 buffer , and adding 20 µl of D2O . Automated peak picking of the three NOESY spectra was performed using UNIO-ATNOS/CANDID 2 . 0 . 2 [41] . Distance restraints were obtained from these peak lists using the internal NOE calibration procedure of CYANA 2 . 1 [42] . Intermolecular contacts were identified by analysis of the 13C , 15N-filtered NOESY spectrum and used as distance restraints with an upper limit of 5 Å . No explicit torsion-angle restraints were used in the input . Structure calculations were performed using CYANA 2 . 1 [42] and involved seven iterations of automated NOE assignment with the routine CANDID [41] followed by a simulated annealing procedure starting in the first cycle from a homology model generated based on the MiSp CT structure from N . antipodiana [19] ( PDB accession code 2M0M ) that was annealed in 15 , 000 steps of torsion-angle dynamics . This approach was used to reduce the assignment ambiguity during the first cycles of the automated NOE assignment and resulted in significantly more unambiguous distance restraints in the final cycle of the calculation concomitantly with a lower target function value . The 20 conformers with the lowest residual restraint violations were energy minimized in a water shell using the program CNS 1 . 2 [43] , [44] , and their coordinates were deposited in PDB ( accession code 2MFZ ) . Table S2 shows an overview of the restraints used and structural statistics . Ramachandran statistics for structured part ( residues 20–120 ) are 94 . 2% most favored , 5 . 8% additionally allowed regions; for all residues including the unstructured N-terminal tail , 88 . 3% most favored , 11 . 1% additionally allowed , 0 . 3% generously allowed , and 0 . 3% disallowed regions . For analysis of amyloid fibril formation , 10 µM of A . ventricosus MiSp NT and CT were incubated under quiescent conditions at 25°C with 10 µM ThT in 20 mM sodium phosphate or 50 mM sodium acetate buffer with or without 154 mM NaCl at different pH values between 5 . 0 and 7 . 5 . ThT fluorescence was recorded on a BMG FLUOstar Galaxy plate reader using bottom optics in 96-well polyethylene glycol-coated black polystyrene plates with a clear bottom ( Corning Glass , 3881 ) using a 440-nm excitation filter and a 490-nm emission filter . For analysis of amyloid fibrils , 10 µM of A . ventricosus MiSp NT and CT were incubated overnight ( 12–16 h ) under quiescent conditions at 25°C in 20 mM sodium phosphate buffers with or without 154 mM NaCl at pH 7 . 5 , 6 . 5 , and 5 . 5 , respectively . Samples were incubated overnight and 2 µl were adsorbed on copper grids , stained with 2 . 5% uranyl acetate in 50% ethanol for about 20 s , and examined and photographed with a Hitachi H7100 electron microscope at 75 kV . Ten µM A . ventricosus MiSp CT was incubated at 37°C with shaking ( 250 rpm ) for 2 . 5 h at pH 5–7 in 20 mM sodium phosphate and 50 mM sodium acetate buffers , respectively . Samples were centrifuged , supernatant removed , washed with dH2O , and then centrifuged again . The supernatant was removed and 10 µl dH2O was added , the sample was vortexed , and droplets ( 0 . 8 µl ) were applied to microscopical slides , air dried , and stained with Congo red B [45] . After mounting under coverslips , the materials were examined in a polarization microscope for Congophilia and green birefringence . A . ventricosus MiSp NT and CT stability between pH 5 . 0 and 7 . 5 with and without 154 mM NaCl was determined by urea denaturation . Like in previous denaturation studies of MiSp CT from N . antipodiana [19] , we used a two-state model for analyzing our denaturation data . Although a two-state transition is supported by a CD isodichroic point at 203 nm [46] for NT at low pH ( Figure 4A ) , this is not the case for CT at any pH , or NT at pH 7 . 5 ( Figure 4B ) . To emphasize that we assumed a two-state transition for both NT and CT , the urea concentrations derived from fitting the data to a two-state unfolding model are referred to as apparent half-denaturation ( [den]50% ) . Notably , the main conclusion from these experiments—that NT and CT respond in completely opposite ways to lowered pH—is not dependent on whether a two-state transition applies or not . For NT , urea-induced denaturation was performed by diluting the protein to 5 µM in 20 mM HEPES/20 mM MES with 0–7 M urea in 0 . 25 M steps . Tryptophan fluorescence emission spectra were measured on a spectrofluorometer ( Tecan Safire 2 ) using Costar black polystyrene assay plates with 96 flat bottom wells . The samples were excited at 280 nm using a 5 nm bandwidth , and emission spectra were recorded in 1 nm steps between 300 and 400 nm using a 10 nm bandwidth . Spectra were recorded at constant pH values ranging from 5 . 0 to 7 . 5 with 0 . 2–0 . 5 unit steps . For CT , CD spectroscopy at 222 nm was used to determine [den]50% as a function of pH . The CT samples were diluted to 7 . 5 µM in 20 mM sodium phosphate buffer and ran with 0–7 M urea in 0 . 25 M steps . At each pH , the average 222 nm CD ellipticity from three temperature scans for different urea concentrations were obtained with the settings described below ( see CD spectroscopy ) . The ellipticities for each measured pH values ranging from 5 . 0 to 7 . 5 with 0 . 2–0 . 5 unit steps were plotted against the urea concentration and fitted to a two-state unfolding model in order to determine the [den]50% by KaleidaGraph . CD spectra were recorded from 260 to 190 nm at 25°C in 0 . 1 mm path length quartz cuvettes using an Aviv 410 Spectrometer . The wavelength step was 0 . 5 nm , averaging time 0 . 3 s , scan speed 20 nm/min , time constant 100 ms , and bandwidth 1 nm . The spectra shown are subtracted for background and averaged over three consecutive scans . The HT voltages were always below 600 V during the entire scans . Spectra of 7 . 5 µM A . ventricosus MiSp NT ( 110 µg/ml ) or CT ( 90 µg/ml ) in 20 mM sodium phosphate buffer at pH 7 . 5 , pH 6 . 5 , or pH 5 . 5 were recorded at 25 , 45 , 65 , 85 , and 95°C and at 25°C again after cooling . For temperature melting curves , the CD at 222 nm was monitored between 25 and 95°C with 1°C/min increase . Deuteration buffers were prepared by freeze-drying 200 µl of 20 mM sodium phosphate buffer , pH 5 . 5 or 6 . 5 , followed by reconstitution in 200 µl D2O ( Cambridge Isotopes ) . A . ventricosus MiSp CT was diluted from 555 µM stock solution , pH 6 . 5 , to 55 . 5 µM in deuterated phosphate buffer , pH 6 . 5 or 5 . 5 . We removed 19 . 5 µl aliquots after 400 s , 50 min , 100 min , 200 min , or 300 min ( pH 5 . 5 ) or after 40 s , 5 min , 10 min , 20 min , or 30 min ( pH 6 . 5 ) . Aliquots were placed in prechilled 500 µl Eppendorf tubes containing 0 . 5 µl 5% TFA ( Merck ) , vortexed , and immediately frozen in liquid nitrogen . For a fully deuterated control , CT was incubated in deuterated phosphate buffer , pH 6 . 5 , for 24 h at 25°C . Samples were stored at −80°C until ESI MS analysis . Samples were thawed and immediately injected into an HPLC system using a chilled 25 µl Hamilton syringe . CT protein was digested in a Porozyme pepsin cartridge ( Applied Biosystems ) , and peptides were trapped and desalted in a Waters Symmetry C18 trap column ( Waters ) . Two 140D solvent delivery systems ( Applied Biosystems ) were employed , operating at 20 µl/min ( for washing with 0 . 05% TFA ) or at 15 µl/min ( for elution with 70% acetonitrile , 0 . 2% formic acid ) . Digestion and desalting were carried out in a single step for 10 min , and peptides were then eluted in a single step and delivered to the mass spectrometer via a TaperTip emitter ( Proxeon ) . The entire flow system was submerged in an ice bath . ESI spectra were acquired in positive-ion mode with a Waters Ultima API mass spectrometer ( Waters ) equipped with a Z-spray source . The source temperature was 80°C , the capillary voltage was 2 . 5 kV , and the cone and radiofrequency lens 1 potentials were 100 and 38 V , respectively . The mass spectrometer was operated in single-reflector mode to achieve a resolution of 10 , 000 ( full width at half maximum ) . The mass scale was calibrated using [Glu1]fibrinopeptide B . Peptic peptides were identified based on a map of pepsin-digested undeuterated CT using automated liquid chromatography–tandem mass spectrometry ( LC-MS/MS ) analysis with a Waters NanoAcquity system ( Waters ) . Peptide sequences were identified by individual analysis of collision-induced dissociation ( CID ) spectra using the Waters MassLynx and ProteinLynx software packages ( Waters ) . We analyzed 100 µl of 1 mg/ml A . ventricosus MiSp CT equilibrated 10 min in 20 mM HEPES/MES pH 7 . 5 or 5 . 5 using Sephacryl S-100 ( GE Healthcare ) in the same buffers and at a flow rate of 0 . 5 ml/min . Molecular mass standards aprotenin ( 6 . 5 kDa ) , ribonuclease ( 13 . 7 kDa ) , CA ( 29 kDa ) , ovalbumin ( 43 kDa ) , and conalbumin ( 75 kDa ) were used for calibration .
The spinning process of spider silk is crucial for making webs or other complex constructions to catch spider's prey . The main components of the silk are spidroins , which are large and repetitive proteins that have conserved nonrepetitive terminal domains ( NT and CT ) . Spiders manage both to store the highly aggregation-prone spidroins in solution at extreme concentrations in the silk glands and then to rapidly convert these spidroins into a solid fiber within fractions of a second as they spin fibres . This process has been extensively studied and is thought to involve a pH gradient , but how this pH gradient is generated and maintained was not resolved . Here , we measured the pH at locations along the ampullate gland and determined that the pH decreases to 5 . 7 in the middle of the spinning duct . We also observed that the carbon dioxide pressure is simultaneously increased and that its accumulation may affect the stability of CT . We find that active carbonic anhydrase ( CA ) is crucial to maintain the pH gradient along the gland . Detailed molecular studies of NT and CT under the disparate conditions present along the gland revealed a lock and trigger mechanism whereby in more neutral pH conditions , precocious spidroin aggregation is prevented , and when in more acidic pH conditions , NT dimers firmly interconnect the spidroins and the CT unfolds into β-sheet nuclei that can trigger rapid polymerization of the spidroins . We conclude that this mechanism enables temporal and spatial control of silk formation and may be harnessed in attempts to produce artificial silk replicas .
You are an expert at summarizing long articles. Proceed to summarize the following text: WAGR syndrome is characterized by Wilm’s tumor , aniridia , genitourinary abnormalities and intellectual disabilities . WAGR is caused by a chromosomal deletion that includes the PAX6 , WT1 and PRRG4 genes . PRRG4 is proposed to contribute to the autistic symptoms of WAGR syndrome , but the molecular function of PRRG4 genes remains unknown . The Drosophila commissureless ( comm ) gene encodes a short transmembrane protein characterized by PY motifs , features that are shared by the PRRG4 protein . Comm intercepts the Robo axon guidance receptor in the ER/Golgi and targets Robo for degradation , allowing commissural axons to cross the CNS midline . Expression of human Robo1 in the fly CNS increases midline crossing and this was enhanced by co-expression of PRRG4 , but not CYYR , Shisa or the yeast Rcr genes . In cell culture experiments , PRRG4 could re-localize hRobo1 from the cell surface , suggesting that PRRG4 is a functional homologue of Comm . Comm is required for axon guidance and synapse formation in the fly , so PRRG4 could contribute to the autistic symptoms of WAGR by disturbing either of these processes in the developing human brain . The Commissureless protein ( Comm ) in Drosophila regulates the cell surface expression of Roundabout ( Robo ) axon guidance receptors by targeting Robos for degradation during secretion through the ER/Golgi network [1] , reviewed in [2] . Failure to down-regulate Robo leads to a dramatic phenotype in which axon crossing of the CNS midline is abolished [3] . Conversely , overexpression of comm induces ectopic midline crossing through increased removal of Robos [4–6] . Comm is also required for the correct formation of the Drosophila brain commissure [7] . Comm is a relatively short protein with a single transmembrane domain and L/PPxY motifs [1 , 8] . Comm binds the WW domain containing ubiquitin ligase Nedd4 via L/PPxY motifs [9] , but this function appears only to be required for endocytosis activities at the neuromuscular junction [10 , 11] . Despite the conservation of the Robo/Slit pathway , homologues of Comm have not been found outside of insects and alternative molecules and mechanisms have been proposed for Robo regulation in the vertebrate spinal cord [12–15] . The vertebrate proline rich and Gla domain genes PRRG1-4 , also known as PRGP1 , PRGP2 , TMG3 and TMG4 respectively [16 , 17] , encode short transmembrane proteins . PRRG4 protein has been found in the Golgi apparatus and at the cell surface [18–20] . All PRRG proteins contain a Gla domain in which glutamic acid ( Glu ) residues are γ-carboxylated in the endoplasmic reticulum by γ-glutamyl carboxylase ( GGCX ) [21 , 22] to form γ-carboxyglutamate ( Gla ) residues . Gla domains coordinate calcium ions to allow binding to membrane phospholipids [23] . Although γ-carboxylation plays a major role in blood clotting , the enzymes required for this post-translational modification are also found in invertebrates , which lack the vertebrate blood clotting cascade , suggesting additional functions [24 , 25] . PRRG proteins are expressed highly in tissues such as the spinal cord and so are believed to play roles outside the coagulation cascade [16 , 17] . The cytoplasmic domains of PRRG proteins are characterized by PPxY and LPxY motifs that are best known as acting as ligands for WW domain containing proteins [26 , 27] . The PRRG proteins are therefore members of a family of transmembrane proteins that can recruit additional proteins or vesicles to the membrane via the Gla domain or L/PPxY motifs . WAGR ( Wilm’s tumor , Aniridia , Genitourinary malformations and mental Retardation ) syndrome is a rare genetic disorder caused by haploinsufficiency of the 11p13 chromosomal region [28–30] . The WAGR critical region includes the WT1 and PAX6 transcription factors , which are responsible for the Wilm’s tumor and aniridia phenotypes respectively [31 , 32] . WAGR syndrome is frequently accompanied by developmental delay and autism like features . The genes that could contribute to these symptoms include PAX6 , SLC1A2 , DCDC1 and PRRG4 [33 , 34] . In a survey of 31 WAGR patients with autism , all were deleted for PRRG4 , a correlation that suggested that PRRG4 is involved in autistic symptoms [33] . The critical region for severe developmental delays and autistic behaviors was subsequently narrowed down to 1 . 6Mb that includes PRRG4 , but not SLC1A2 or DCDC1 [35] . Understanding the function of PRRG4 is therefore a key step in determining whether PRRG4 contributes to the autistic behaviors . During literature searches for short transmembrane proteins containing L/PPxY motifs , we noticed similarities between Comm , the Rcr1 and Rcr2 genes in yeast , and the PRRG , CYYR and Shisa families in vertebrates ( Fig 1A ) . We tested representatives of these families for the ability to affect axon guidance in the fly ventral nerve cord . We find that expression of PRRG4 in a sensitized background induces midline crossing . When expressed in COS cells , PRRG4 reduces the surface localization of Robo proteins . Our results place PRRG4 in an evolutionarily conserved gene family that regulate the cellular localization of cell surface proteins . The GLPSYDEAL motif of Comm has been shown to be essential for Comm function in midline crossing [1] , and constitutes an extended version of an L/PPxY motif [36] ( Fig 1B ) . We searched the literature for PY motif proteins from other species and compared their structure to that of Comm . In S . cerevisiae , the Rcr1 and Rcr2 proteins contain PPSY and VPEY motifs and have an overall structure resembling that of Comm . The VPEY motif binds the Rsp5 ubiquitin ligase with PPSY having a cooperative function [37] . This activity is likely required for endocytotic trafficking of yeast membrane proteins [38] . In Drosophila , the Nedd4 ubiquitin ligase binds Comm by either the LPSY or PPCY motifs , but with an in vivo preference for LPSY [9] . The Nedd4 interaction is required for endocytosis at the neuromuscular junction formation [11] , but not the regulation of Robo during midline crossing [10] . In an interesting parallel , Rsp5 is not required for activity of Rcr1 in chitin deposition . These similarities led us to test Rcr1 and Rcr2 for activity in the fly nervous system . Yeast has been used to screen for human genes regulating plasma membrane protein trafficking and CYYR1 gene was identified in this manner [39] . CYYR1 is characterized by a cysteine ( Cys ) rich N-terminal , three conserved Cys residues within the transmembrane domain as seen for Comm2 and other insect proteins , and three PPxY motifs ( Fig 1A ) . CYYR1 appears to be a member of the large Shisa-like protein family ( STMC6 ) , all of which are short single pass transmembrane proteins involved in protein trafficking and degradation [40] . Shisa proteins physically interact with Frizzled and FGF receptors in the ER/Golgi , preventing their maturation and trafficking to the cell surface in Xenopus and mice [41 , 42] . Disruption of these developmentally important pathways could potentially mask subsequent effects on axon guidance . However , Comm proteins lack Cys residues in their extracellular domain so are less likely to be homologues . We tested two divergent members , Xenopus Shisa4 and human CYYR1 to check for the ability to regulate Robo . After testing these genes , we observed that the uncharacterized gene CG15760 is likely the Drosophila homologue of Shisa-like gene family , based on the C*C*CC*CC arrangement of Cys amino acids in the putative extracellular/lumenal domain ( S1 Fig ) [40] . Searching for other PY motif proteins , our attention was drawn to the PRRG proteins , two of which lack signal sequences like Comm . All have PPxY and LPxY motifs in their cytoplasmic domains , with PRRG4 having an exceptional match to the critical Comm GLPSYDEAL motif: GLPSYEQAV , when conservative substitutions for the negatively charged and hydrophobic amino acids are taken into account ( Fig 1B ) . The human PRRG2 LPxY sequence closely matches that of Comm homologues from the housefly and the Mediterranean fruit fly . The PPxY motif comes after the LPxY motif in these genes , and an SH3 binding motif is also present in PRRG2 and PRRG4 [17 , 18] . Finally , we noticed an uncharacterized C . elegans gene C17G10 . 7 with two PPxY motifs , one with acidic residues following the tyrosine ( S2 Fig ) . However , an alternative alignment for the predicted C17G10 . 7 protein has four putative transmembrane domains so may align with LAPTM4 proteins instead ( S2 Fig ) [39] . Nevertheless , the predicted protein had additional homologies at the N- and C- termini that led to it being included in testing . As noted in the introduction , PRRG proteins contain an N-terminal Gla domain consisting of Glu residues that are γ-carboxylated by GGCX . The GGCX and VKOR enzymes required for γ-carboxylation are present and functional in flies , but surprisingly GGCX knockouts have no apparent phenotypic defects [43–45] . Gla domains contain a propeptide sequence bound by GGCX , a hydrophobic region called the “keel” or ω-loop that binds phospholipids giving Gla domains membrane binding properties [46] , and a highly conserved region of Glu and Cys residues that coordinate calcium ions . The activity of GGCX on its substrates is greatly enhanced by the presence of a propeptide sequence that is proteolytically removed after GGCX has moved along the protein [47 , 48] . The propeptide consensus consists of a highly conserved phenyalanine residue at -16 , an alanine at -10 and a leucine at position -6 relative to the proteolytic cleavage site , as well as additional conserved hydrophobic amino acids [49 , 50] . An N-terminal motif , ITFEIP , conserved among Comm proteins is centered on a Phe residue and is followed by Ala and Leu residues only slight offset from the vertebrate consensus , suggesting this region could function as a propeptide ( Fig 2B ) . GGCX functions in a processive manner and usually begins modifying Glu residues immediately downstream of the propeptide , which frequently occur within the keel or ω-loop . The sequence FLEEL in PRRG3 represents this initial substrate and is identical to a sequence frequently used to measure GGCX activity and the influence of the propeptide [51] . Comm proteins show distant homology to this initial substrate , although the Glu residues are missing ( Fig 2B ) . The keel region may insert directly into the membrane being bound by the Gla domain , so the hydrophobic residues are likely the most important [52 , 53] . Deletion of this region of Comm greatly reduces Comm function in vivo indicating its importance [54] . The remainder of the Gla domain coordinates calcium ions via the Gla residues . In Comm , a short sequence adjacent to the transmembrane domain is essential to Comm activity ( labeled the “sorting sequence” in Fig 2B ) [10] . As before there is weak homology to the Gla domain ( Fig 2C ) , with the proposed ω-loop and the rest of the domain physically separated in Comm . Given the distant homologies to Gla domains in Comm , as well as the conservation of the LPxY motif , we tested PRRG1-4 genes in the fly nervous system . The open reading frames of the selected genes ( S . cerevisiae Rcr1 , Rcr2 , C . elegans C17G10 . 7 , X . laevis Shisa4 , and Mus musculus CYYR1 and PRRG1-4 ) were synthesized with a Drosophila codon bias . All open reading frames had a myc epitope tag added at the carboxy-terminus , were subcloned into the pUAST expression vector and used to generate transgenic fly lines . The lines were tested by pan-neural expression using the scabrous-GAL4 ( sca-GAL4 ) driver and staining for the myc epitope to confirm expression . Comm protein is found in cell bodies , cytoplasmic vesicles and axons [8] , and we expected that a candidate homologue might show the same pattern . As yeast lacks a nervous system , we did not expect to see axonal localization of Rcr1 or Rcr2 . However , we found that by stage 16 of embryonic development yeast Rcr1 and to a lesser extent , Rcr2 , localized to longitudinal axons in a manner reminiscent of Robo1 protein ( Fig 3B and 3C ) . This raises the possibility that the Rcr proteins may be weakly interacting with Robo proteins . In contrast , the vertebrate PRRG4 protein remained in the neuronal cell bodies ( Fig 3D ) . As trafficking and cell surface localization of Gla domain proteins can be dependent on γ-carboxylation [55 , 56] , it is possible that the fly GGCX enzyme does not properly process PRRG4 . Pan-neuronal over-expression of comm in the fly CNS induces ectopic midline crossing that phenocopies robo mutants because Robo proteins are downregulated by excess Comm [4–6] . In our hands , CNS axon guidance phenotypes require multiple copies of the sca-GAL4 driver and the UAS-comm transgene . We screened several independent UAS transgene insertions for each candidate Comm homologue by crossing to sca-GAL4 , recovering the F1 generation and examining the embryos laid . This allowed us to rapidly generate large numbers of embryos potentially carrying more than one copy of the sca-GAL4 driver and/or the UAS transgene . Staining of the CNS axon scaffold revealed no mis-expression phenotypes for the CYYR1 , xShisa4 , C17G10 . 7 , Rcr1 , PRRG1 , PRRG2 and PRRG3 genes . Rcr2 expression resulted in very minor aberrations in the axon scaffold in a very low percentage of embryos . PRRG4 expression had very rare and subtle phenotypes ( Fig 4B ) , but still stood out from the other transgenes for having a noticeable effect . Very rarely stronger effects ranging from increased midline crossing in single segments to missing commissures were observed . The latter phenotype suggests that PRRG4 might act as a dominant negative . We repeated the PRRG4 experiments with the scratch-GAL4 promoter , which has a similar expression pattern as sca-GAL4 , but may express for longer , but saw no increase in phenotypes . The low frequency of PRRG4 phenotypes suggested that two copies of the GAL4 and UAS transgenes are required to obtain phenotypes . Increasing expression of the PRRG4 transgene beyond two copies would have been challenging , so we sought out alternative approaches to increase the phenotypic penetrance of PRRG4 expression . We were concerned that interactions between Robo and Comm might be species specific , as Comm has no effect on zebrafish Robo1 or Robo3 localization in S2 cells [57] . Expressing human Robo1 ( hRobo1 ) in the ventral nerve cord subtly increases midline crossing ( Fig 4C , Table 1 ) . This is in contrast to fly robo1 over-expression , which leads to a commissureless phenotype [58] . Fly and vertebrate Robo proteins can dimerize via their cytoplasmic and extracellular domains [6 , 59 , 60] , and also form heterodimeric complexes with other receptors bridged by Slit [61] . This suggests that hRobo1 may be acting as a dominant negative , interfering with the function of endogenous Robos perhaps by creating inactive heterodimers . Co-expression of PRRG4 with hRobo1 strongly enhanced the midline crossing phenotype ( Fig 4D , Table 1 , S1 Data ) . The interaction of the γ-carboxylated PRRG4 protein and hRobo1 suggested that γ–carboxylation might be important for fly nervous system formation . We examined the nerve cords of mutants for the γ-glutamyl carboxylase ( GC ) gene , but found no defects in the axon scaffold ( Fig 4E ) . In these embryos co-expressing PRRG4 and hRobo1 , fly Robo1 protein can be found in the commissures ( Fig 4F and 4H ) . A similar mislocalization is seen in comm gain of function embryos [4] . We examined the protein localization of hRobo1 when expressed in the fly ventral nerve cord and found it present in the commissures suggesting it is not regulated by fly Comm ( Fig 4J ) . Of the candidate genes tested , PRRG4 was the strongest candidate for a Robo regulator identified in these tests . To further investigate the potential PRRG4-Robo interaction , we co-expressed constructs in COS cells and looked for co-localization . We began by testing Comm and rat Robo1 ( rRobo1 ) . Robo proteins localize to the cell surface , whereas Comm is primarily in the ER/Golgi ( Fig 5A ) [1 , 8] . Some co-localization occurs but may be because both proteins are in the secretory pathway . The clearance of dRobo1 from the cell surface of COS cells has been used as an assay for Comm function [1 , 62] , but we saw no evidence that rRobo1 is cleared from the cell surface by Comm suggesting that these two proteins do not interact . Similarly , co-expression of PRRG4 and fly Robo1 ( dRobo1 ) showed limited co-localization and no re-localization of dRobo1 from the cell surface ( Fig 5B ) . Taken together with previous results showing no interaction between Comm and zebrafish Robo1 and Robo3 in S2 cells [57] and our results showing no localization of hRobo1 in the fly ventral nerve cord ( Fig 4J ) , this suggests that interactions between Robo and Comm/PRRG genes have co-evolved since insects and vertebrates split . We tested all four mouse PRRG proteins for co-localization with rRobo1 . PRRG1 and PRRG2 showed minimal or no co-localization with rRobo1 , and rRobo1 did not appear to re-localize from the cell surface ( Fig 5C and 5D ) , suggesting these proteins do not interact . We obtained mixed results with PRRG3 as we saw partial co-localization with rRobo1 , but also clear separation of staining ( Fig 5E ) . rRobo1 also appeared to be partially cleared from the cell surface ( Fig 5E” ) , and these results may be interpreted as a weak interaction between PRRG3 and rRobo1 . We have included additional examples of co-localization to document this effect ( S3 Fig ) . PRRG4 showed a strong co-localization with rRobo1 , clearing rRobo1 from the cell surface and co-localizing in the presumed ER/Golgi adjacent to the cell nucleus ( Fig 5F ) . This result strongly resembles that of Comm and dRobo1 , suggesting that PRGR4 and rRobo1 interact in cell culture . To verify the co-localization results , we chose to test the PRRG proteins' ability to clear rRobo1 from the cell surface in a blinded experiment in which COS cells were co-transfected with both genes of interest but the experimenter responsible for scoring only observed the dRobo1/rRobo1 staining . Comm and dRobo1 served as a positive control and Comm re-localized dRobo1 from the cell surface with 100% efficiency when scored blind ( Fig 6A–6C; S2 Data ) . When rRobo1 was co-expressed with the PRRG genes , PRRG4 prevented cell surface localization of rRobo1 or showed increased rRobo1 localization in the ER/Golgi over 80% of the time ( p < 0 . 0001 , two tailed Fisher’s exact test; Fig 6G; S2 Data ) . None of the other PRRG genes had a statistically significant effect on rRobo1 localization , although PRRG3 trended towards statistical significance , ( p = 0 . 0538 , cutoff value is p < 0 . 0125 , Fig 6F ) , consistent with the mixed results obtained in the co-localization assay . A dosage sensitive relationship between Comm and Robo has previously been demonstrated in cell culture , with increasing amounts of Robo plasmid leading to less Comm protein detectable by immunoblot [62] . We modified this assay to verify the PRRG4 result and found that increasing amounts of PRRG4 expression reduced rRobo1 levels as detected by immunoblot ( Fig 7; S3 Data ) . We used the related immunoglobulin family member hDscam as a control and found negligible downregulation in the presence of PRRG4 . 250ng of PRRG4 plasmid per well ( 9 . 5cm2 ) produced a very reliable down-regulation of rRobo1 compared to hDscam ( p = 0 . 00002 , one-way ANOVA , Fisher LSD test ) . Together these results indicate that PRRG4 downregulates Robo in COS-7 cells in a manner analogous to Comm . The PRRG4 gene has been implicated in the autistic features of WAGR syndrome . Our work suggests that PRRG4 is a functional homologue of the Drosophila commissureless gene and may regulate the cell surface localization of the Robo guidance receptors and other molecules during human brain development . How could haploinsufficiency for PRRG4 lead to autistic symptoms ? The simplest explanation is that reduction in PRRG4 levels alters connectivity patterns in the developing brain due to increased Robo levels . Connectivity defects have been suggested as potentially underlying some cases of autism [63 , 64] . Robos have been implicated in autism through single nucleotide polymorphism and expression studies [65–67] . It has been proposed that Robo gene variants are interfering with the serotonergic system , the anterior cingulate cortex or through a general effect on neurodevelopment . Additionally , alterations to the corpus callosum have also been implicated in autistic symptoms [68] , and Robo/Slit signaling is required for corpus callosum formation [69 , 70] . Robo /Slit signaling has been implicated in all aspects of neural development , not just axon guidance [71] , so it is unclear at what stage of development PRRG4 function might be required . There is little information on the expression pattern of PRRG4 in the embryo , with the exception of Xenopus embryos in which expression appears quite broad and likely to include the CNS [72] . PRRG4 expression has been observed in Purkinje cells in the human cerebellum [19] , neurons known to be important in autism models [73] . Embryonic comm expression is highly dynamic in the fly [1 , 8] , so thorough surveys of PRRG4 expression will be required to identify candidate regions for further analysis . In parallel , the development of knockout mice may also help identify affected brain areas . Identification of a PRRG gene that is expressed in spinal cord commissural neurons during axon crossing of the CNS midline would also establish whether the most well-known function of comm is conserved . Comm is also required for the formation of Drosophila neuromuscular synapses , and is proposed to clear molecules from the cell surface to allow synaptogenesis to take place [11 , 74 , 75] . As autism appears to primarily be a synaptic disorder [76] , haploinsufficiency for PRRG4 may disrupt synapse formation in WAGR syndrome . The synaptic function of Comm in flies has not been linked to regulation of Robo and likely involves unidentified molecules . The ubiquitin ligase Nedd4 is important for the synaptogenesis function and PRRG4 also binds Nedd4 proteins [18] . Additional proteins that interact with the PY motifs of PRRG4 have been observed , including the MAGI proteins , which are required for learning and memory [18] . PRRG4 could function as an adaptor protein regulating molecules acting at the synapse . Our findings suggest that Comm may be γ-carboxylated and that γ-carboxylation could have arisen as a nervous system post-translational modification that was later co-opted for blood clotting . Surprisingly , an absence of γ-carboxylation leads to no phenotypic defects in the fly ( Fig 4E ) [44] , and we have observed no effects of warfarin on embryonic development . If Comm is γ-carboxylated , then this modification is not required for embryonic function . In commissural neurons , Comm sorts Robo into vesicles destined for late endosomes and the lysosome [10] . Sorting may not require γ-carboxylation of Comm , or alternatively the putative Gla domain may have additional functions . We favor a model in which the cell surface localization of Comm/PRRG proteins will require γ-carboxylation and whereas trafficking from trans Golgi network to the lysosome will not . Comm and PRRG proteins have been studied independently up to this point . The existence of molecular and genetic datasets for both genes will aid future experiments into the functions of these protein families . For example , the LPSY motif that binds Nedd4 is also required for Comm function in midline crossing . Additional binding partners for the LPSY motif have been identified [18] , and these can be tested for functions in the fly CNS . Similarly , studies of comm in Drosophila and other species can guide expectations of PRRG4 function in WAGR syndrome [77 , 78] . We were surprised to find that PRRG3 did not interact with rRobo1 at a statistically significant level in the cell clearance assay as we observed partial co-localization ( Fig 5E ) . PRRG3 may be able to regulate Robo proteins in the exocytosis pathway , but less efficiently in endocytosis and will deserve further investigation . Interestingly , PRRG3 and PRRG4 both share a conserved cysteine in the transmembrane domain with insect Comm homologues ( Fig 2C , highlighted in red and blue ) , whereas PRRG1 and PRRG2 do not . Our results suggest that Comm/PRRG proteins are part of an ancient family of cell surface protein regulators that originated in single celled eukaryotes and that a subset of WAGR syndrome symptoms are likely due to increased levels of cell surface proteins in axons or synapses . The coding sequences of candidate genes Rcr1 ( NM_001178353 ) , Rcr2 ( NM_001180311 ) , C17G10 . 7 ( NM_062689 ) , CYYR1 ( AF442733 ) , xShisa4 ( NM_001096205 ) , PRRG1 ( NM_027322 ) , PRRG2 ( NM_022999 ) , PRRG3 ( BC137616 ) and PRRG4 ( NM_178695 ) were synthesized with codon optimization for expression in Drosophila by Genscript . A C-terminal Myc epitope tag was added to each sequence and genes were delivered in pUC-57 with 5’ and 3’ restriction sites added to facilitate cloning into pUAST . Rcr1 , Rcr2 , CYYR1 , xShisa4 and C17G10 . 7 coding sequences were subcloned into pUAST with EcoRI and XbaI . The PRRG1-4 coding sequences were inserted as EcoRI-KpnI fragments . Drosophila injections were performed by Genetic Services Inc . or Rainbow Transgenics and transformants were selected and insertions mapped using standard methods . For construction of UAS-hRobo1 , the human Robo1 clone described in Kidd et al . 1998 ( Genbank #AF040990 ) was modified by PCR to change the stop codon to leucine ( TGA to TTA ) , thereby introducing a HindIII site at the carboxy terminus of the protein . The original intention to insert an epitope tag appears to have failed . The hRobo1 gene was subcloned into the pUAST vector as an XbaI-HindIII fragment and used to transform Drosophila by standard techniques . scabrous-Gal4 and scratch-Gal4 were obtained from the Bloomington Drosophila Stock Center . Drosophila embryos were processed and immunostained as previously described [79] . The following antibodies were used: mouse anti-c-Myc 9E10 ( Santa Cruz ) 1:200 , BP102 ( DSHB ) 1:10 , mouse monoclonal antibody 13C9 against fly Robo1 ( DSHB ) 1:20 , rabbit anti-hRobo1 ( Abcam ab7279 ) 1:1000 . Anti-mouse ( 1:500 ) and rabbit ( 1:1000 ) HRP- conjugated secondary antibodies were obtained from Jackson Laboratories . For phenotypic comparisons , transgene presence was confirmed by immunostaining . C-terminal myc-tagged Rcr1 , Rcr2 , PRRG1 , PRRG2 , PRRG3 and PRRG4 synthetic sequences were subcloned into pcDNA3 . 1 ( Life Technologies ) for expression in mammalian cells . Drosophila Robo1 in pcDNA is described in [80] . HA-tagged rat Robo1 in pCS2+ was a gift from Yi Rao ( National Institute of Biological Sciences , Beijing University ) to Grant Mastick ( University of Nevada , Reno ) . GFP-Comm in pcDNA was provided by Daniela Rotin ( Peter Gilgan Centre for Research and Learning , Toronto ) . Myc-tagged human Dscam in pcDNA was a gift from K . -L . Guan ( Pharmacology , UCSD ) . COS-7 cells were transfected using Lipofectamine 3000 ( Life Technologies ) and analyzed 48 hours post-transfection for all cell culture experiments . To assay re-localization of Robo in response to Comm/PRRG proteins , 500 ng of Robo plasmid alone or with 250ng candidate gene plasmid were added to each well of a six well plate . For immunocytochemistry , cells were washed with PBS then fixed in 4% PFA . Cells were blocked in 5% NGS for 30 minutes prior to antibody labeling . Antibodies used for immunocytochemistry were mouse anti-c-Myc 9E10 ( Santa Cruz ) 1:200 , rabbit anti-HA ( Covance ) 1:250 , mouse monoclonal antibody 13C9 against fly Robo1 ( DSHB ) 1:20 . Secondary detection used Alexa Fluor anti-rabbit 488 and anti-mouse 568 ( Jackson Laboratories ) . To assay total levels of rRobo1 and hDscam protein in the presence of PRRG4 , 500ng of rRobo1 or hDscam plasmid alone and with increasing amounts of PRRG4 plasmid were transfected per well of six well plates , as described in [62] . After 48 hours cells were harvested and lysed in ice cold lysis buffer containing 50mM HEPES ( pH 7 . 2 ) , 100mM NaCl , 1mM MgCl2 , 1mM CaCl2 and 1% NP-40 with protease inhibitors [81] . Total protein content was normalized using a BCA Protein Assay Kit ( Thermoscientific , Pierce ) . Protein was separated on a 4–20% gradient SDS-PAGE gel and electroblotted to nitrocellulose membrane ( Bio-Rad ) . Membranes were blocked in 5% milk with 0 . 1% Tween 20 and subsequently incubated with monoclonal antibody 13C9 ( DSHB , 1:20 ) , rabbit anti-HA ( Covance , 1:1000 ) or anti-C-Myc ( Santa Cruz , 9E10 1:250 ) ( to confirm increasing levels of PRRG4 protein ) . Proteins were detected using HRP-conjugated secondary antibodies ( Jackson Laboratories , 1:5000 ) and visualized with ECL detection reagents in a ChemiDoc imager ( Bio-Rad ) . Signal intensities were measured in ImageJ .
Mutants for the fruit fly commmissureless gene ( comm ) dramatically lack connections between the left and right hand sides of the nervous system . This is due to a failure to prevent Robo receptors from reaching the cell surface , where they guide growing axons away from the CNS midline . Comm proteins are not thought to exist outside of insects . By carefully comparing proteins from other species , candidate homologues from vertebrates and yeast were identified . The candidates were tested by expression in the fly nervous system and one gene , PRRG4 , was found to affect the phenotype caused by expression of the human Robo1 gene . When Robo genes are expressed in cell culture , they localize to the surface of the cell . PRRG4 was found to be able to re-localize Robo away from the cell surface , a property shared with Comm protein , indicating that they are functional homologues . Human patients with WAGR syndrome often display autistic features and these have been attributed to loss of one copy of PRRG4 . Our findings suggest that PRRG4 guides growing axons and that brain wiring patterns may be subtly altered in WAGR patients .
You are an expert at summarizing long articles. Proceed to summarize the following text: The modification of DNA by methylation is an important epigenetic mechanism that affects the spatial and temporal regulation of gene expression . Methylation patterns have been described in many contexts within and across a range of species . However , the extent to which changes in methylation might underlie inter-species differences in gene regulation , in particular between humans and other primates , has not yet been studied . To this end , we studied DNA methylation patterns in livers , hearts , and kidneys from multiple humans and chimpanzees , using tissue samples for which genome-wide gene expression data were also available . Using the multi-species gene expression and methylation data for 7 , 723 genes , we were able to study the role of promoter DNA methylation in the evolution of gene regulation across tissues and species . We found that inter-tissue methylation patterns are often conserved between humans and chimpanzees . However , we also found a large number of gene expression differences between species that might be explained , at least in part , by corresponding differences in methylation levels . In particular , we estimate that , in the tissues we studied , inter-species differences in promoter methylation might underlie as much as 12%–18% of differences in gene expression levels between humans and chimpanzees . Changes in the regulation of gene expression levels have long been hypothesized to play an important role in primate evolution [1] , [2] . To begin to address this hypothesis , a large number of studies have characterized gene expression differences across primates , in particular between humans and chimpanzees [3]–[9] . These studies have pointed to several classes of biological processes ( such as transcriptional regulation , oxidative stress response , and a number of metabolic pathways ) , which might have evolved under natural selection in primates . In addition , in a few cases , comparative studies in primates have been able to draw strong connections between regulatory adaptations and ultimate physiological or anatomical phenotypes [10]–[15] . Despite the wealth of comparative gene expression data , there are many fewer studies of the mechanisms that underlie inter-primate differences in gene regulation ( e . g . , [12] , [13] , [16]–[18] ) . In particular , we know relatively little about the degree to which changes in epigenetic profiles might explain differences in gene expression levels between primates . One of the most extensively studied epigenetic mechanisms is DNA methylation – an epigenetic modification that facilitates fine-tuned regulation of transcription rates [19] , [20] . Spatial and temporal regulation of transcription by DNA methylation has been shown to play an important role in many contexts , including in female X-chromosome inactivation [21] , [22] , genomic imprinting [23] , [24] , and susceptibility to complex diseases in humans , especially cancers [25] , [26] . Methylation is also essential for proper differentiation and development of mammalian tissues [27] , [28] . For instance , the knockout of genes encoding for the DNA-methyl-transferase ( DNMT ) enzymes , which are responsible for de-novo methylation of DNA , results in embryonic lethality in mice [29] , [30] . The causal relationship between changes in promoter DNA methylation and differences in gene regulation has been well established [28] , [31] . It has been shown that hyper-methylation at promoter CpG islands typically results in decreased transcription of downstream genes [32] . When methylation is experimentally removed from promoter regions , transcription levels rise [33] . The specific mechanisms by which DNA methylation affects gene regulation are less clear , though DNA methylation is thought to interact with proteins ( such as methyl-DNA binding proteins ) that associate with histone modifications or the nucleosome in order to maintain a silenced chromatin state [28] , [31] , [34] , [35] . Additionally , it has been proposed that the binding of the transcriptional machinery and enhancer-related transcription factors to methylated genomic regions is less frequent , resulting in decreased transcription levels or absolute gene silencing [28] , [36] . Previous studies have typically described patterns of DNA methylation in a single or few tissues across species [26] , [37]–[41] or in multiple tissues or developmental stages within a single organism [26] , [27] , [34] , [42]–[45] . Comparative studies of DNA methylation across mammals have suggested that the role of DNA methylation in tissue-specific gene regulation is generally conserved . For example , after identifying Tissue-specific Differentially Methylated Regions ( T-DMRs [42] ) , in heart , colon , kidney , testis , spleen , and muscle tissues in mice , Kitamura and colleagues were able to use the methylation status in orthologous human regions to distinguish between the corresponding human tissues [44] . Irizarry and colleagues [26] , who studied genome-wide DNA methylation patterns in spleen , liver , and brain tissues from human and mouse , reported that 51% of T-DMRs are shared across both species . However , there also are a large number of potentially functional differences in methylation levels across species . In particular , in primates , Gama-Sosa and colleagues [39] found that relative methylation levels within tissues generally differ between species , with the exception of hyper-methylation in the brain and thymus , which were observed regardless of species . In addition , Enard and colleagues [38] , who compared methylation profiles of 36 genes in livers , brains , and lymphocytes from humans and chimpanzees , reported significant inter-species methylation level differences in 22 of the 36 genes , in at least one tissue . With few exceptions , however ( e . g . , [46] ) , comparative studies in primates have not explored the extent to which methylation differences between species might contribute to the genome-wide regulation of inter-species differences in gene expression levels . Towards this goal , we compared genome-wide gene expression levels and DNA methylation data in tissue samples from humans and chimpanzees . We obtained methylation profiles from each sample ( using two independent DNA extraction replicates ) by using the Illumina HumanMethylation27 DNA Analysis BeadChip assay , which provides reproducible ( Figure S2 ) quantitative estimates of methylation levels at 27 , 578 CpG-loci near transcription start sites . Since the 50 bp probes on the Illumina array were designed to interrogate human samples , we limited our analysis to probes that were a perfect sequence match to the chimpanzee genome . In addition , we only used probes that were associated with genes for which we had expression measurements across the three tissues [8] . Following these exclusion criteria , we retained 10 , 575 CpG site probes in the putative promoter regions of 7 , 723 genes ( see Methods for more details ) . At each probe , DNA methylation levels were estimated using the Illumina-recommended β values , which are essentially estimates of the proportion of methylated DNA at each CpG site ( see Methods ) . We note that limiting our analysis to identical methylation probes in humans and chimpanzees resulted in a slight ( 0 . 5% ) but significant decrease of the median sequence divergence estimates within 500 bp windows around the retained probes ( Figure S3 ) . As a result , it is possible that , in what follows , we slightly underestimate the proportion of inter-species differences in methylation levels . However , we confirmed that limiting our analysis to identical methylation probes in the two species did not result in a noticeable shift in the distribution of expression levels of the associated genes , nor in the proportion of observed differences in gene expression levels between the two species . As a first step of our analysis , we examined patterns of promoter methylation across tissues and species . As expected [28] , [31] , we found a negative correlation between methylation and gene expression levels in each individual , whereby , regardless of tissue and species , the promoters of highly expressed genes tended to be lowly methylated while the promoters of lowly expressed genes were usually highly methylated ( Figure 1A; Figure S4 ) . We also confirmed that methylation patterns on the X-chromosome account for variation due to sex , regardless of species , as expected due to X-inactivation in mammalian females [21] ( the first component of variance , corresponding to sex , accounts for 67% of the overall variation in the X-chromosome data; Figure 1B ) . Finally , we found that genes known to be imprinted in humans tend to show a similar hemi-methylation pattern in chimpanzees ( permutation tests P<0 . 001; Figure 1C ) , suggesting that the imprinted status of this set of genes is conserved in the two species . For the remainder of the analyses , we considered only the methylation data from autosomal probes . We observed that methylation patterns across different tissues and species were quite distinct ( Figure 2; similar patterns for the expression data in Figure S5 ) . The first component of variance for the autosomal probes , accounting for 69 . 3% of the overall variation in methylation , distinguished samples based on tissue , while the second principal component ( accounting for 12 . 7% of the overall variation ) , separated the species . Overall , an average of 14 . 5% ( range of 8 . 2–26 . 1% , depending on the pairwise comparison ) of the assayed promoter CpG sites were differentially methylated between tissues within a species , while an average of 8 . 6% of the CpG sites ( range of 3 . 4–13 . 5% , depending on the tissue ) were differentially methylated between humans and chimpanzees ( at FDR<0 . 001 ) . Reassuringly , these patterns recapitulate previous observations in human and mouse [26] , [44] . We identified regions with tissue-specific patterns of methylation ( T-DMRs [26] , [42] ) by analyzing the data from each species separately ( Figure 3 ) . Specifically , we modeled the methylation data ( namely , the β values ) from each autosomal CpG site independently , using a linear mixed-effects model with a fixed effect for the tissue and a random effect to account for variation between individuals . We tested for differences in methylation levels between tissues by using likelihood ratio tests within the framework of the linear model ( see Methods ) . Using this approach , we identified 1 , 578 and 1 , 401 T-DMRs in humans and chimpanzees , respectively ( at an FDR<0 . 001; Figure 3A; Table S1 ) . Tissue-specific methylation profiles are of interest because they may underlie tissue-specific patterns of gene expression levels . To test this hypothesis , we calculated , separately for each species , Pearson correlation values between promoter methylation profiles and the corresponding gene expression levels , across the three tissues . If methylation was consistently used to silence tissue-specific gene expression across the genome , we would expect to observe an abundance of negative correlations between the estimates of methylation and gene expression levels . However , when we considered the data for all genes that were expressed in at least one tissue , we found no evidence for an enrichment of negative correlations between methylation and gene expression levels ( Figure 3B , Figure S6; 48% and 49% of the correlation values were negative in human and chimpanzee , respectively ) . In contrast , when we restricted the analysis to species-specific T-DMRs , we found an enrichment of negative correlations between methylation and gene expression levels ( Figure 3B; 64% and 67% of correlation values were negative in human and chimpanzee , respectively; Fisher's exact P<10−16 ) . This result suggests that T-DMRs underlie a subset of gene expression differences across tissues , a notion that is consistent with the important role played by DNA methylation in tissue differentiation in a wide range of species [42] . We then focused on the subset of T-DMRs with the same methylation pattern in both species . We found that 18–26% ( depending on the tissue ) of loci classified as T-DMRs in either human or chimpanzee are shared between the two species ( Figure 3A , Table S2 ) , a highly significant overlap compared to that expected by chance alone ( hypergeometric distribution P values across all pairwise tissue comparisons <10−16 ) . Importantly , the observation of a significant overlap in T-DMRs across species is robust with respect to the statistical cutoff used to classify T-DMRs ( 0 . 001≤FDR≤0 . 05; Table S2 ) . Interestingly , when we considered correlations of methylation and gene expression levels only at conserved T-DMRs , we found an even more pronounced enrichment of negative correlations ( Figure 3B and 3C; 72% of the correlation values were negative , regardless of species; Fisher's exact for an enrichment of negative correlations: P<10−23 ) , suggesting that conservation of T-DMRs often relates to functionally important tissue-specific patterns of gene regulation . It is perhaps interesting to note that we did not find a difference in the correlation of methylation and expression levels between T-DMR CpG sites that are located within or outside an annotated CpG island ( as defined by [47]; Figure S7 ) . When we examined the functional annotations of genes associated with species-specific T-DMRs as well as conserved T-DMRs ( using gene ontology annotations ) , we found an expected enrichment of genes annotated as important in ‘developmental’ processes , regardless of tissue ( P<5×10−3; FDR<0 . 3; Table S3 ) , congruent with the importance of epigenetic modification in tissue differentiation . We also found enrichments of tissue-specific biological processes , such as genes associated with cardiac muscle cell differentiation processes among heart T-DMRs ( P<5×10−3; FDR<0 . 3 ) , genes associated with embryonic organ morphogenesis and embryonic organ development processes among kidney T-DMRs ( P<5×10−4; FDR<0 . 05 ) , and genes associated with blood coagulation and with the regulation of body fluid levels ( putatively involved in homeostatic functions ) among liver T-DMRs ( P<10−5; FDR<6×10−3 and P<10−4; FDR<0 . 007 , respectively ) . The enrichment of genes associated with both developmental and tissue-specific processes among genes associated with T-DMRs is consistent with previous observations [27] , [42] . Furthermore , when we considered only conserved T-DMRs , we observed a significant under-representation of genes associated with nucleic-acid and primary metabolic processes in all three tissues studied ( all P<5×10−3; FDR<0 . 01; Table S4 ) . This result suggests that the epigenetically-mediated tissue-specific regulation of these core processes tends to be conserved between humans and chimpanzees . We next focused on the relationships between inter-species differences in methylation profiles and differences in gene expression levels between humans and chimpanzees . To estimate the relative contribution of changes in DNA methylation to inter-species differences in gene expression levels , we used linear regression analysis to account for promoter methylation effects ( per autosomal CpG site ) before analyzing the gene expression data from both species . We analyzed methylation and gene expression data in each tissue using a linear model framework similar to the one described in Blekhman et al . 2008 [8] . We then compared the evidence supporting an inter-species difference in gene expression levels before and after correcting for methylation profiles ( see Methods for more details ) . For the majority of genes ( 78% , 82% , and 77% in liver , kidney , and heart , respectively; Figure 4A ) , the evidence for a difference in expression level between the species was similar , regardless of whether or not methylation status was taken into account . For a small subset of genes ( 1% , 3% , and 2% in liver , kidney , and heart , respectively ) , we did not find compelling evidence for a difference in expression level between the species using the uncorrected expression level data , but after correcting for methylation levels using regression analysis , we rejected the null hypothesis of no inter-species differences in gene expression level ( at an FDR<0 . 01 ) . This observation , however , is unlikely to be biologically meaningful , since it is expected by chance alone ( by permutation analysis; P>0 . 434 for all tissues; Figure S8 ) . In contrast , in all three tissues , we found a significant enrichment of genes for which the evidence for inter-species differences in expression level was compelling ( FDR<0 . 01 ) before , but not after we corrected for the methylation levels ( 21% , 15% , and 21% in liver , kidney , and heart , respectively , permutation analysis yields P<0 . 001 for all tissues; Figure 4B and 4C ) . Based on the expectation of such a pattern by chance alone ( by permutations – see Methods for details ) , we estimated that , in the three tissues we studied , inter-species differences in promoter DNA methylation might underlie as much as 12–18% of differences in gene expression levels between humans and chimpanzees . When we analyzed the data considering only the sets of genes that have negative correlations between methylation and gene expression levels ( as expected if methylation is used to silence gene expression ) , we found that 8 . 1% , 7 . 6% , and 8 . 8% of interspecies differences in gene expression levels in liver , kidney , and heart , respectively , might be explained by corresponding methylation differences . The extent to which inter-species gene expression differences might be explained by methylation differences between the species was similar regardless of whether the methylated site was within or outside an annotated CpG islands ( Figure S9 ) . We found a substantial degree of conservation of tissue-specific methylated regions in human and chimpanzee . This observation is not surprising given that previous studies found a marked conservation of T-DMRs between human and mouse , which are much more distantly related [26] , [41] , [43] , [44] . On the other hand , 7 . 0% , 21 . 6% , and 23 . 8% of the kidney , heart , and liver T-DMRs , respectively ( identified in either species ) , were differentially methylated ( in the relevant tissue ) between humans and chimpanzees , while only 3 . 3% , 8 . 0% , and 11 . 8% of non-TDMRs in these three tissues were differentially methylated between the two species ( P<10−10 for all pairwise comparisons ) . The conservation of T-DMR profiles yet the generally faster rate of inter-species change in promoter methylation at T-DMRs compared to non-T-DMRs are intriguing . These observations are difficult to explain by technical or uncontrolled aspects of the study design , because it is unlikely that those confounding factors would affect methylation at T-DMRs differently than at non-T-DMRs . Instead , it is likely that the different patterns truly reflect a functional difference between methylation at T-DMRs and at non-T-DMR CpG sites ( in the studied tissues ) . Though there is substantial evidence that DNA methylation levels upstream of genes are often inversely correlated with gene expression levels [24] , [28] , [31] , recent studies proposed that methylation of promoters may play only a relatively minor role in the regulation of tissue-specific gene expression [34] . In particular , Maunakea et al . [48] posited that methylation of gene body regions ( in regions that putatively serve as alternative promoters ) might have a greater influence on regulatory differences across tissues . While we cannot use our data to ask about the relative importance of different types and locations of epigenetic marks to tissue-specific gene regulation , our observations strongly imply that any such debate would benefit from further investigation into the evolution of epigenetic profiles . Indeed , in addition to a faster rate of evolutionary change of the methylation profiles in T-DMRs , we found evidence for an enrichment of inverse correlations between inter-tissue gene expression patterns and promoter methylation profiles at genes associated with T-DMRs , but not when we considered all genes ( the latter observation is consistent with the findings of Weber et al . [34] and Maunakea et al . [48] ) . Our results , therefore , imply that tissue-specific promoter methylation patterns may play especially important roles in regulating gene expression . The data also suggest that altered methylation levels , primarily at these sites , may underlie regulatory differences between species . We estimated that as much as 12–18% ( depending on the tissue ) of inter-species differences in gene expression levels might be explained , at least in part , by changes in DNA methylation patterns . It is important to note that this statement is based on the proposed mechanism by which DNA methylation affects the rate of transcription and overall levels of gene expression [28] , [31] . Though we did not perform experiments from which causality can be directly deduced , a causal relationship between changes in DNA methylation and gene regulation is strongly supported by previous studies ( e . g . , [24] , [28] , [31] ) . When we only consider negative correlations between methylation and gene expression levels to be indicative of a putative causal relationship , 8–9% of inter-species differences in gene expression levels might be explained by corresponding changes in DNA methylation . However , other mechanisms are also likely [34] , [43] . While DNA methylation is typically considered a silencing mechanism , high levels of methylation may be causally linked to increased gene expression levels . For example , the methylation of a repressor site could prevent the binding of repressor transcription factors , or enhancer transcription factors could favor binding to a methylated site rather than to the unmethylated site [49]–[51] . The observation of a small enrichment of positive correlations between methylation and expression when only T-DMRs are considered provides additional support for these types of mechanisms . Thus , perhaps as much as 12–18% of differences in gene expression levels between humans and chimpanzees might be explained by inter-species changes in DNA methylation . Either way , our results suggest that DNA methylation differences in promoter regions might account for , at most , a modest proportion of inter-primate differences in gene expression levels ( we confirmed that our estimates do not rely on arbitrary choices of specific statistical cutoffs; Tables S2 and S5 ) . Many inter-species differences in promoter methylation are not associated with gene expression differences between the species . One explanation for that observation may simply be that these methylation patterns are not regulatory or functional . An alternative , more interesting possibility to consider , is that a subset of genes whose regulation differed between species later acquired modifications in nearby DNA methylation patterns to accommodate ( or even partially counteract ) the original expression level changes . Since we assayed methylation using a pre-designed microarray , changes in DNA methylation in un-assayed genomic regions might explain additional regulatory differences between the species . In particular , while our assay focused on methylation at promoter regions , it has been recently shown that as a class , gene-body methylation profiles might explain a larger proportion of variation in gene expression levels than methylation profiles at currently annotated promoters [26] , [48] . With the advent of new sequencing technologies , it will soon be feasible to extend our comparative approach to characterize genome-wide patterns of methylation . In summary , we have taken some of the first steps towards characterizing variation in one mechanism that affects gene expression differences between closely related primate species [16] , [17] . In a broader context , DNA methylation is just one of many mechanisms that have been posited to regulate gene expression levels [28] , [31] , [52] . In that sense , our study is a step towards the ultimate goal of understanding the relative importance of changes in different regulatory mechanisms to human evolution . Our observations indicate that at least 82% of gene expression differences between humans and chimpanzees ( in the three studied tissues and specific promoter CpG sites examined ) are not likely to be explained by differences in promoter DNA methylation . We collected methylation data from the same human and chimpanzee liver , kidney , and heart tissue samples used in Blekhman et al . 2008 [8] ( Figure S1; see Table S6 for details on the samples ) . DNA was extracted from each sample ( 6 human and 6 chimpanzee samples from each of the three tissues ) in two independent technical replicates using the QIAamp DNA Mini Kit ( Qiagen ) ( with the exception of chimpanzee sample CK2 , for which DNA was only available for one replicate – see Table S4 ) . The methylation profile of each sample was assayed using the Illumina HumanMethylation27 DNA Analysis BeadChip , which assays methylation at 27 , 578 CpG sites . Methylation array data are deposited to the NCBI GEO database under the accession number GSE26033 ( http://www . ncbi . nlm . nih . gov/projects/geo/query/acc . cgi ? acc=GSE26033 ) . To facilitate an unbiased comparison of methylation and gene expression levels in the human and chimpanzee samples , we first mapped the 27 , 578 50-bp Illumina probes to the human genome sequence ( hg18 ) using BLAT [53] and MAQ [54] . We retained only the 26 , 690 probes that unambiguously mapped to a single location in the human genome with a maximum of two mismatches . These probes were then associated with the nearest gene using Ensembl gene annotation , and we retained only the subset of probes associated with genes that were represented on the multi-species gene expression microarray used by Blekhman et al . 2008 [8] . This resulted in the retention of 19 , 849 probes , associated with 11 , 059 genes . Finally , since the Illumina array was designed based on human genomic sequence , we limited our analysis to probes that were a perfect sequence match to a single location in the chimpanzee genome , by mapping the remaining 19 , 849 probes to the chimpanzee genome ( panTro2 ) using BLAT [53] and MAQ [54] . We retained 10 , 575 probes that mapped uniquely to the chimpanzee genome with no sequence mismatches . This step ensures that our relative methylation measurements are not biased due to the effect of sequence mismatches on hybridization intensities . The resulting set of 10 , 575 probes is associated with 7 , 723 genes , which are present on every chromosome in the genome except for the Y-chromosome ( Figure S10 ) . The majority ( 97% ) of retained probes are located within 2 kb of an annotated transcription start site of the associated gene ( Figure S11 ) . We note that a similar screen for probes that were a perfect match to the genomes of human , chimpanzee , and rhesus macaque resulted in the retention of only 1 , 944 probes ( associated with 1 , 715 genes ) . For that reason , we limited our current study to a comparison between human and chimpanzee samples . All samples were hybridized to the Illumina HumanMethylation27 DNA Analysis BeadChip at the Southern California Genotyping Consortium facility following standard manufacturer's instructions . Basic quality checks were performed using Illumina's BeadStudio software . Of the 10 , 575 probes we considered as the final dataset , 299 had missing data for one or more individuals and were discarded in all subsequent analyses . This resulted in 9 , 911 autosomal probes ( corresponding to 7 , 291 genes ) and 365 probes on the X-chromosome ( corresponding to 266 genes ) . Since the probes map to distinct CpG island regions , which can affect downstream gene expression independently , we treated methylation levels from each CpG probe as distinct data points in all subsequent analyses . We further classified each probe as being located confidently within a CpG island region or outside of a strict CpG island region using the CpG Islands track information downloaded from UCSC [47] . For each sample , the methylation status at a probed location was summarized as: where M and U denote the signal emitted from the beads assaying the methylated and unmethylated versions at each site , respectively . Due to the number of samples being interrogated , it was necessary to hybridize the samples in two balanced batches . We observed a small difference in the mean β-value between batches , and corrected for this difference by standardizing the means across batches . After this correction , there was no further evidence for a batch effect . To further assess the quality of the data , we calculated pairwise correlations between the β-values for all hybridized samples ( Figure S2 ) . As expected , technical replicates ( which were independent DNA extractions ) were the most highly correlated ( 36 comparisons; median r = 0 . 99 ) , followed by samples from the same tissue and species ( 396 comparisons; median r = 0 . 98 ) , samples from the same tissue across species ( 432 comparisons; median r = 0 . 97 ) , samples from different tissues from the same species ( 864 comparisons; median r = 0 . 95 ) , and samples from different tissues and different species ( 864 comparisons; median r = 0 . 93 ) . To look for evidence of imprinting in both humans and chimpanzees , we focused on a set of 27 genes ( associated with 90 methylation probes ) known to be imprinted based on the Imprinted Gene Catalog ( IGC ) at http://igc . otago . ac . nz/ . To assess whether the patterns of DNA methylation at these imprinted genes were likely to occur by chance , we compared the observed proportion of hemi-methylated sites ( defined as 0 . 3<β<0 . 7 ) to the distribution obtained by analyzing methylation patterns in 1000 randomly chosen sets of 90 methylation probes , associated with an average of 27 genes ( range 26–28 ) . Measurements of gene expression levels for all samples in our study were previously described by Blekhman et al . ( 2008 ) [8] . These data are available at the Gene Expression Omnibus ( GEO ) database ( http://www . ncbi . nlm . nih . gov/geo/ ) under series accession number GSE11560 . In that study , a multi-species microarray was used to estimate gene expression levels in cDNA samples from humans , chimpanzees , and rhesus macaques . The multi-species array includes orthologous probes for 18 , 109 genes , thus facilitating comparisons of gene expression levels between species without the confounding effects of sequence mismatches on hybridization intensities [8] . Since our current study focused only on the human and chimpanzee gene expression data , we re-normalized the expression data using only the human and chimpanzee probes on the array , using the same modified quantile normalization approach described in Blekhman et al . ( 2008 ) [8] . All further analyses used these re-normalized gene expression estimates . When examining the relationships between gene expression and methylation levels , we limited our analyses to genes that were either expressed in at least one tissue ( for inter-tissue comparisons within a species ) or expressed in at least one species ( for the inter-species comparisons within a tissue ) , using a conservative threshold for defining expression , based on the entire distribution of expression values ( normalized expression value of 8; see Figure S14 in Blekhman et al . ( 2008 ) [8] ) . All statistical analyses were performed using the R statistical framework ( http://www . r-project . org ) . To identify T-DMRs , we modeled the methylation level of each CpG site separately within both humans and chimpanzees using a linear mixed-effects model . Specifically , for each of the 9 , 913 probes ( associated with 7 , 291 genes ) located on the autosomal chromosomes , if yijk represents the β value for technical replicate k ( k = 1 or 2 ) , for individual j ( j = 1 , … , 6 ) , from tissue i ( i = heart , liver , or kidney ) , we assume that: ( 1 ) where: Here , αi represents the mean methylation value at a given site in tissue i . To account for correlation between samples of the same tissue from different individuals , a random effect , ρij , which follows a N ( 0 , σ2rand ) distribution , is also included in the model . To determine whether a CpG site was likely to fall within a T-DMR , we assessed how well the model ( 1 ) fitted the data under various parameterizations of μijk . The three types of parameterizations considered are: In the simplest model ( H0 ) , the region's methylation value is assumed to be constant across all three tissues , while in the second alternative ( H2 ) the methylation value is allowed to differ between all three tissues . The first alternative ( H1 ) models the situation where the methylation level at the site of interest is constant in the two non-target tissues but differs in the target tissue . All models are fitted using a restricted maximum likelihood ( REML ) framework , and the maximum likelihoods were calculated . In this study , we are interested in identifying sites whose methylation levels are best modeled by H1 . To find such sites , we first used a likelihood-ratio test statistic ( with one degree of freedom ) to exclude sites where H2 provides a better fit to the data than H1 ( specifically , if the likelihood-ratio p-value was less than 0 . 05 , we removed these sites from the analysis ) . H2 provides a better fit for 1220 and 886 ( in humans and chimpanzees , respectively ) of the total 9911 autosomal CpG sites . For the remaining positions , we examined whether there was significant evidence to reject H0 in favor of H1 using a likelihood-ratio test statistic ( which we compared to a χ2 distribution with 1 degree of freedom ) . We corrected for multiple testing using the FDR approach of Storey and Tibshirani [55] . We used GeneTrail ( http://genetrail . bioinf . uni-sb . de ) [56] to test for enrichments of functional annotations among different classes of T-DMRs . In all tests , we used a background set of genes that were present in our study and classified as expressed in at least one tissue ( conditional on a normalized expression value of 8 ) . The tests were performed using all GO categories and KEGG pathways . We calculated p-values using a Hyper-geometric distribution and report false discovery rates for each p-value . To examine whether changes in gene expression levels between humans and chimpanzees ( within each tissue ) can be explained by inter-species differences in methylation levels , we extended the linear mixed-effects model framework described in Blekhman et al . ( 2008 ) [8] to include methylation as a covariate . However , since we have to correct the multi-species array data for probe-effects [8] , it is difficult to interpret the methylation coefficient when it is added directly to the model , since it is confounded with the probe effects . Consequently , we used an alternative approach in which we used regression to correct for the methylation effect . Specifically , for each gene-tissue combination , we tested for differences in expression level between human and chimpanzee after regressing out the following effects: To do this , we used a fully parameterized model where gene expression probe effects , CpG-probe methylation values , and species effects were explanatory variables . Additionally , a random effect was used to account for variability between biological replicates . Specifically , if ysroi denotes the normalized log2 intensity expression value for individual i ( i = 1 , … , 6 ) , from species s ( s = human or chimpanzee ) measure at probe r ( r = 1 , … , 7 ) , which is derived from species o , we assume that: where: Here , μs denotes the species effect , πro is a fixed-effect representing the probe effect for each individual probe within a probe-set and the composition effect of species-specific orthologous probes , and κsro is a fixed-effect representing the attenuation of hybridization intensities due to sequence mismatches between species of RNA and a species-specific derived probe , which are different for each individual probe within a probe set ( see [8] for more details ) . Additionally , γsi is a random effect ( following a N ( 0 , σ2rand ) distribution ) and βsi denotes the β value for the methylation probe of interest for individual i from species s . Upon fitting this model , using the lmer package within the R statistical framework , estimates of the parameters and the residuals were obtained . To obtain corrected measures of expression for each individual from each species , when probe and methylation effects are regressed out ( scenario 2 ) , we defined . When we only regressed out probe effects ( scenario 1 ) , the corrected values are defined as . In both of these scenarios , once the corrected data were obtained , we tested for differences in gene expression levels as follows . If , for each tissue-gene combination , xsik denotes the ( corrected ) level of expression for replicate k of individual i from species s , we modeled these data as follows:where:Here , αs is a species effect , and ρsi is a random individual effect . Subsequently , to test for inter-species differences in expression levels , we compare the following hypotheses:Here , the null model assumes equal expression level between the two species , and the alternative assumes different expression levels . Evidence against the null model was determined using a likelihood-ratio test statistic ( compared against a chi-squared distribution with one degree of freedom ) . By performing this analysis independently for each CpG-gene combination in all tissues , we obtained a p-value indicating the strength of the evidence against the null hypothesis , before ( under scenario 1 above ) and after ( under scenario 2 above ) accounting for the region's DNA methylation status . By comparing these p-values , we were able to identify genes within each tissue where the difference in expression level between human and chimpanzee was likely explained by inter-species differences in DNA methylation . To assess the statistical significance of our observations , we permuted the methylation values for a given gene across all individuals ( maintaining replicate correlations , but allowing labels to permute across species classifications ) . Subsequently , we repeated the analysis described above to obtain an expected distribution of discrepancies between the methylation-corrected and uncorrected data . We performed 1000 permutations and p-values were calculated based on the number of times we observed as many or more discrepancies in the permuted compared to the real data . In order to estimate the proportion of genes for which methylation differences might underlie gene expression differences , we treated the medians of the permutation distributions from each tissue as background levels . For each tissue , we then subtracted the background level from the observed proportion of genes with reduced evidence for inter-species differences in gene expression levels , once methylation was taken into account .
It has long been hypothesized that changes in gene regulation have played an important role in primate evolution . However , despite the wealth of comparative gene expression data , there are still only few studies that focus on the mechanisms underlying inter-primate differences in gene regulation . In particular , we know relatively little about the degree to which changes in epigenetic profiles might explain differences in gene expression levels between primates . To this end , we studied DNA methylation and gene expression levels in livers , hearts , and kidneys from multiple humans and chimpanzees . Using these comparative data , we were able to study the evolution of gene regulation in the context of conservation of or changes in DNA methylation profiles across tissues and species . We found that inter-tissue methylation patterns are often conserved between humans and chimpanzees . In addition , we also found a large number of gene expression differences between species , which might be explained , at least in part , by corresponding differences in methylation levels . We estimate that , in the tissues we studied , inter-species differences in methylation levels might underlie as much as 12%–18% of differences in gene expression levels between humans and chimpanzees .
You are an expert at summarizing long articles. Proceed to summarize the following text: Detecting regular patterns in the environment , a process known as statistical learning , is essential for survival . Neuronal adaptation is a key mechanism in the detection of patterns that are continuously repeated across short ( seconds to minutes ) temporal windows . Here , we found in mice that a subcortical structure in the auditory midbrain was sensitive to patterns that were repeated discontinuously , in a temporally sparse manner , across windows of minutes to hours . Using a combination of behavioral , electrophysiological , and molecular approaches , we found changes in neuronal response gain that varied in mechanism with the degree of sound predictability and resulted in changes in frequency coding . Analysis of population activity ( structural tuning ) revealed an increase in frequency classification accuracy in the context of increased overlap in responses across frequencies . The increase in accuracy and overlap was paralleled at the behavioral level in an increase in generalization in the absence of diminished discrimination . Gain modulation was accompanied by changes in gene and protein expression , indicative of long-term plasticity . Physiological changes were largely independent of corticofugal feedback , and no changes were seen in upstream cochlear nucleus responses , suggesting a key role of the auditory midbrain in sensory gating . Subsequent behavior demonstrated learning of predictable and random patterns and their importance in auditory conditioning . Using longer timescales than previously explored , the combined data show that the auditory midbrain codes statistical learning of temporally sparse patterns , a process that is critical for the detection of relevant stimuli in the constant soundscape that the animal navigates through . As we interact with the environment , our brain is constantly detecting patterns—i . e . , regularities—in the sensory world . This capacity allows us to recognize surrounding stimuli and make predictions necessary for survival . Patterns in the sensory input are extracted through a process known as statistical learning [1] . Regularities in the continuous sensory input that fit relatively short windows , in the order of seconds to tens of seconds , can be encoded through neuronal adaptation of response gain in both subcortical and cortical structures [2–4] . However , little is known about the circuits that code patterns that are temporally sparse , i . e . , when the regularity is repeated discontinuously across time windows of minutes and hours . Statistical learning of sparse patterns is important for grammatical learning or musical sensitivity in humans [5 , 6] , both of which are achieved through exposures that occur across days to years . This type of learning is likely to involve long-term plasticity mechanisms , different from neuronal adaptation . Changes in neuronal response gain that reflect fast adaptation are ubiquitous in the auditory cortex ( AC ) [2 , 7 , 8] but can also be found in the inferior colliculus , a subcortical midbrain structure that is the first convergence station in the auditory circuit [9] . For example , stimulus probability selectivity [3 , 4 , 10 , 11] , as well as some forms of response selectivity to natural sounds [12–14] , is observed in some divisions of the inferior colliculus [4] . Correlations between inferior colliculus activity and temporal patterns , such as speech or rhythmic tapping , have also been described in humans [11 , 12] . We hypothesized that neuronal correlates of statistical learning of temporally sparse patterns can also be found in the inferior colliculus . The context can be a strong predictor of the soundscape . In real life , as animals move through the environment , they can reencounter the same context and its characteristic sounds in temporally spread bouts . Here , in order to understand the neuronal coding of temporally sparse patterns in the sensory input , we used context–sound associations as stimuli . Thus , we set out to specifically test ( 1 ) whether mice can detect temporally sparse context–sound associations and ( 2 ) whether this detection triggers changes in the response patterns of neurons in the inferior colliculus . To recreate a natural environment while maintaining control over the experimental variables , we used the Audiobox—a socially , acoustically , and behaviorally enriched environment in which mice lived in groups for up to 2 weeks [15] . Mice were exposed to sounds that were associated with the context , with different degrees of predictability . The consequence of this exposure was assessed at the behavioral , electrophysiological , and molecular levels . First , we measured the effect that temporally sparse sound exposure had on the response gain of collicular neurons by simultaneously measuring evoked responses across different frequency bands . We subsequently assessed the effect these changes had on frequency coding and discrimination before testing how physiological changes in sensory gating paralleled behavioral generalization measures . We then confirmed that plasticity-associated changes in gene and protein expression had taken place . Since conditioning-triggered midbrain plasticity can depend on corticofugal input [16] , we tested the dependence of the observed changes on cortical feedback . Finally , to ascertain the origin of changes in the activity of inferior colliculus neurons , we assessed the effect that sound exposure had on upstream and downstream structures . We did not find changes in the animal’s behavior during sound exposure that could indicate learning of the context–sound association . In order to ascertain whether statistical learning had occurred , we tested the effect that the different exposure patterns had on subsequent conditioned frequency discrimination . For that purpose , we used latent inhibition ( LI ) [17 , 18] . LI is the effect by which exposure to a neutral , nonreinforced stimulus delays learning of a subsequent association between this stimulus and an aversive outcome . We have shown before [15] that the mere exposure to a sound in the corner elicits LI in the Audiobox when the sound is subsequently conditioned in the same place , indicating that the presence of the sound in the corner was learned . We now probed the conditions under which LI is observed by comparing the effect of predictable and random sound exposure . Following the predictable or random sound exposure phases ( 16 kHz; S1C Fig and Methods ) , all mice were conditioned to 16 kHz sound in some visits to the water corner , such that a nose-poke during conditioned visits would trigger the delivery of an aversive air puff ( S1D Fig ) . Mice needed to discriminate between safe visits and conditioned visits and refrain from nose-poking during the latter . On the first day of conditioning , the control ( never exposed to 16 kHz ) and random ( exposed to 16 kHz outside the corner ) groups showed successful avoidance when 16 kHz was present in the corner and good discrimination , as reflected in d′ values above 1 ( S1E Fig ) . The predictable group , on the other hand , had d′ values significantly below the other groups ( S1E Fig ) , indicating the failure to avoid nose-poking when 16 kHz was present , i . e . , the occurrence of LI . This indicates that mice had learned the association between the safe 16 kHz tone and the corner during the exposure phase . Note that random sound exposure in the food area had a mild effect on the levels of avoidance in the corner during conditioning ( S1E and S1F Fig , green triangles ) , and mice never reached the level of performance of the control group , suggesting that both forms of sound exposure influenced subsequent avoidance during conditioned visits , albeit with weaker effects when random . In summary , all three groups behaved identically during the exposure phase but showed three different patterns of behavior during subsequent conditioning of the 16 kHz sound in the corner . Thus , learning of the association between the predictable sound and the context where it was heard ( the water corner ) did occur even though it had no effect on behavioral measures during the exposure itself . We conclude that the exposure protocol constitutes a successful model of temporally sparse statistical learning . The inferior colliculus is an auditory subcortical station on which diverse sensory information converges [9] . It has been shown to be sensitive to short-term statistical learning through neuronal adaptation . We now investigated whether statistical learning of temporally sparse patterns could affect the coding properties of the inferior colliculus . We acutely recorded from the inferior colliculus of anesthetized animals exposed to predictable or random 16 kHz for 6–12 days ( Fig 1A and 1B ) . We recorded multiunit activity from well-separated spikes ( S2A Fig ) using linear multielectrode arrays ( 16 sites , 50 μm apart ) inserted dorsoventrally along the collicular tonotopic axis ( Fig 2A and 2B ) . The first electrode was on the dura , and the second electrode rarely gave reliable responses . We therefore characterized auditory-evoked responses to different tone frequency–intensity combinations simultaneously in the remaining 14 depths ( 100–750 μm , see Methods ) . Depths of 100 and 150 μm were considered to be putative dorsal cortex based on different response patterns [19 , 20] , and the remaining depths , the central nucleus . All experimental groups showed a dorsoventral axis of tonotopic organization in the inferior colliculus such that progressively higher frequencies elicited responses progressively deeper ( Fig 2C; representative example raster plots in S2B–S2D Fig ) , in agreement with previous studies [21 , 22] . Tuning was quantified using spikes evoked at 70 dB SPL ( behavioral mean exposure intensity was 68 dB ) by stimuli of 30 ms length ( see Methods ) . An increase in response gain was evident in the tuning curves of predictable animals with respect to control animals at multiple depths along the tonotopic axis of the inferior colliculus ( Fig 2C ) . The predictable group had homogenously high levels of activity across all depths ( see Fig 2C , red , for mean ) . The random group had high activity localized to the putative dorsal cortex ( <200 μm depth ) and to depths with best frequencies ( BFs; the frequency that elicits the strongest response in a given location ) around 16 kHz ( 500–550 μm: Fig 2C and S2E Fig , green ) . This pattern of responses in the predictable and random groups was confirmed by quantification of peak firing rates in depth zones ( S3A Fig ) . The overall mean peak of firing rate of the control group was similar to age-matched animals reared under standard conditions ( home cage group ) but significantly smaller than the predictable group ( S3B Fig ) . Thus , sound exposure , whether predictable or random , generated an increase in collicular evoked activity compared to control animals . While in the random group , the increase was localized to depths with good responses at and near 16 kHz; in the predictable group , it was homogeneously distributed . The effect was not dependent on the frequency of the exposed tone , since mice in a predictable group exposed to frequencies other than 16 kHz also showed an increase in response gain ( S3C Fig for group exposed to 8 kHz ) . The effect was not dependent on the number of exposure days ( 6–12 days ) in the Audiobox ( S3D and S3E Fig ) . When individual tuning curves were aligned by BF rather than depth , the overall increase in excitability in the predictable group remained ( S3F Fig ) . Experience-dependent plasticity , such as auditory conditioning , can induce transient shifts in the BF of collicular neurons [23–25] . Indeed , we noticed that the peaks of the tuning curves of the predictable group were shifted in multiple depths ( Fig 2C , e . g . , 300–500 μm ) compared to the control group . Unlike what has been reported before as a result of conditioning , the shift in BFs that resulted from sound exposure was not toward the conditioned frequency but toward higher frequencies , even in regions with BFs of 16 kHz or above . The average BFs were consistently higher in the predictable and , to a lesser extent , the random group than in the control and home cage groups ( Fig 2D ) . Further quantification of the mean difference in BF across depth with respect to the control group confirmed this effect ( Fig 2E ) . The BF shift was independent of the frequency of the sound played in the water corner area . We measured the BFs in animals that were exposed under identical conditions to frequencies different from 16 kHz ( either 8 kHz , 13 kHz , or a combination of 8 and 13 kHz ) . Except for the group exposed to 8 kHz alone , which did not show a reliable shift in BF with respect to controls ( but note shifts in this group at specific depths , S3C Fig ) , shifts were similar in magnitude to those observed in mice exposed to 16 kHz ( S4A Fig; see Methods ) . Interestingly , average BF at threshold intensities was similar between groups ( S4B Fig ) , indicating that the shift is in suprathreshold tuning rather than a real change in tonotopy . Care was taken during the probe insertion to ensure consistency in the location and depth of the electrodes ( see Methods ) , and small variations from animal to animal cannot explain the systematic group differences . Additionally , simultaneous recordings along the rostrocaudal axis of predictable and control animals ( S4C Fig; see Methods ) revealed that the upward shift was present throughout the dorsoventral axis in the rostral and caudal portions of the inferior colliculus . In summary , there was a homogenous , frequency-unspecific , and suprathreshold shift in tuning in both exposed groups . The shift was significantly stronger in the predictable group and , unlike previously described for conditioning paradigms [23–25] , the shift was not toward the exposed frequency but upward along the tonotopic axis . Experience-dependent plasticity often results in changes in response gain [26 , 27] , which can take the shape of changes in response reliability , spontaneous activity , signal-to-noise ratio ( SNR ) , and tuning bandwidth [28 , 29] . To evaluate which of these variables was responsible for the increase in response gain in the predictable and random groups in a frequency-specific manner , we divided recording sites in 2 equally sized regions: one of sites with a BF tuned around 16 kHz ( 14–19 kHz , “tuned” hereafter; Fig 3A ) and another with sites tuned to 10–13 kHz ( “adjacent” hereafter; Fig 3A ) . We first measured whether the increase in gain was the result of an increase in firing rate alone or also in the reliability of evoked responses ( defined as the percentage of trials with at least 1 spike during the evoked period , 0–80 ms from stimulus onset; example in Fig 3A , right ) . In both the tuned and adjacent regions , response reliability was stronger around the local BF and decreased toward the edges of the frequency range , mirroring tuning ( Fig 3B ) . In the tuned region ( Fig 3B , right ) , the reliability of the evoked responses was significantly higher in the random group compared to the other groups , as quantified for the peak of tuning ( Fig 3C , right; see example in Fig 3A , right ) . On the other hand , spontaneous activity was similar across groups in the tuned region but higher for the predictable group in the adjacent region ( Fig 3D; see example in Fig 3A , right ) . If only adjacent regions showed an increase in spontaneous activity , mice exposed to a tone in the low frequency range ( 8 kHz ) would show a converse pattern: an increase in spontaneous activity in the region that we now call tuned ( Fig 3E ) . Indeed , when mice were exposed to 8 instead of 16 kHz , we found that the spontaneous activity was increased in the area with BFs near 16 kHz and comparable in the regions with BFs near 8 kHz ( Fig 3F ) . The region-specific increase in spontaneous activity had a direct effect on the SNR ( evoked/spontaneous firing rate ) , which was significantly smaller in the adjacent region compared to the tuned region in the predictable group ( S5A Fig ) . We conclude that the SNR increased in the area that responds to the exposed tone , independently of its frequency , compared to the flanking regions . Finally , tuning bandwidth was increased in the predictable group with respect to both control and random groups . The effect was observed at both the base and half-maximum of the tuning curve ( Fig 3G , left and right respectively ) . Changes in gain were not the result of changes in overall excitability , since intensity thresholds were similar ( 35 dB ) in all groups ( S5B Fig ) . Additionally , we quantified response latency ( see Methods ) , which is known to decrease with the efficiency of the stimulus [30] . In the predictable group , latencies were similar in both regions compared to the control group ( S5C Fig ) . In the random group , latencies were lower than the control group in the adjacent region and lower than the predictable group in the tuned region ( S5C Fig ) . To conclude , the increase in response gain observed in the predictable and random groups resulted from different mechanisms ( Fig 3H ) . In the predictable group , the increase in response gain was frequency unspecific and affected the evoked and the spontaneous activity , as well as the tuning bandwidths . Moreover , spontaneous activity was reduced in the tuned region , resulting in a local increase in SNR . In the random group , the increase in evoked activity was centered around the exposure frequency and was , at least in part , the result of increased reliability without affecting either spontaneous activity or tuning bandwidth . Auditory input evokes responses throughout the tonotopic map . This is reflected in neither peri-stimulus time histogram ( PSTH ) nor tuning curves , both of which represent local responses . Since we recorded simultaneously from 14 locations along 700 μm of the inferior colliculus , we were able to quantify the simultaneous response to a given frequency along the collicular tonotopic axis . We will refer to this response as structural tuning ( Fig 4A and 4B ) . Unspecific increases in bandwidth , such as that observed in the predictable group , would have the effect of increasing the response gain to a given frequency tone throughout the tonotopic map ( Fig 4A , light red versus dashed structural tuning ) . Increases in reliability that are not accompanied by changes in tuning bandwidth , such as that observed in the random group , would have the effect of increasing a structural tuning curve’s gain at a local depth without much change elsewhere ( Fig 4B , light green versus dashed structural tuning curves ) . Indeed , sound exposure affected structural tuning curves of different frequencies for the predictable and random groups , which were more distinct across frequencies compared to those of control animals ( Fig 4C ) . The effect this has on coding will be assessed below . We assessed how different changes in response gain across groups both locally ( region specific , tuning curves ) and globally ( structural tuning ) affected frequency coding and discrimination . We measured between-frequency discrimination and within-frequency response consistency using receiver operating characteristic ( ROC ) curve analysis and classification accuracy measures , respectively . ROC analysis is used to assess discriminability between two stimuli [31] by comparing the cumulative probability distributions of responses to these stimuli for different discrimination criteria ( Fig 5A and 5B ) . For the local tuning , we used individual tuning curves with a BF of 11 . 3 kHz ± 1 . 1% ( adjacent region ) or 16 kHz ± 1 . 1% ( tuned region ) and generated ROC curves for comparison between the BF and the to-be-compared frequency ( f1 and f2 in Fig 5A ) . We then used the area under the ROC curve ( AUROCC , Fig 5B ) as the index of discriminability . ROC curves obtained from tuning curves in the adjacent region were not different between predictable and random groups ( Fig 5D ) . In the tuned region , however , the random group showed better discrimination ( larger AUROCC ) for all ΔFs than both the control and predictable groups , who do not differ between them ( Fig 5E ) . This region-specific increase in discriminability in the random group parallels the region-specific increase in both gain and reliability in this group , in the absence of a change in bandwidth . In the predictable group , there was no change in discriminability in either region , which is consistent with the region-unspecific increase in both gain and bandwidth ( Fig 5D and 5E ) . This consistency derives from the fact that ROC curves are not sensitive to changes in response size , only to changes in distributions , and these are not necessarily changed when gain and bandwidth increase together . We then performed the same analysis for the structural tuning . This was performed for individual responses to a given frequency compared to the mean response ( across trials ) to 11 . 3 kHz ( Fig 5F ) and 16 kHz ( Fig 5G ) . Here , the predictable group shows less discriminability between frequency pairs ( Fig 5F , in which f1 = 11 . 3 kHz , and Fig 5G , in which f1 = 16 kHz ) than both the random and control groups . This decrease in discriminability in the predictable group is consistent with the increase in bandwidth and the concomitant increase in activity throughout the structural tuning curve ( see Fig 4A ) , which ultimately changes response distribution across the tonotopic axis and increases overlap between structural tuning curves . To a certain extent , ROC analysis reflects the variability in the response to each of the stimuli compared . Yet this is not true for the structural tuning ROC curves , because their wide response distributions ( responses across all depths ) and their asymmetrical shapes ( Fig 5C ) increase the level of overlap between the distribution curves without reflecting the trial-to-trial variability at the peak of the distribution ( Fig 5H ) . Trial-to-trial response consistency can be measured using classification accuracy probabilities . We used structural tuning curves to train a classifier [32 , 33] to predict the played frequency ( see Methods ) . The probability of predicting a given frequency correctly was significantly higher in both predictable and random groups with respect to control . In both groups , accuracy was higher in the tuned versus the adjacent region ( Fig 5I ) . Overall , the data suggest that statistical learning is accompanied by changes in neuronal coding in the inferior colliculus that affect frequency discrimination and response classification accuracy . The described changes in frequency coding could , potentially , have different effects on behavioral measures of frequency discrimination . We next tested this using a behavioral measure of spontaneous frequency discrimination . We used the prepulse inhibition of the auditory startle reflex ( PPI ) , a behavioral assay that is known to engage the inferior colliculus [34 , 35] and has been successfully used to determine frequency discrimination acuity in mice in the absence of training ( Fig 6A ) . When assessed in the presence of a constant background tone , the percentage of PPI is proportional to the difference between the background and prepulse tones [36–38] . Predictable and random groups were exposed as before to a 16 kHz tone for 6–12 days in the Audiobox . PPI was then measured in a separate apparatus , using a background tone of 16 kHz and progressively different prepulse tones up to 1 octave ( see Methods ) . The percentage of PPI elicited was significantly smaller in the predictable group than in the control and random groups at multiple prepulse frequencies tested ( Fig 6B ) . Similarly , the average discrimination threshold ( 50% of inhibition of maximum response , see Methods ) of the predictable group was higher than both the control and random groups but only reached significance against the latter ( S5D Fig ) . The increased generalization in the predictable group was not specific to frequencies around 16 kHz . PPI measured with a background tone of 11 . 3 kHz in animals exposed to 16 kHz ( Fig 6D ) also showed a significant increase in frequency generalization ( Fig 6E ) . Thus , only predictable sound exposure resulted in greater frequency generalization . Next , we questioned whether changes in behavioral frequency discrimination were related to the collicular changes observed in frequency coding described above . We calculated ROC curves from the PPI data to be able to compare the behavioral and neuronal responses under the same method [31] . Surprisingly , the predictable and random groups showed larger AUROCCs when the background tone was 16 kHz , although the effect was not significant ( Fig 6C ) . This is surprising because lower PPI is typically attributed to decreased discrimination acuity . The effect was specific to the frequencies around the exposed tone . When the background tone was 11 . 3 kHz , the increased generalization observed in the PPI for the predictable group was paralleled by diminished discrimination , as reflected in the lower AUROCCs , in this group with respect to the control group ( Fig 6F ) . In conclusion , the increased generalization observed in the PPI in the predictable group is consistent with the ROC analysis of the structural but not the local tuning for the same group ( Fig 5F and 5G ) . This increase in generalization paradoxically did not reflect a decrease in discrimination , which was normal in both predictable and random groups for frequencies in the tuned region . That this effect was frequency specific , since discrimination was reduced for frequencies in the adjacent region , is consistent with the physiological classification accuracy measures ( Fig 5D , 5E and 5I ) . Auditory conditioning studies have shown that collicular plasticity depends on direct cortical feedback through descending projections from layer V of the AC [39 , 40] . To test whether the maintenance of the changes in collicular response that had been triggered by predictable sound exposure were also dependent on cortical feedback , we performed simultaneous inactivation of the AC with muscimol and recordings in the inferior colliculus on a subset of control and predictable animals ( see Methods , Fig 7A and S6A Fig ) . Cortical inactivation generated an increase in collicular evoked activity in both groups without affecting the differences in overall tuning between groups , including the BF shift ( see tuning curves at 600 μm in Fig 7B; and S6B and S6C Fig ) . The increase in the activity of individual recording sites before and after cortical inactivation was comparable between groups ( Fig 7C ) . Cortical inactivation affected neither reliability ( Fig 7D ) nor the difference in spontaneous activity in the adjacent region ( Fig 7E , left ) . However , upon cortical inactivation , spontaneous activity of the predictable group increased in the tuned region ( Fig 7E , right ) . This increase reveals a cortical control of collicular excitability that occurs specifically in the region tuned to the exposed sound . Cortical inactivation slightly increased the bandwidths for both groups without affecting the difference between them ( Fig 7F ) . In summary , cortical inactivation resulted in an overall increase in the amplitude of the tuning curves that did not affect the difference in gain between the groups . The relatively lower spontaneous activity in the tuned region disappeared after cortical inactivation , revealing a frequency-specific form of cortical control on the inferior colliculus SNR . These data suggest that cortical feedback plays a minor role in the maintenance of sound exposure–triggered collicular plasticity . We next asked whether the changes in evoked activity and frequency representation were the result of an overall increase in excitability throughout the auditory pathway . Single-unit recordings in the cochlear nucleus—the main ascending input into the inferior colliculus—of animals in the control and predictable groups were similar in tuning , evoked , and spontaneous activity ( Fig 8A–8C ) . Additionally , predictable sound exposure had no effect on either thresholds or bandwidths ( S7A–S7D Fig ) , suggesting that exposure-triggered changes in the inferior colliculus were not the result of upstream plasticity . Similarly , evoked responses recorded in the primary auditory cortices of control and predictable mice were similar in overall tuning , temporal response pattern , and BF distribution ( S7E–S7H Fig ) . Changes observed in the inferior colliculus were thus not inherited from the main upstream input , the cochlear nucleus . They also did not result in an obvious change in cortical tuning , although it is possible that more subtle effects would be observable in a behaving animal . Fast neuronal adaptation , previously described in the inferior colliculus [3 , 41] , occurs within tens of seconds and would not necessarily be expected to be accompanied by changes in gene or protein expression . Sparse sound exposure , however , requires the integration of information across minutes and over several visits to the context associated with the sound . To investigate whether the observed changes were paralleled at the molecular level after predictable exposure , our key experimental condition , we measured gene expression in the predictable and control groups , using the home cage group as reference . We assessed the expression of neuronal genes reported to change their expression levels upon sound exposure , acoustic learning , or environmental enrichment [42–47] . In most cases , the expression was similar between the control and predictable groups and different from the home cage group ( S1 Table ) , suggesting that the largest effect was triggered by the placement of animals in the Audiobox itself . Exceptions were the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid ( AMPA ) receptor subunits gria1 and gria2 and brain-derived neurotrophic factor ( BDNF ) , which were significantly reduced only in the control group with respect to the home cage group . The ratio between the expressions of the presynaptic markers glutamate vesicular transporter 2 ( vglut2 ) and the GABA vesicular transporter ( vgat ) showed a significant increase for control and predictable groups . To investigate whether the increase in the Vglut2/VGAT ratio at the level of gene expression were accompanied by molecular changes in protein expression at specific locations of the inferior colliculus , we measured immunoreactivity to VGAT and Vglut2 proteins at two depths ( 300 and 600 μm ) , corresponding roughly to the “adjacent” and “tuned” areas used before , in the central nucleus of the inferior colliculus of control and predictable animals ( S8A Fig , see Methods ) . This ratio was used as an expression of excitation/inhibition balance , since this ratio has been shown to change upon environmental manipulations and to be a signature of synaptic plasticity [46] . We found that the number of Vglut2 puncta in the dorsal ( “adjacent” ) area was similar between groups , while VGAT was significantly reduced in the predictable group . This resulted in a significant increase in the Vglut2/VGAT ratio ( S8B Fig , left ) . At 600 μm in depth ( “tuned” ) , there was a decrease in Vglut2 in the predictable animals but only a trend in the same direction for VGAT , with no difference in the Vglut2/VGAT ratio between groups ( S8B Fig , right ) . Thus , predictable and sparse sound exposure results in changes in gene and protein expression that are characteristic of long-term memory . Statistical learning is essential for a correct interpretation of the sensory input . This form of learning is likely to be distributed throughout different brain regions , depending on the stimulus patterns to be learned , their modalities , and spatiotemporal combinations [48–50] . Some forms of statistical processing must happen at the level of subcortical structures as part of sensory gating . Neuronal adaptation—changes in firing rate as a result of continuous stimulation—is maybe the best-studied mechanism of experience-dependent plasticity believed to be underlying statistical learning of environmental regularities that occur within the recent stimulation history . It has been hypothesized to increase the dynamic range of neurons as well as gating of specific inputs [51] and is observed in cortical [2 , 7 , 52–54] and subcortical structures [2–4] . Meta-adaptation has been observed across 5-second windows in a continuously alternating sensory stimulation paradigm in the inferior colliculus [4] . Yet the circuits underlying statistical learning of temporally sparse patterns have not been characterized . This timescale of statistical learning is reflected in the sensitivity of neurons in the auditory system for natural sounds [12–14 , 55–58] . Neuronal adaptation is achieved through short-term plasticity [59–61]; therefore , it is unlikely to be the mechanism underlying the type of statistical learning that needs to be accumulated across bouts of exposure that are separated by minutes to hours , like the one we describe here . Using a combination of electrophysiological , behavioral , and molecular approaches , we show that the inferior colliculus , an auditory subcortical structure , was sensitive to statistical learning of temporally sparse auditory patterns . We exposed mice to sounds that were fully predictable ( predictable group ) . This exposure was self-initiated , limited to visits to the water corner ( context specific ) , and lasted only for the duration of the individual visits ( temporally sparse ) . Exposure to these patterns resulted in an increase in response gain that was frequency unspecific and was not due to mere sound exposure , since the random group ( exposed to a sound in a fixed context but at random time intervals ) showed a different pattern of collicular plasticity . Increase in response gain changed the pattern of population activity , resulting in increased between-frequency overlap in the structural tuning but a more consistent trial-to-trial within-frequency coding . These effects were paralleled at the behavioral level , at which increased response generalization was , paradoxically , not paralleled by a decrease in frequency discrimination as is discussed below . Cortical feedback played a minor role in the maintenance of collicular plasticity , and changes were not observed in the main input structure , the cochlear nucleus [62 , 63] . This suggests that plasticity was initiated in the inferior colliculus , as further supported by changes in gene expression indicative of long-term plasticity . The combined analysis of local ( region-specific tuning curves ) and global ( structural tuning ) neuronal responses allowed us to uncover 2 coexisting mechanisms of frequency coding in the predictable group . On one hand , consistency in frequency coding was increased , as reflected in frequency-specific increase in classification accuracy . On the other hand , the potential for increased generalization was reflected in the increased overlap between structural tuning curves in the predictable group . Both increased discrimination and increased generalization were paralleled at the behavioral level . While , typically , a decrease in PPI has been interpreted as a decrease in frequency discrimination , here we found that different prepulse tones can generate discriminable startle responses and yet be less effective in generating PPI near the background tone . Thus , at the behavioral level , increased generalization in the startle’s inhibition was found to coexist with normal frequency discrimination near the exposed frequency . This highlights the relevance of responses across spatially distributed neuronal populations , in which even increased responses away from the tuned region ( the tail of the structural tuning ) might have an impact on behavioral output . Predictable sounds , when highly repetitive and consistent , are less salient . It is maybe because of this that behavioral responses to pure tones are largely more inhibited in the predictable group . In striking contrast , mice in the random group showed no evidence of diminished discrimination at either the neuronal population level or behaviorally , probably reflecting the saliency of randomness . Indeed , in this group , changes in response gain were—unlike in the predictable group—typically constrained to the tuned region . Corticocollicular projections are believed to modulate collicular sensory filters [23 , 64–67] . The narrow corridors of the Audiobox prevented us from optogenetically modulating cortical activity during the exposure . Cortical inactivation during the recording , however , subtly increased the size of the evoked responses in both control and predictable groups and had no effect on either the suprathreshold tonotopic shift induced by sound exposure or the increase in bandwidth . However , it affected the levels of spontaneous activity . The frequency-specific low level in spontaneous activity in the tuned region disappeared upon inactivation , meaning that the cortical feedback can locally reduce spontaneous activity in one region of the inferior colliculus to increase the SNR . Nonetheless , overall , the cortical inactivation data suggest that the AC plays a small role in the maintenance of learning-induced plasticity and that this is limited to local modulations of spontaneous activity . Whether corticofugal feedback is required to initiate this plasticity in the early times of exposure will require further investigation . Recently , Slee and David [68] reported increases in spontaneous activity in the inferior colliculus that resulted in suppression of responses to the target sound during an auditory detection task . Differences in excitability can be attributed to changes in interactions within the local circuit . In the predictable group , we observed changes in excitation/inhibition ratios at the presynaptic level that had no parallel at the postsynaptic level . Together , this might reflect the implementation of a switch that can be either turned on or off depending on , for example , the presence of a global signal in the form of a neuromodulator or brain state [69 , 70] . Indeed , a frequency-specific decrease in spontaneous activity in the predictable group resulted in an increase in SNR ( evoked/spontaneous activity ) . SNRs have been studied in the context of speech saliency in noisy backgrounds [71–73] and have been hypothesized to contribute to compromised sensory gating in neuropsychiatric diseases , highlighting their importance for auditory processing [74] . Recordings were performed in anaesthetized animals , and although anesthesia does not prevent the expression of preattentive mechanisms , the exact implementation of the proposed switch might be different in the behaving animal [75 , 76] . In both exposed groups , we observed a surprising shift in suprathreshold tonotopy with respect to the control group . This was reflected in a homogeneous shift in BFs across all depths measured . This shift was significantly larger in the predictable group than in the random group . While reinforcement-driven plasticity is characterized by locally measured shifts toward a conditioned frequency in both inferior colliculus and AC [77 , 78] , spatially broad frequency shifts cannot always be measured . In the one case in which this was done [64] , the shift was also found to extend beyond the directly activated frequency band . Whether the inferior colliculus uses the BF shift as a coding mechanism or this is rather a byproduct of other plastic changes will require further investigation . In fact , BF might not be a very reliable coding variable [79 , 80] . Measurements such as structural tuning , in which simultaneous responses across a widespread neuronal population are measured , might better represent the information that the brain is using at any given point in time . Differences in sensory filtering at the level of the inferior colliculus are likely to influence how information is conveyed downstream to thalamus and cortex . Depending on whether the change impinges primarily on the excitatory or inhibitory ascending input into the thalamus , the overall effect might be either to enhance or suppress selective responses . The collicular inhibitory input into the thalamus acts monosynaptically on thalamocortical projecting neurons [81] , potentially regulating the magnitude and timing of cortical activity and thus playing a crucial role in sensory gating . We did not find obvious changes in excitability or frequency representation at the cortical level after predictable sound exposure . In the auditory system , which processes a constant input of stimuli arising from all directions , preselection of to-be-attended stimuli might happen at the level of subcortical structures . In other sensory systems , filtering of stimuli might involve different circuit mechanisms [82 , 83] . Taken together , our results demonstrate that the inferior colliculus , a subcortical structure , plays a significant role in the detection of statistical regularities that arise from temporally sparse interactions with a naturalistic environment . The effect this learning had on subsequent behavior suggests that the observed changes in coding modulate the filtering of the exposed sounds to control behavioral outcomes . Our study places the inferior colliculus as a key player in the processing of context–sound associations , which are of great relevance in sound gating . This role might be the basis for the link between the inferior colliculus and autism , in which patients exhibit alterations in sensory gating [84–86] . The finding that neuronal responses are sensitive to the context in which sounds appear suggests that the inferior colliculus might integrate stimuli across a parameter space that goes beyond the auditory domain . Thus , the inferior colliculus could be acting as an early multimodal warning system . All animal experiments were approved by the local Animal Care and Use Committee ( LAVES , Niedersächsisches Landesamt für Verbraucherschutz und Lebensmittelsicherheit , Oldenburg , Germany ) in accordance with the German Animal Protection Law . Project license number 33 . 14-42502-04-10/0288 and 33 . 19-42502-04-11/0658 . Female mice C57BL/6JRj ( Janvier labs , France ) between 5 and 8 weeks old were used for all experiments . A sterile transponder ( IS0 compliant 11784 transponder , 12 mm long , TSE , Germany ) was implanted subcutaneously in the back of the anaesthetized mice . The small wound caused by the injection was closed with a drop of a topical skin adhesive ( Histoacryl , Braun , United States of America ) . After 1 to 2 days of recovery , animals were placed in the Audiobox ( New Behaviour/TSE , Germany ) . The Audiobox is an automatic testing chamber consisting of 2 compartments connected by a corridor ( Fig 1A ) , where mice lived in groups of up to 10 animals . The first compartment—the “food area”—consists of a normal mouse cage , where animals have ad libitum access to food . Water was available in the second compartment—the “water corner”—located inside a sound-attenuated chamber . An antenna located in the entrance of the corner identified the individual mouse transponder . The individual visits to the corner were detected by coincident activity of a heat sensor and the reading of the transponder . Visits occurred mainly during the dark cycle [15] . A water port is present at either side of the corner and can be closed by a sliding door . To open the door and gain access to the water , animals needed to nose-poke . Nose-pokes were detected by a sensor located by the door . The end of the visit was signaled by deactivation of the heat sensor and the absence of transponder reading . Individual-mouse data ( start and end of visit , time and number of nose-pokes ) were recorded for each single visit . Visits to the corner could be accompanied by a sound , depending on the identity of the mouse . A loudspeaker ( 22TAF/G , Seas Prestige ) was located above the corner to present sound stimuli . The sounds presented were generated in MATLAB ( The MathWorks , USA ) at a sampling rate of 48 kHz and consisted of 30 ms pure tones with 5 ms slope , repeated at 3 Hz for the duration of the visit and at variable intensity of 70 dB ± 5 dB ( measured at the center of the corner in the predictable group or the center of the home cage in the random group ) . The sound intensity was calibrated with a Bruël & Kjaer ( 4939 ¼” free field ) microphone . The microphone was placed at different positions within the corner , as well as outside the corner , while pure tones ( 1–40 kHz ) were played at 60–70 dB . Microphone signals were sampled at 96 kHz and analyzed in MATLAB . Tones between 3 kHz and 19 kHz did not show harmonic distortions within 40 dB from the main signal . The sounds presented inside the corner were attenuated by over 20 dB outside the attenuated box . Since little attenuation occurred in the corridor located inside the attenuated box immediately connected to the corner , mice in this location could hear the sound played in the corner . All the experimental groups were first habituated to the Audiobox for 3 days without sound presentation . After the habituation phase , the exposed group heard a fixed-tone pip of a specific frequency for the duration of every visit , regardless of nose-poke activity and water intake . The random group was exposed to a fixed-tone pip in the mouse cage at random intervals . The sound was delivered by a loudspeaker located above the cage and calibrated such that sound intensity in the center of the cage was comparable to that inside the corner . The presentation of the sound was triggered by corner visits of a mouse living in another Audiobox , in a yoke control design . This ensured that the pattern ( mainly at night ) and duration of sound presentation in the cage was comparable to that experienced by each mouse in the predictable group when making corner visits . The control group consisted of age-matched animals that lived during the same amount of time in a different Audiobox without sound presentation . The number of mice reported in Fig 1C–1E corresponds to exposed animals to 16 kHz used for recordings in the inferior colliculus and AC . The sounds used during the exposure phase were fixed for each mouse and replication: 8 , 13 , or 16 kHz , depending on the experiment . One group of animals ( 8 and 13 kHz group ) was exposed in 71% of the visits to 8 kHz and the remaining 29% of the visits to 13 kHz , similar to the preconditioned phase of the LI protocol . The experiment consisted of 4 phases: habituation , safe , exposure , and conditioning [15] . Animals were divided in 3 different groups that differed only in the exposure phase before conditioning . During the habituation phase ( 3 days ) , no sound was presented , and the sliding doors remained open . In the safe phase ( 7 days ) , a safe tone of 8 kHz was paired with every visit to the corner , and the sliding doors opened only after nose-poke . In the exposure phase ( 5 days ) , groups were exposed to different frequencies as follows: ( i ) for the control group , 71% of the visits were paired with an 8 kHz tone , and 29% were paired with a 4 kHz tone; ( ii ) for the predictable group , 71% of the visits were paired with an 8 kHz tone , and 29% were paired with a 16 kHz tone; ( iii ) for the random group , 100% of the visits were paired with 8 kHz , and a 16 kHz tone—played in the home cage—was paired to 29% of the visits of a mouse living in another Audiobox to its corresponding corner . Up to this point , all nose-pokes resulted in access to water independently of the sound played . In the conditioning phase , 71% of visits were paired with an 8 kHz tone , and 29% were paired with a 16 kHz , which was conditioned such that a nose-poke resulted in an air puff and no access to water . During this phase , mice had to learn to avoid nose-poking when they heard 16 kHz ( conditioned visit ) . To assess discrimination performance , the discriminability index ( d’ ) was calculated . d’ used in signal detection theory is defined as d'=Z ( HR ) -Z ( FAR ) , in which Z ( p ) , p ∈ [0 1] is the inverse of the cumulative of the gaussian distribution; HR is the hit rate , in which a hit is the correct avoidance of a nose-poke during a conditioned visit; and FAR is the false alarm rate , in which a false alarm is the avoidance of a nose-poke during a safe visit . Since d’ cannot be calculated when either the hits or the false alarms reach levels of 100% or 0% , in the few cases when this happened , 99% and 1% , respectively , were used for these calculations . Mice were anesthetized with avertin before acute electrophysiological recordings in the inferior colliculus ( induction with 1 . 6 mL/100 grs and 0 . 16 mL/100 grs ip to maintain the level of anesthesia as needed ) . Anesthetized mice were fixed with blunt ear bars on a stereotaxic apparatus ( Kopf , Germany ) . The temperature of the animal was monitored by a rectal probe and maintained constant at 36 °C ( ATC 1000 , WPI , Germany ) . The scalp was removed to expose the skull , and bregma and lambda were aligned vertically ( ± 50 μm ) . A metal head-holder was glued to the skull 1 . 3 mm rostral to lambda to hold the mouse , and the ear bars were removed . To access the left inferior colliculus , a craniotomy of 2 . 8 × 3 mm was made , with the center 1 mm lateral to the midline and 0 . 75 mm caudal to lambda . The inferior colliculus was identified by its position posterior to the transverse sinus and anterior to the sigmoid sinus . The tip of the left inferior colliculus became visible after the craniotomy , and measurements from the rostrocaudal and mediolateral borders were made to place the recording electrode exactly in the middle of the inferior colliculus , targeting the central nucleus . The probe was inserted such that the most dorsal electrode was aligned with the dura ( Fig 2B ) , thus minimizing the error in depth alignment . An error in depth assessment might arise from the topmost recording site ( with a diameter of 13 μm ) not being exactly aligned with dura . Since the electrode sites are visible under microscope , the depth error is unlikely to have been more than ± 25 μm ( half the distance between electrode sites ) . Other measures were in place to ensure reliability of the positioning: ( 1 ) before inserting the probe , bregma and lambda were aligned to the same horizontal plane; ( 2 ) the probe was lowered at a fixed rostrocaudal and mediolateral position with respect to bregma; ( 3 ) the probe angle was 90° with respect to the bregma–lambda plane; ( 4 ) dura was intact; and ( 5 ) penetration was very slow . Extracellular multiunit recordings were made using mainly multielectrode silicon arrays ( Neuronexus Technologies , USA ) of 16 electrode sites in either a single shank ( most data; 177 μm2 area/site and 50 μm spacing between sites ) or 4 shanks ( rostrocaudal analysis; 150 μm intershank spacing ) . Glass-coated single electrodes were used to collect data on exposure to frequencies other than 16 kHz . These were either glass-coated tungsten electrodes with a typical impedance of 900 mOhm and an external diameter of 140 μm ( AlphaOmega , Germany ) or glass-coated platinum/tungsten electrodes with a typical impedance of 1 mOhm ( Thomas Recordings , Germany ) . The electrodes were inserted in the central part orthogonally to the dorsal surface of the inferior colliculus and lowered with a micromanipulator ( Kopf , Germany ) . In the case of single electrodes , recordings were made every 50–100 μm . When multielectrode silicon arrays were used , they were lowered ( at a rate of 100 μm/5 minutes ) until the upper electrode was in contact with the inferior colliculus surface , visualized with a microscope ( 750 μm depth ) . The electrodes were labeled with DiI ( 1 , 1'-dioactedecyl-3 , 3 , 3 , 3'-tethramethyl indocarbocyanide , Invitrogen , Germany ) to allow the reconstruction of the electrode track in postmortem sections using standard histological techniques ( Fig 2B ) . The electrophysiological signal was amplified ( HS-36 or HS-18 , Neuralynx , USA ) and sent to acquisition board ( Digital Lynx 4SX , Neuralynx , USA ) . The raw signal was acquired at 32 kHz sampling rate , band-pass filtered ( 0 . 1–9 , 000 Hz ) , and stored for offline analysis . Recording and visualization were made by Cheetah Data Acquisition System ( Neuralynx , USA ) . The sound was synthesized using MATLAB , produced by an USB interphase ( Octa capture , Roland , USA ) , amplified ( Portable Ultrasonic Power Amplifier , Avisoft , Germany ) , and played in a free-field ultrasonic speaker ( Ultrasonic Dynamic Speaker Vifa , Avisoft , Germany ) located 15 cm horizontal to the right ear . The sound intensity was calibrated at the position of the animal’s right ear with a Bruël & Kjaer ( 4939 ¼” free field ) microphone . Microphone signals were sampled at 96 kHz and analyzed in MATLAB . Tones between 2 kHz and 30 kHz did not show harmonic distortion within 40 dB from the main signal . Sound stimuli consisted of 30 ms pure-tone pips with 5 ms rise/fall slope played at a rate of 2 Hz . We used 24 frequencies ( 3 . 3–24 . 6 kHz , 0 . 125 octave spacing ) at different intensities ( 0–80 dB with steps of 5 or 10 dB ) played in a pseudorandom order . Each frequency-level combination was played 5 times . For the analysis of SNRs , data were bundled in “adjacent” and “tuned” regions . Each of these regions comprised 4 steps in the frequency sweep ( 14 . 6 , 16 , 17 . 6 , and 19 kHz for the tuned; 10 . 3 , 11 . 3 , 12 . 3 , and 13 . 4 kHz for the adjacent region ) and ranges of frequencies with a ΔF of 30% . For the two-tone inhibition protocol , a fixed tone ( 16 kHz , 50 dB ) was played simultaneously with a variable tone of a specific frequency-intensity combination ( 3 . 3–24 . 6 kHz , 0 . 125 octave spacing; 0–80 dB with steps of 5 or 10 dB ) . The stored signals were high-pass filtered ( 450 Hz ) . To improve the SNR in the recordings with the silicon probes , the common average reference was calculated from all the functional channels and subtracted from each channel [87] . Multiunit spikes were then detected by finding local minima that crossed a threshold that was 6 times the median absolute deviation of each channel ( S2A Fig ) . Recorded sites were classified as sound driven when they fulfilled 2 criteria: ( 1 ) Significant evoked responses: a PSTH was built , with 1 ms bin size , combining all the frequencies and the intensities above 30 dB . The overall spike counts over 80 ms windows before and after tone onset were compared ( p < 0 . 05 , unpaired t test ) . ( 2 ) Responses were excitatory: they crossed an empirically set threshold ( evoked spikes–baseline spikes ) of 45 spikes . Responses that were inhibitory ( less evoked spikes than baseline , <10% of cases ) were not used . Using these criteria , 85% of the recorded sites where classified as sound driven . In auditory-driven recording sites and for each testing protocol , the spikes across all the trials for each frequency-intensity combination were summed at 1 ms bins . Evoked firing rates were calculated in an 80 ms window , starting with stimulus onset expressed as spikes per second . This yielded a specific spike rate per each frequency-intensity combination that was used to build iso-intensity tuning curves . The peak in collicular activity for each group was computed by averaging the peak of the tuning curve at 70 dB for each recording site along the tonotopic axis . The BF ( frequency that elicited the best response in a given recording depth ) was selected as that with the highest spike count when responses were summed over all intensities . In the rare cases in which more than one frequency elicited the highest response , the mean was used as BF . The difference in BF along the tonotopic axis was computed as the mean across depths of each individual BF minus the average control BF at each depth . Reliability was calculated for recording sites with a BF within a specific range . For each selected site , reliability was calculated as the percentage of trials in which the BF in the selected range evoked at least 1 spike at 70 dB . The spontaneous activity was calculated as the firing rate within a window of 80 ms previous stimulus onset . The SNR was the ratio between the activity evoked by a specific frequency at 70 dB ( calculated as described above ) and the spontaneous activity . The intensity threshold—the lowest sound intensity that elicited a reliable response—was calculated from the FRA as the lowest sound intensity that elicited a spike count 1 . 5 times higher than the spontaneous activity [88] . The bandwidth at the base , for each sound intensity above threshold , was calculated from the smoothed FRA ( 4-point averaging [88] ) as the width in octaves of the frequencies that evoked at least 20% of the maximum response . The bandwidth at half-maximum , for each sound intensity above threshold , was calculated from the smoothed FRA as the width in octaves of the frequencies that evoked 50% of the maximum response at each intensity level . Only recording sites with a BF of 9 to 16 kHz were included in the analysis to avoid the inclusion of incomplete tuning curves due to the frequency range we used as stimuli . The intensity-specific BF corresponded to the frequency that elicited the strongest response at each sound intensity . Latencies corresponded to the time after sound offset of the first evoked spike . ROC analysis was used to assess the discriminability across frequencies in the tuning curves , across structural tuning curves , and across prepulse frequencies in the behavioral PPI . For the tuning curves ( local tuning ) , we generated response distributions ( perfcurve function , MATLAB ) based on the number of spikes elicited by a given tone across trials ( Fig 5A left ) . The probability that a given frequency f2 will be bigger than a growing criterion of number of spikes will go from 1 to 0 as the criterion traverses the range of spike numbers elicited by f2 ( Fig 5A right ) . For the blue f2 in the figure , the criteria that elicit probabilities above 0 will overlap with those of f1 ( yellow ) , while for the brown f2 , there will be no overlap . The ROC curve will therefore be largest for the comparison between the brown f2 and f1 and shallower for the comparison between the blue f2 and f1 ( Fig 5B ) . The ROC analysis of the structural tuning was based on the variability in the size of the response across depths ( 250 to 750 μm ) , rather than trials , and was calculated for structural tuning curves elicited by individual tone presentations ( trials , Fig 5C ) . The number of spikes was used to generate depth distributions in the same way that the number of trials was used to generate spike distributions for the local tuning . In this case , f1 was either the average structural tuning of 16 kHz or 11 . 3 kHz , while f2 was the trial-by-trial structural tuning of frequencies below f1 . The trial-by-trial ROC values for each frequency were averaged before they were plotted . The ROC analysis for the behavioral data was based on the variability in the startle response across prepulse presentations of a given frequency ( see PPI methods below ) . Distributions were constructed , like for the local tuning , from the individual trial values . For each PPI test , f1 was whatever frequency was the background frequency ( 16 or 11 . 3 kHz ) , and f2 varied across the range of prepulse frequencies . Structural tuning–based classification [32 , 33] was performed as follows . The input to the model is a spike-counts dataset of size S × T × N in which S is the total number of stimuli ( S = 24 frequencies ) , T is the number of repetitions for each stimulus ( T = 5 ) , and N is the number of recorded depths ( N = 14 ) . The vector Vs , t = ( Vs , t1 , … , Vs , tN ) represents a single-trial response of the neural population to stimulus s , in which s goes from 1 to S , and t goes from 1 to T . The model is then “trained” to create individual response templates for each stimulus s calculated by averaging the vector Vs , t over the T − 1 trials in the training set . The single trial left out of the training set is used to generate a prediction and classified as being generated by a given stimulus if the euclidean distance between the single trial and the template corresponding to that stimulus is minimal compared to all the other distances . We classified all S × T single trials using this scheme and summarized the results in a confusion matrix C of size S × S , in which the i , j-th element Ci , j is the fraction of trials with stimulus i being classified as stimulus j . The individual confusion matrices , representing the probability of correctly predicting the actual frequency , were averaged across groups and used to estimate classification accuracy . Animals were placed in a custom-made acrylic chamber of 12 cm long and 4 cm in diameter . Movement was detected by a piezoelectric sensor located below the chamber . The protocol was as previously reported by others [36 , 37] . The experiment was divided in 5 phases following one after the other uninterruptedly . ( 1 ) Chamber habituation: at the start of each session , animals were placed in the test chamber and allowed to habituate for 10 minutes; ( 2 ) Sound habituation: a constant background tone ( f1: 16 kHz , 70 dB SPL ) was played for 5 minutes; ( 3 ) Startle-only trials: 10 startle-only trials were presented on the background of 16 kHz to allow for short-term habituation to the startle sound; ( 4 ) Test phase: 10 pre-pulse trials and 10 startle only trials were presented to assess frequency discrimination; ( 5 ) Startle-only trials: 5 startle-only trials were presented to check for habituation over the duration experiment . Trials consisted of a frequency change from the background tone ( f1 ) to the prepulse tone ( f2 , 80 ms long , 1 ms ramp ) at constant 70 dB SPL ( Fig 1F ) . This was immediately followed by 20 ms broadband noise ( BBN ) at approximately 100 dB , which was in turn followed by the background tone at 70 dB until the following trial in a seamless manner . For the “startle-only trials , ” f1 and f2 were 16 kHz , and for prepulse trials , f2 was 15 . 92 , 15 . 84 , 15 . 68 , 15 . 472 , 15 . 2 , 14 . 72 , 14 , or 8 kHz , corresponding to Δf of 0 . 5% , 1% , 2% , 3 . 3% , 5% , 8% , 12 . 5% , and 50% , respectively , relative to f1 . For animals in which f1 was 11 . 3 kHz , f2 was 11 . 31 , 11 . 25 , 11 . 19 , 11 . 08 , 10 . 93 , 10 . 74 , 10 . 4 , 9 . 89 , or 5 . 65 kHz . Trials had pseudorandom lengths between 8 and 25 seconds . The mouse acoustic startle reflex was measured as the maximal vertical force exerted on the piezo within a 200 ms window starting with the onset of the startle noise , minus the mean of the force for 50 ms before the startle noise . For each animal , the startle-only trials of the test phase and the prepulse trials of each frequency were averaged . The percent of PPI for each prepulse frequency PPI ( % ) was calculated as follows: PPI ( % ) =100×ASRnopps-ASRppsASRnopps , in which ASRnopps is the mean response of the startle-only trials , and ASRpps is the mean response of the prepulse trials for that particular frequency . Discrimination thresholds for each animal , defined as the Δf that caused 50% of inhibition of the maximum response , were calculated from parametric fit to a generalized logistic function ( fit function MATLAB ) [37] PPI=-a2+a1+exp ( b+cΔf ) . Animals with a fit coefficient of the curve ( R2 ) below 0 . 7 were excluded from statistical analysis ( 3 control animals , 2 exposed animals , and 1 random animal ) . Additionally , the pooled data for each group were also fitted to a generalized logistic function . In a subset of the animals and after the surgery in the inferior colliculus , a 4x3 mm craniotomy medial to squamosal suture and rostral of the lambdoid suture was made to expose the left AC . The AC was located dorsal and posterior of the transverse sinus [89] . A small amount of Vaseline was applied to the boundaries of the craniotomy to form a well . A single electrode or a 16-channel multielectrode array was inserted . Evoked responses to the tone pips were constantly monitored . A small amount of volume of phosphate-buffered saline solution ( Sigma , USA ) was applied ( 3–5 μL ) every 10–15 minutes during baseline recordings in the inferior colliculus . Then , 3–5 μL of muscimol were applied over the AC ( 1 mg/mL , dissolved in phosphate-buffered saline solution , Sigma , USA ) . AC evoked activity was monitored using frequency sweeps at 70 dB SPL or BBN of different intensities every 5 minutes . AC was usually inactivated 15–20 minutes after muscimol application . Once cortical inactivation was confirmed , recordings in the inferior colliculus were repeated . Six to 12 days after the beginning of sound exposure ( 8 kHz ) , mice were removed from the Audiobox one at a time for acute electrophysiology . Mice were anesthetized with urethane ( 1 . 32 mg/kg , ip ) and xylazine ( 5 mg/kg , ip ) . Animal temperature was maintained at 36 . 5 °C using a custom-designed heating pad in a soundproof chamber with ambient temperature of 30 °C . A tracheotomy was performed , and the cartilaginous ear canals were removed before the mouse was positioned in a custom-designed head-holder and stereotaxic apparatus . Then , a craniotomy was performed on part of the occipital bone , and part of the cerebellum was aspirated to visualize the superior semicircular canal as a reference point . A glass microelectrode filled with 2 M NaCl and 1% methylene blue was advanced in 4 μm steps ( Inchworm micromanipulator , EXFO Burleigh , Germany ) , aiming for the anterior part of the anteroventral cochlear nucleus . Extracellular signals were amplified and band-pass filtered ( 300–3 , 000 Hz ) using an ELC-03X amplifier ( NPI Electronic , Tamm , Germany ) . Digitized signals ( TDT system 3 ) were saved for offline analysis using custom-written MATLAB software . Once a sound-responsive neuron was isolated , the spontaneous rate , CF , and best threshold were determined as described by Jing and colleagues [90] . Unit classification was based on the response pattern to 200 repetitions of 50 ms tone burst at CF ( 2 . 5 ms cos2 rise/fall , 10 Hz repetition rate ) , as described by Taberner and Liberman [91] . The analysis for “other cell types” includes mostly chopper units , some onset units , and a few pauser/build-up units . Likewise , responses to 8 kHz tone bursts were recorded , and the receptive area of each unit was mapped using 30 ms tone bursts at 70 dB ( 10 repetitions per sweep , 3 Hz repetition rate ) for a total of 13 frequencies ranging from 4 kHz to 30 kHz . A 4 × 3 mm craniotomy medial to squamosal suture and rostral of the lambdoid suture was made to expose the left AC . The AC was located dorsal and posterior of the transverse sinus [89] . Single-electrode penetrations ( 400–450 μm ) were made along the exposed cortical surface spaced between 200–250 μm . Auditory core fields ( A1 and AAF ) were identified according to their response latencies and tonotopic distribution [89] . Data acquisition and acoustic stimulation were similar as with inferior colliculus recordings . A separate set of mice was used for gene expression analysis . After 3 days of habituation and 7 days of sound exposure in the Audiobox , mice were anesthetized with avertin and killed by cervical dislocation; immediately , the brain was extracted; and both inferior colliculi were dissected and immediately frozen at −80 °C and stored for later analysis . RNA was isolated from inferior colliculi using the RNAeasy Kit ( Qiagen ) , following manufacturer’s instructions . cDNA was synthesized from 1 μg of RNA using the Superscript III Kit ( Invitrogen ) and random nonamer primers . For quantitative real-time PCR , SyBr Green Master Mix kit ( Applied Biosystems , Germany ) was used , and amplification reactions were run on a Roche LC480 Detection System ( 384-well plates ) or 7500 Fast Real-Time PCR System ( 96-well plates ) . Reactions were run in 4 replicates . The efficiency ( E ) of each pair of primers was estimated based on the slope ( m ) of a standard curve of the Ct values from 5 serial logarithmic dilutions of a template cDNA , using the following formula: E=10 ( -1m ) . The goodness of fit ( R2 ) of all the standard curves was >0 . 98 . We used the gene of the ribosomal protein L13a ( rpl13a ) as a reference gene , since it has been reported as the best candidate gene for brain gene expression analysis [92] . The relative expression of Rpl13a showed no change between the three groups tested ( F2 , 17 = 0 . 8 , p = 0 . 47 , n = 7 , 8 , and 5 for exposed , control , and home cage groups , respectively ) . Gene expression relative to the housekeeping gene ( Rpl13a ) was calculated with the method used by [93] , in which corrections for different efficiencies between target gene and housekeeping gene are made: RE=EkhgCThkgEtgCTtg , in which RE is the relative expression , Ekhg is the efficiency of the housekeeping gene , CThkg is the Ct value of the housekeeping gene , Etg is the efficiency of the target gene , and CTtg is the Ct value of the target gene . After testing for normality distribution using the Jarque-Bera test , group comparisons were made using multiple way ANOVAs , accordingly . For experiments with multiple measures per animal , we used mixed-design ANOVA , with mouse identity as a nested random effect . To test the effect of days on frequency representation and collicular activity , we used a linear mixed effects model ( fitlme , MATLAB , with mouse identity as a random effect ) . For data in which normality test failed , a Kruskal-Wallis test or wilcoxon signed rank test for paired data was used . Where possible , post hoc Bonferroni corrections for multiple comparisons were used . Means are expressed ± SEM . Statistical significance was considered if p < 0 . 05 .
Some things are learned simply because they are there and not because they are relevant at that moment in time . This is particularly true of surrounding sounds , which we process automatically and continuously , detecting their repetitive patterns or singularities . Learning about rewards and punishment is typically attributed to cortical structures in the brain and known to occur over long time windows . Learning of surrounding regularities , on the other hand , is attributed to subcortical structures and has been shown to occur in seconds . The brain can , however , also detect the regularity in sounds that are discontinuously repeated across intervals of minutes and hours . For example , we learn to identify people by the sound of their steps through an unconscious process involving repeated but isolated exposures to the coappearance of sound and person . Here , we show that a subcortical structure , the auditory midbrain , can code such temporally spread regularities . Neurons in the auditory midbrain changed their response pattern in mice that heard a fixed tone whenever they went into one room in the environment they lived in . Learning of temporally spread sound patterns can , therefore , occur in subcortical structures .
You are an expert at summarizing long articles. Proceed to summarize the following text: Melioidosis is an important cause of morbidity and mortality in East Asia . Recurrent melioidosis occurs in around 10% of patients following treatment either because of relapse with the same strain or re-infection with a new strain of Burkholderia pseudomallei . Distinguishing between the two is important but requires bacterial genotyping . The aim of this study was to develop a simple scoring system to distinguish re-infection from relapse . In a prospective study of 2 , 804 consecutive adult patients with melioidosis presenting to Sappasithiprasong Hospital , NE Thailand , between1986 and 2005 , there were 141 patients with recurrent melioidosis with paired strains available for genotyping . Of these , 92 patients had relapse and 49 patients had re-infection . Variables associated with relapse or re-infection were identified by multivariable logistic regression and used to develop a predictive model . Performance of the scoring system was quantified with respect to discrimination ( area under receiver operating characteristic curves , AUC ) and categorization ( graphically ) . Bootstrap resampling was used to internally validate the predictors and adjust for over-optimism . Duration of oral antimicrobial treatment , interval between the primary episode and recurrence , season , and renal function at recurrence were independent predictors of relapse or re-infection . A score of <5 correctly identified relapse in 76 of 89 patients ( 85% ) , whereas a score ≥5 correctly identified re-infection in 36 of 52 patients ( 69% ) . The scoring index had good discriminative power , with a bootstrap bias-corrected AUC of 0 . 80 ( 95%CI: 0 . 73–0 . 87 ) . A simple scoring index to predict the cause of recurrent melioidosis has been developed to provide important bedside information where rapid bacterial genotyping is unavailable . Melioidosis , a serious Gram-negative infection caused by Burkholderia pseudomallei , is endemic across much of rural East and South Asia and in northern Australia [1] . The causative organism is present in the environment in these areas and infection is acquired by bacterial inoculation or inhalation . B . pseudomallei causes 20% of community-acquired septicemias in northeast Thailand [2] , and is the most common cause of fatal community-acquired bacteremic pneumonia in Darwin , Australia [3] . Acute melioidosis is treated with parenteral treatment for at least 10 days , followed by oral treatment for 20 weeks [1] . The overall mortality of acute melioidosis is 50% in NE Thailand ( 35% in children ) , and 19% in Australia [1] , [4] . Recurrent infection occurs despite 20 weeks of antimicrobial treatment and is the most important complication in survivors , affecting 13% of Thai patients who survive the primary episode [5] . A study that compared the bacterial genotype of strain pairs isolated during primary and recurrent melioidosis in over one hundred patients demonstrated that three quarters of cases were due to relapse ( paired isolates had the same genotype ) , and one quarter were due to re-infection with a new strain [6] . Clinically this is an important distinction , with implications for epidemiology , investigation and management , but the overwhelming majority of medical centers treating patients with melioidosis in Asia do not have the facilities to perform bacterial genotyping and recurrence is usually considered to be synonymous with relapse . In addition , isolates from the primary episode are usually unavailable because bacterial strains are not routinely frozen . The purpose of this study was to define the association of readily accessible factors with relapse or re-infection , and to use these to develop a simple scoring system to help distinguish the most probable cause of recurrent melioidosis . Study patients were adults ( ≥15 years ) with culture-confirmed recurrent melioidosis who presented to Sappasithiprasong Hospital , Ubon Ratchathani , northeast Thailand between June 1986 and September 2005 and who were included in prospective studies of antimicrobial chemotherapy during this period . The standard of care throughout the study period was inpatient intravenous antimicrobial therapy , followed by a prolonged course of oral drugs . The prospective studies were either trials comparing parenteral antimicrobial regimens or trials comparing oral eradicative treatment regimens , as previously described ( see [7] for list of published trials ) . Patients were followed up for recurrent melioidosis as a secondary outcome for trials comparing parenteral drugs and as a primary outcome for trials comparing oral treatment regimens . Patients with suspected melioidosis were identified by twice-daily active case finding in the medical and intensive care wards . As part of eligibility screening for the clinical trials a history and examination was performed and samples taken for culture from suspected cases ( blood culture , throat swab , respiratory secretions , pus or surface swab from wounds and skin lesions ) . Microbiology specimens were cultured for the presence of B . pseudomallei , as described previously [8] . Additional passive surveillance was undertaken via the diagnostic microbiology laboratory for patients on the surgical and pediatric wards with cultures positive for B . pseudomallei . All isolates were stored in trypticase soy broth with 15% glycerol at −80°C . A history and full clinical examination was performed on all cases of culture proven melioidosis . Details of history , examination , laboratory results , antimicrobial treatment and clinical course were maintained on a password protected computer database . Patients who survived the primary episode received oral eradicative treatment and were followed up monthly for one year , then yearly thereafter . Oral antimicrobial regimens were as described elsewhere [7] . Patients with recurrence were identified from the history , patient notes and by cross-reference with our database . Follow up data in this study was to February 2007 . Ethical permission for all clinical trials was obtained from the Ethical and Scientific Review Subcommittee of Thai Ministry of Public Health . Patients gave written informed consent to participate in the trials . Single isolates obtained from the first and recurrent episode were compared using a combination of PFGE and MLST , as described previously [6] , [9] . Recurrent melioidosis was defined as the development of new symptoms and signs of infection in association with a culture positive for B . pseudomallei following initial response to oral antibiotic therapy . Relapse and re-infection were defined on the basis of typing of isolates from the first and subsequent episode ( s ) . Isolates from the same patient with an identical banding pattern on PFGE were considered to represent a single strain and these patients were classified as having relapse . Isolates from the same patient that differed by one or more bands were examined using a screening approach based on MLST , as described previously [6] . Isolates from the same patient with a different sequence type ( ST ) were classified as representing re-infection , while those with an identical ST were classified as representing relapse . All B . pseudomallei isolates were tested for susceptibility to the antimicrobial drugs used to treat melioidosis ( meropenem , ceftazidime , amoxicillin-clavulanic acid , chloramphenicol , doxycycline and trimethoprim/sulfamethoxazole ( TMP-SMX ) ) . This was performed using the disk diffusion method with the exception of TMP-SMX , which was assessed using the Etest ( AB Biodisk , Solna , Sweden ) [10] . All isolates defined as intermediate or resistant to a given drug by disk diffusion were tested further using the E-test . Interpretative standards were based on CLSI guidelines , which lists resistance for ceftazidime , amoxicillin-clavulanic acid , doxycycline and TMP-SMX as ≥32 mg/L , ≥32 mg/L , ≥16 mg/l and ≥4/76 mg/L , respectively , and intermediate resistance as 16 mg/L , 16 mg/L , 8 mg/l and N/A , respectively [11] . Diabetes mellitus was defined as either pre-existing , or a new diagnosis as defined by the American Diabetes Association criteria [12] . Impaired renal function was defined as an estimated glomerular filtration rate ( GFR ) below 60 mL/min/1 . 73 m2 at admission . GFR was estimated using an abbreviated form of the Modification of Diet in Renal Disease study equation [13] . Hypotension was defined as a systolic blood pressure less than 90 mmHg , acute renal failure as a 50% decrease in the baseline-calculated GFR [14] , and respiratory failure as the need for mechanical ventilation . The time between the primary episode and recurrent episode was measured from the start of oral antimicrobial therapy to the clinical onset of culture-confirmed recurrent infection . The primary outcome of interest was cause of recurrent infection . Comparison between relapse and re-infection for each variable was performed using Fisher's exact test or the Wilcoxon-Mann-Whitney test , as appropriate . We selected potential predictor variables to study based on our collective clinical experience and information from other studies [5]–[7] . The variables considered included sex , age , diabetes , estimated GFR during recurrent infection , body sites involved in the primary and recurrent episode , complications of recurrent infection , antimicrobial treatment given for the primary episode , patterns of antimicrobial resistance for the primary and recurrent isolates , calendar month of presentation of recurrent episode , and duration between primary and recurrent episode . The creatinine level on recurrent episode was missing for 17 patients ( 12% ) and the most recent creatinine levels during follow-up before the recurrent episode were used instead . Variables associated with relapse/re-infection at p<0 . 20 were included as independent variables in a multivariable logistic regression model with relapse/re-infection as the dependent variable . Variables were removed one at a time from the model if the p-value as determined by the likelihood ratio test was >0 . 05 , least significant variable first . To double check that no significantly predictive variables were removed during this process , each de-selected variable was tested in turn with the final model and reintroduced into the model if p<0 . 05 [15] . Variables in the final model were used to construct a scoring system . For simplicity , estimated GFR was categorized into four levels ( <30 , 30 to <60 , 60 to <90 or ≥90 ) based on clinical practice guidelines [13] . Time to recurrent melioidosis was dichotomized ( <1 year or ≥1 year ) and duration of oral treatment received on primary episode was categorized into four levels ( <8 weeks , 8 to <16 weeks , 16 to 20 weeks or >20 weeks ) based on previous knowledge [6] , [7] . These dummy variables were used in a multivariable logistic regression analysis . The coefficient for each variable was multiplied by 10 and rounded off to the nearest integer . A total score was calculated by summing the points from each variable for each patient , and the results plotted on a receiver-operator characteristic curve . The Hosmer-Lemeshow goodness-of-fit test was used to evaluate the regression model . Discrimination referred to the ability to distinguish re-infection from relapse , and was quantified by the area under receiver operating characteristic curves ( AUC ) . Bootstrap resampling procedures were used to assess the internal validity of the model and to adjust for over-fitting or over-optimism . The apparent performance of the scoring system ( AUC ) on the original data set may be better than the performance in another data set . One thousand random bootstrap samples were drawn with replacement from the original data set . The logistic regression model and scoring system generated from the bootstrap sample was evaluated in the bootstrap sample and in the original sample . The bootstrap sample set represented training data and the original sample set represented test data . The difference between the performances in both sets was an estimate of the optimism in the apparent performance . This difference was averaged to obtain a stable estimate of the optimism . The optimism was subtracted from the apparent performance to estimate the internally validated performance . All analyses were performed using the statistical software STATA/SE version 9 . 0 ( StataCorp LP , College Station , Tx . ) . A total of 2 , 804 adult patients with culture-confirmed melioidosis were seen during the 19-year study period . Of these , 1 , 401 ( 50% ) adult patients died during admission . Of the adults who survived , 1 , 001 ( 71% ) patients presented to follow up clinic at least once . Median duration of follow-up for patients without recurrence was 65 weeks ( 25th percentile-75th percentile , 22–179 weeks; range , 1–954 weeks ) . A total of 194 episodes of culture-confirmed recurrent melioidosis occurred in 170 ( 17% ) patients . Of these , 148 ( 76% ) strain pairs from the primary and recurrent episode were available for genotyping from 141 patients . Bacterial genotyping had been performed previously for 122 episodes in 115 patients [6] , and genotyping of the remainder was performed during this study . Of the 148 episodes of recurrent melioidosis , 98 episodes in 92 ( 65% ) patients were defined by genotyping as relapse . Four of these patients relapsed twice and 1 patient relapsed three times . The other 50 episodes in 49 ( 35% ) patients were due to re-infection . One patient had re-infection after completing treatment for an episode of relapse . For the purposes of this study , only the 141 first episodes of recurrent melioidosis ( 92 relapse and 49 re-infection ) were analyzed . All B . pseudomallei isolates associated with the primary episode of recurrent infection were susceptible to ceftazidime , amoxicillin-clavulanic acid and doxycycline , while 21/141 ( 15% ) were resistant to TMP-SMX . All isolates associated with recurrence were susceptible to ceftazidime . Strains associated with re-infection were resistant to amoxicillin-clavulanic acid , doxycycline and TMP-SMX in 2% ( 1/49 ) , 2% ( 1/49 ) and 16% ( 8/49 ) of cases , respectively , while , strains associated with relapse were resistant in 1% ( 1/92 ) , 1% ( 1/92 ) and 12% ( 11/92 ) , respectively ( p>0 . 05 , all ) . Two patients with relapse associated with the development of bacterial resistance to amoxicillin-clavulanic acid ( MIC from 2 to 16 mg/L ) or doxycycline ( MIC from 1 to 96 mg/L ) received antimicrobial treatment with the respective agent for at least 8 weeks prior to relapse . The majority of patients with re-infection presented in the rainy season , the period of greatest melioidosis incidence , while patients with relapse presented throughout the calendar year without evident seasonality ( p = 0 . 002 , Figure 1A ) . Demographic characteristics and clinical features are shown in Table 1 . Sex and age were comparable between the two groups . Diabetes mellitus was the most common underlying condition in both relapse and re-infection . Impaired renal function was present in 55 ( 60% ) of 92 patients with relapse and 39 of 49 ( 80% ) patients with re-infection ( p = 0 . 02 ) . Distribution of infection and organ involvement during primary infection and at time of recurrence was not different between patients with relapse and re-infection . There was no difference in severity of infection between relapse and re-infection as defined by hypotension , acute renal failure or respiratory failure ( p>0 . 05 in all cases ) . Death occurred in 17 ( 18% ) patients with relapse and 13 ( 27% ) patients with re-infection ( p = 0 . 29 ) . On univariable analysis , the duration of oral antibiotic treatment for the primary episode was significantly shorter for patients with relapse than re-infection ( p<0 . 001 ) . The median time to relapse was also significantly shorter than time to re-infection ( 6 months versus 24 months , p<0 . 001 ) ( Figure 1B ) . On multivariable analysis , significant independent predictors of re-infection were the presence of a low GFR on admission for the recurrent episode , an interval between the primary infection , and recurrence of more than one year and calendar period of presentation ( rainy season ) . Short duration of oral antimicrobial treatment for first episode of infection was predictive for relapse ( Table 2 ) . The AUC for this model was 0 . 81 ( 95% CI: 0 . 74–0 . 89 ) , and the Hosmer-Lemeshow goodness-of-fit test was not significant for lack of fit ( Hosmer-Lemeshow statistics = 9 . 24 , df = 8 , p = 0 . 32 , ) . A scoring system was generated based on a combination of predictors of re-infection or relapse in the final logistic regression model ( Figure 2 ) . Factors associated with re-infection ( time to recurrence more than one year , presentation during the rainy season or with reduced renal function ) were given a positive score . Factors associated with relapse were given a negative score . A non-linear association was found between the duration of oral treatment received and predictive value of relapse . A score was reached based on the accumulation of points from the four variables . The AUC for the re-infection score was 0 . 80 ( 95%CI: 0 . 73–0 . 87 ) after applying the bootstrap correction . The predictive ability of the risk index model for relapse and re-infection is depicted in Figure 3 . A score of less than 5 correctly identified relapse in 76 of 89 patients ( 85% ) in this group , whereas a score of more than or equal to 5 correctly identified re-infection in 36 of 52 patients ( 69% ) . Determining the cause of recurrence in infectious diseases is important as relapse and re-infection have different implications for disease control and clinical management . Relapse reflects treatment failure , in which antimicrobial regimen , elimination of a persistent focus and drug adherence are the main concerns . This contrasts with re-infection , which involves exogenous infection with a new strain and therefore has implications for disease prevention and health education strategies . In clinical practice , cause and management of recurrent infection is highly complex and standard second-line drug regimen may be recommended where individualized retreatment schemes are not practical [16] . In recurrent melioidosis , if all recurrent episodes are assumed to be relapse due to failure of primary eradicative treatment ( TMP-SMX based regimen ) , then inferior secondary treatment ( amoxycillin-clavulanic acid ) may be used despite the presence of an organism that is still sensitive to TMP-SMX [17] , [18] . Use of inferior second-line drugs would unnecessary expose patients with re-infection to a higher risk of relapse from this new episode than would otherwise be the case [7] . In addition , non-medical treatment , the prevention of re-infection , remains ignored . For many infectious diseases , the clinical differentiation of relapse from re-infection is difficult or impossible , and genotyping has generally been used for this purpose . Examples include tuberculosis [19] , [20] , malaria [21] , [22] , Staphylococcus aureus bacteremia [23] , pneumococcal bacteremia [24] , infective endocarditis [25] and nosocomial infections [26] , [27] . Two typing methods were used in this study since MLST can resolve any uncertainty that arises during the interpretation of DNA macrorestriction patterns generated by PFGE [6] . The MLST scheme has been shown to confirm cluster assignments based on PFGE results in common organisms [28]–[30] . However , genotyping techniques are not widely available for tropical infections in endemic areas . In addition , isolates are rarely stored outside of the research setting , making it impossible to compare isolates associated with the primary and recurrent infection . Clinical differences between re-infection and relapse have been proposed for Lyme disease , although a scoring system was not developed [31] . Scoring systems have been described to predict outcome from melioidosis [32] , and to predict a number of other events including atrial fibrillation after cardiac surgery [33] . To our knowledge , our scoring system is the first clinically-based scoring system to differentiate between relapse and re-infection in any infectious disease . It is rapid and simple to use , necessitating data on only four easy to assess factors . This scoring index can be used where bacterial genotyping is unavailable , which covers nearly all melioidosis-endemic regions . The factors associated with recurrent melioidosis are similar to those reported for recurrence of Lyme disease ( relapse after previous inadequate treatment and within a short period , and re-infection during the ‘high’ season when ticks increase in numbers ) [31] , and may represent features that could be used for assessing other infectious diseases . Using genotyping to compare primary and recurrent isolates to distinguish between relapse or re-infection could be confounded by two major factors . First , ‘re-infection’ could actually represent relapse in the event that primary infection was caused by simultaneous infection with more than one bacterial strain , and different strains were picked by chance for genotyping [34] . This is unlikely in melioidosis since infection with more than one strain of B . pseudomallei occurs in less than 2% of cases [35] . ‘Re-infection’ could also actually represent relapse if genetic events occurred in vivo that led to alteration of one of the seven housekeeping genes that are sequenced in order to generate a sequence type . This would be predicted to be extremely unlikely as MLST is based on the sequence of housekeeping genes which are under neutral selection pressure [36] . Second , ‘relapse’ could actually represent re-infection in the event that re-infection was caused by a different strain that was nonetheless indistinguishable on genotyping from the first infecting strain . This would happen when infection sources were clonal or had limited genetic diversity , but this is highly unlikely in melioidosis as the B . pseudomallei population in the environment is extremely diverse [37] . Our finding of a non-linear association between duration of oral treatment received for the primary episode and predictive value of relapse is consistent with a previous analysis; patients treated for more than 20 weeks may have included those with a slow response to treatment or who had more complicated or severe disease associated with a higher risk of treatment failure and relapse [7] . Bacteremia and multifocal infection during the primary episode have been identified as risk factors for relapse compared to patients who did not have relapse [7]; however , these two variables were not significantly different between the relapse and re-infection groups . B . pseudomallei isolates obtain from patients with primary infection and re-infection were not resistant to amoxicillin-clavulanic acid and doxycycline , a finding that is consistent with previous studies [38] , [39] . Acquired antimicrobial resistance in relapse organisms was also uncommon . A number of factors may relate to this: acquired resistant to ceftazidime is infrequent and related to fatal outcome during the acute episode of infection [40]; acquired resistance to carbapenems has never been observed in our patients; and patients who had incomplete treatment with oral eradicative drugs mainly abandoned their treatment due to drug side effects , which may not increase the risk of selection of resistance [1] . This scoring system will not affect prescribing practice relating to the initial treatment of recurrent melioidosis; standard first-line parenteral antimicrobials are recommended for the treatment of both relapse and re-infection as acquired resistance to either ceftazidime or carbapenems is uncommon . In general , first line oral eradicative treatment ( TMP-SMX ) should be used if the organism isolated is susceptible to this drug . However , the scoring system could help to identify the cause of recurrent melioidosis and may lead to individualized oral eradicative treatment and management . Patients with recurrent infection require a detailed history of initial treatment including duration of each drug used and compliance , and any lifestyle modification made by the patient that reduces exposure to environmental B . pseudomallei . For patients with predicted re-infection , first-line eradicative treatment should be used and education provided on prevention of further re-infection . For patients with predicted relapse , efforts should be focused on patient compliance and completion of a course of therapy of adequate duration . The second-line , less effective amoxycillin-clavulanic acid should be used in patients with relapse only where in vivo failure of TMP-SMX is considered possible . We propose that this scoring system can provide timely and important bedside information where bacterial genotyping is unavailable , though it would be important to validate it in different settings , particularly those outside northeast Thailand .
Melioidosis is a serious infectious disease caused by the Gram-negative bacterium , Burkholderia pseudomallei . This organism is present in the environment in areas where melioidosis is endemic ( most notably East Asia and Northern Australia ) , and infection is acquired following bacterial inoculation or inhalation . Despite prolonged oral eradicative treatment , recurrent melioidosis occurs in approximately 10% of survivors of acute melioidosis . Recurrent melioidosis can be caused by relapse ( failure of initial eradicative treatment ) or re-infection with a new infection . The aim of this study was to develop a simple scoring system to distinguish between re-infection and relapse , since this has implications for antimicrobial treatment of the recurrent episode , but telling the two apart normally requires bacterial genotyping . A prospective study of melioidosis patients in NE Thailand conducted between 1986 and 2005 identified 141 patients with recurrent melioidosis . Of these , 92 patients had relapse and 49 patients had re-infection as confirmed by genotyping techniques . We found that relapse was associated with previous inadequate treatment and shorter time to clinical features of recurrence , while re-infection was associated with renal insufficiency and presentation during the rainy season . A simple scoring index to help distinguish between relapse and re-infection was developed to provide important bedside information where rapid bacterial genotyping is unavailable . Guidelines are provided on how this scoring system could be implemented .
You are an expert at summarizing long articles. Proceed to summarize the following text: Genes with male- and testis-enriched expression are under-represented on the Drosophila melanogaster X chromosome . There is also an excess of retrotransposed genes , many of which are expressed in testis , that have “escaped” the X chromosome and moved to the autosomes . It has been proposed that inactivation of the X chromosome during spermatogenesis contributes to these patterns: genes with a beneficial function late in spermatogenesis should be selectively favored to be autosomal in order to avoid inactivation . However , conclusive evidence for X inactivation in the male germline has been lacking . To test for such inactivation , we used a transgenic construct in which expression of a lacZ reporter gene was driven by the promoter sequence of the autosomal , testis-specific ocnus gene . Autosomal insertions of this transgene showed the expected pattern of male- and testis-specific expression . X-linked insertions , in contrast , showed only very low levels of reporter gene expression . Thus , we find that X linkage inhibits the activity of a testis-specific promoter . We obtained the same result using a vector in which the transgene was flanked by chromosomal insulator sequences . These results are consistent with global inactivation of the X chromosome in the male germline and support a selective explanation for X chromosome avoidance of genes with beneficial effects late in spermatogenesis . Sex chromosomes , such as the X and Y chromosomes of Drosophila , are thought to have evolved from a pair of homologous autosomes that lost their ability to recombine with each other [1 , 2] . Over evolutionary time , the sex chromosome that is present only in the heterogametic sex ( the Y in Drosophila and mammals ) tends to degenerate , losing most of its gene complement and accumulating transposable elements [3–6] . The X chromosome , which is still able to recombine within the homogametic sex , maintains a fully functional complement of genes and resembles an autosome in its size , cytogenetic appearance , repetitive element content , and gene density . Recent genomic studies , however , have revealed a number of more subtle differences in gene content , expression pattern , and molecular evolution between the X chromosome and the autosomes [7] . One pattern that has emerged from the genomic analysis of Drosophila melanogaster is that there is a significant excess of gene duplications in which a new autosomal gene has arisen from an X-linked parental gene through retrotransposition [8] . Most of these new autosomal genes appear to be functional and are expressed in testis [8] . Several of these genes that have been studied in detail show evidence of adaptive evolution and/or functional diversification [8–11] . Another pattern that has emerged from functional genomic studies is that genes with male-enriched expression are under-represented on the X chromosome [12 , 13] . For example , about 19% of all D . melanogaster genes reside on the X chromosome , but only 11% of the genes with a 2-fold or greater male bias in expression are X-linked [14] . Furthermore , the male-biased genes that are X-linked tend to show less sex bias in their expression than those that are autosomal [15] . A number of hypotheses have been put forth to explain the above observations [16–18] . To explain the large excess of retrotransposed genes that have “escaped” the X chromosome , Betrán et al . [8] proposed the X inactivation hypothesis , which posits that genes with a beneficial effect late in spermatogenesis are selectively favored to be autosomally located . Otherwise , their expression would be prevented by male germline X inactivation , which is supposed to occur early in spermatogenesis when autosomal genes are still actively transcribed . Early X inactivation could also explain the paucity of genes with male-biased expression on the X chromosome: if X-linked genes cannot be expressed in the later stages of spermatogenesis , then one would expect to see fewer X-linked genes with enriched expression in adult males . In particular , this should be true for genes expressed in the male germline and those encoding sperm proteins , which has been observed [12 , 19] . Male germline X inactivation , however , cannot completely explain the observations . For instance , male-biased genes that are expressed only in somatic cells , where X inactivation does not occur , are also significantly under-represented on the X chromosome [12 , 20] . An alternative explanation that accommodates this observation invokes sexual antagonism , that is , evolutionary conflict between males and females . The fixation probability of an X-linked , sexually antagonistic mutation is expected to differ from that of an autosomal one , with the direction of this difference depending on the dominance coefficient [21 , 22] . If the antagonistic effects are ( at least partly ) dominant , then female-beneficial/male-harmful mutations will accumulate on the X chromosome , while male-beneficial/female-harmful mutations will be removed from the X . This is because the X chromosome spends two-thirds of its evolutionary history in females and , thus , is more often under selection in the background of this sex . Since genes with sex-biased expression may be prime targets for sexually antagonistic mutations , the above scenario could lead to an excess of female-biased genes and a paucity of male-biased genes on the X [13] , resulting in “feminization” or “demasculinization” of this chromosome [12] . A hypothesis that combines the concepts of sexual antagonism and X inactivation was proposed by Wu and Xu [23] . This hypothesis , termed SAXI ( sexually antagonistic X inactivation ) , suggests that natural selection has favored the movement of sexually antagonistic X-linked genes whose expression is beneficial to males to the autosomes , leaving those beneficial to females on the X . Over evolutionary time , the accumulation of female-beneficial/male-harmful genes on the X leads to selection for X inactivation in the male germline , particularly during the later stages of spermatogenesis where the effects of sexual antagonism are expected to be greatest [23] . The hypotheses of Betrán et al . [8] and Wu and Xu [23] assume that the X chromosome becomes inactive before the autosomes during spermatogenesis . This phenomenon has been established in mammals and nematodes [24–26] . However , the evidence for male germline X inactivation in Drosophila has been equivocal . Lifschytz and Lindsley [27] cited cytological observations and genetic experiments to argue that X inactivation during spermatogenesis was common to most animal species with heterogametic males , including D . melanogaster . However , similar evidence was used to argue against X inactivation in Drosophila [28] . A later study of the expression of sperm-specific proteins in transgenic Drosophila provided experimental support for X inactivation [29] . Here the authors used a testis-specific promoter to drive the expression of altered forms of β-tubulins in the male germline and noted that X-linked inserts of the constructs showed reduced expression relative to autosomal inserts . Although this result was consistent with X inactivation , there were some limitations . For instance , the sample sizes were small for each of the expression constructs , with only one or two X-linked inserts per construct . Furthermore , the expression level of the genes was only roughly estimated from protein abundance on electrophoresis gels . A more recent experimental study failed to find support for male germline X inactivation in Drosophila [30] . These authors examined the expression and intracellular location of the MLE protein ( encoded by maleless ) , as well as the acetylation pattern of histone H4 , in male germline cells . Although MLE is known to be involved in X chromosome hypertranscription in somatic cells , presumably through the recruitment of histone acetylation factors [31 , 32] , it does not associate specifically with the X chromosome in male germ cells . Furthermore , H4 acetylation at lysine 16 , which is thought to be a reliable marker for active transcription , was observed equally on the X chromosome and the autosomes . Thus , there was no evidence for dosage compensation or X inactivation in the male germline . However , it is not necessary that these two processes occur through the same mechanism , or that they rely on the same proteins required for somatic cell dosage compensation . Indeed , a microarray analysis of germline gene expression indicated that dosage compensation does occur in the male germline [33] . Because these microarray experiments used reproductive tissues that contained somatic cells and germline cells from all stages of gametogenesis , they could not directly address the issue of early X inactivation . However , the fact that most X-linked genes showed similar levels of expression in both male and female reproductive tissues suggests that , if X chromosome inactivation does occur in the male germline , it does not have a large effect on the global pattern of sex-biased gene expression . In this study , we perform a more rigorous experimental test for X inactivation in the male germline . Using a transgenic construct in which the expression of a reporter gene is driven by the promoter of the autosomal , testis-specific ocnus ( ocn ) gene , we show that autosomal inserts are expressed specifically in males and in testis . X-linked inserts , in contrast , show greatly reduced levels of expression . These results hold for a large sample of independent insertions and for two different transformation vectors and , thus , provide strong support for inactivation of the X chromosome during Drosophila spermatogenesis . The ocn gene is expressed specifically in testis and encodes a protein abundant in mature sperm [19 , 34] . It is part of a cluster of three tandemly duplicated genes on chromosome arm 3R that are present in all species of the D . melanogaster species subgroup and shares greatest homology to the neighboring janusB ( janB ) gene , which is also expressed in testis . Although ocn lies only 250 bp distal to janB , it produces a unique transcript that does not overlap with that of janB [34] . The first half of the janB-ocn intergenic region is highly diverged among species of the D . melanogaster subgroup and cannot be aligned unambiguously . However , the portion just upstream of the ocn start codon is well conserved , suggesting that it has regulatory function ( Figure S1 ) . We refer to this region as the ocn promoter . To test its ability to drive tissue-specific gene expression , we fused it to the open reading frame of the Escherichia coli lacZ gene , which encodes the enzyme β-galactosidase ( Figure 1A ) . Transgenic flies with autosomal insertions of P[wFl-ocn-lacZ] showed reporter gene expression specifically in testis , as expected ( Figure 2 ) . Overall , we obtained 15 independent autosomal insertions of P[wFl-ocn-lacZ] . The mean β-galactosidase activity in adult males was 8 . 67 units , while that in adult females was 0 . 34 units . The difference between the sexes was highly significant ( Mann-Whitney U test , p < 0 . 001 ) . The mean β-galactosidase activity of gonadectomized males was 0 . 24 units , which was significantly less than whole males ( Mann-Whitney U test , p < 0 . 01 ) . If the X chromosome is inactivated before the autosomes during spermatogenesis , then one would expect transgenic lines with X-linked insertions of P[wFl-ocn-lacZ] to show lower levels of reporter gene expression than those with autosomal insertions . This is indeed what we observed . In total , we obtained ten independent X-linked insertions of P[wFl-ocn-lacZ] . All of these lines showed reduced β-galactosidase activity in adult males relative to the autosomal-insertion lines ( Figures 2 and 3 ) . On average , the activity difference between autosomal and X-linked insertions was 7-fold ( 8 . 67 versus 1 . 19 units ) , and the difference between the two groups was highly significant ( Mann-Whitney U test , p < 0 . 001 ) . Although β-galactosidase activity was very low for the X-linked insertions , it was significantly greater than zero . Assuming a normal distribution of activity among the X-insertion lines , the 95% confidence interval was 0 . 82–1 . 56 units . Five of the autosomal insertion lines ( the last five in Figure 3 ) were obtained through the re-mobilization of X-linked inserts ( see Materials and Methods ) , demonstrating that the reduction in expression was not caused by undesired sequence changes in the ocn promoter or lacZ coding sequence , but instead was a direct result of X linkage . Because the assays of β-galactosidase activity measure expression at the level of protein abundance , it is possible that they do not reflect underlying levels of transcription . To test this possibility , we performed quantitative reverse-transcription PCR ( qRT-PCR ) to estimate the relative transcript abundance of a subset of eight transformed lines , including four with autosomal and four with X-linked inserts . The autosomal inserts had significantly higher transgene expression at the level of mRNA ( Mann-Whitney U test , p = 0 . 02 ) , with the relative expression difference being 5-fold ( Figure 4B ) , which corresponds well to the observed difference in β-galactosidase activity and suggests that the enzymatic assays provide a reliable estimate of expression . To test if the reduced expression of the X-linked ocn-lacZ transgenes could be attributed to the presence of localized transcriptional repressors bound to the X chromosome , we performed additional experiments using the P[YEStes-lacZ] transformation vector ( Figure 1B ) , which contains binding sites for the protein encoded by suppressor of Hairy-wing . These binding sites flank the inserted transgene and serve to insulate it from the effects of external transcriptional regulators [35] . We obtained 12 independent autosomal insertions of P[YEStes-lacZ] , and these lines showed male- and testis-specific expression of the lacZ reporter gene . The mean β-galactosidase activity in adult males was 1 . 84 units , which was significantly greater than that of adult females ( mean = 0 . 42; Mann-Whitney U test , p < 0 . 001 ) or gonadectomized males ( mean = 0 . 22; Mann-Whitney U test , p < 0 . 001 ) . We also obtained ten independent insertions of P[YEStes-lacZ] on the X chromosome . Adult males of these lines had a mean β-galactosidase activity of 0 . 17 units , which differed significantly from the autosomal-insert lines ( Mann-Whitney U test , p < 0 . 001 ) , but did not differ significantly from zero ( 95% confidence interval = −0 . 09–0 . 43 ) . The reduction in reporter β-galactosidase activity caused by X linkage was more than 10-fold ( Figure 4A ) . We also assayed expression at the level of transcript abundance by performing qRT-PCR on a subset of eight transformed lines ( four with autosomal and four with X-linked inserts ) . Again , the X chromosome insertion lines showed significantly less transgene expression than the autosomal insertion lines ( Mann-Whitney U test , p = 0 . 02 ) . The reduction in reporter gene expression measured by qRT-PCR was 3 . 4-fold ( Figure 4B ) . Thus , the presence of the chromosomal insulator sequences did not alleviate transcriptional repression of the X-linked transgenes . For adult males with autosomal insertions , the coefficient of variation ( CV ) for β-galactosidase activity was lower among the P[YEStes-lacZ] transformed lines ( CV = 0 . 16 ) than among the P[wFl-ocn-lacZ] transformed lines ( CV = 0 . 28 ) . A more pronounced difference was seen at the level of mRNA abundance , where the CVs for P[YEStes-lacZ] and P[wFl-ocn-lacZ] transformants were 0 . 07 and 0 . 44 , respectively . This suggests that the insulator sequences successfully reduced position effect variation caused by the chromosomal context of the insertion . The P[YEStes-lacZ] transformants , however , showed significantly less β-galactosidase activity than the P[wFl-ocn-lacZ] transformants ( Mann-Whitney U test , p < 0 . 001; Figure 4A ) . Interestingly , this difference was not detectable at the level of mRNA abundance ( Figure 4B ) , which suggests additional , post-transcriptional regulation of the P[YEStes-lacZ] transgenes . Although a number of hypotheses regarding genome and sex chromosome evolution assume that the Drosophila X chromosome becomes transcriptionally inactive before the autosomes during spermatogenesis , little direct evidence for this scenario has been reported . Our experimental results indicate that X chromosome inactivation does occur in Drosophila and that it can have a considerable effect on gene expression in the male germline . In total , we examined 27 autosomal and 20 X-linked insertions of a testis-specific reporter gene in two different transformation vectors . In all cases , transformed lines with autosomal insertions showed significantly greater transgene expression than their X-linked counterparts , with the differences in expression ranging from 3 . 4- to 10-fold . The consistency of these results across a large number of independent insertions suggests that this transcriptional inactivity is a global property of the X chromosome . The fact that we observe the same pattern when using a vector that insulates the transgene from external transcriptional regulators further suggests that inactivation of the X chromosome in the male germline occurs through a major structural change , rather than by the binding of localized transcriptional repressors . Could our results be explained by something other than male germline X inactivation ? One possibility is that there is an insertional bias of our transgenes that differs between the X chromosome and the autosomes . For example , X-linked inserts could preferentially target inactive or heterochromatic regions . To investigate this possibility , we used inverse PCR to map the insertion sites ( Figure 5 ) . We found that the insertions span the euchromatic regions of the X and autosomes , with many being in or near genes ( Table S1 ) . Thus , our mapping results run counter to the expectations of insertional bias as a cause of the observed differences in expression . Another possibility is that insertion of the transgenes onto the X chromosome may cause rearrangements or other disruptions to the gene or promoter that prevent proper expression . However , by remobilizing multiple , independent X inserts to new autosomal locations , we have shown that their expression can be restored . Thus , the X-linked insertions must have been intact . Finally , a lack of proper dosage compensation of transgenes inserted onto the X chromosome could possibly lead to reduced expression . We consider this mechanism unlikely for two reasons . First , X chromosome dosage compensation has been shown to occur on a global level in the Drosophila germline [33] . Second , the expression assays for the autosomal-insert lines were performed on flies heterozygous for the insertion . Thus , even if dosage compensation did not occur , we would expect to observe equal expression of X-linked and autosomal transgenes . Any degree of dosage compensation would result in higher activity in the X-insertion lines , which makes our test conservative . The use of the ocn promoter may make our experimental system especially sensitive to the effects of male germline X inactivation for two reasons . First , the promoter fragment used here is rather short ( 150 bp ) and , thus , may be abnormally influenced by differences in chromatin environment between the autosomes and the X chromosome . It should be noted , however , that other known testis-specific promoters are also relatively short , in the range of 76–390 bp [36–38] . Second , ocn is likely to be expressed relatively late in spermatogenesis , when the effects of X inactivation should be pronounced . The ocn gene was originally identified as one encoding a protein abundant in the testes of mature males , but absent from those of immature males [34] . Our observation that β-galactosidase activity imparted by the ocn-lacZ transgenes is greatest in proximal regions of the testis ( Figure 2 ) also supports its relatively late expression . Furthermore , levels of β-galactosidase activity , as well as transgene transcript abundance as measured by qRT-PCR , are at least 50-fold lower in the third larval instar stage , when spermatogenesis is not yet complete , than in adult males ( unpublished data ) . Thus , it may be that a large proportion of ocn expression occurs after the X chromosome is inactivated . Indeed , if X-linked genes expressed early in spermatogenesis are hypertranscribed through a dosage compensation mechanism [33] , the effects of later X inactivation may be masked . Finally , we wish to point out that , although testis-expressed genes are under-represented on the X chromosome , they are not absent . Thus , many X-linked genes involved in spermatogenesis must be expressed at levels sufficient for proper function . This may be a result of their ( hyper ) transcription early in spermatogenesis . Recently , it has been noted that a region of the X chromosome is enriched for newly evolved , testis-expressed genes [39–41] , which suggests that this region may escape germline X inactivation . One of our transgene inserts falls within ∼500 kb of this interval , but does not differ in expression from other X-linked insertions . A higher density of X-linked transgene insertions may reveal specific regions that escape inactivation . Overall , P[YEStes-lacZ] transformants had much lower β-galactosidase activity than P[wFl-ocn-lacZ] transformants ( Figure 4A ) . This difference was not observable at the level of mRNA ( Figure 4B ) , suggesting additional regulation at the level of translation . Two major differences between the vectors could account for the discrepancy between the enzymatic assays and the qRT-PCR measurements . The first is the suppressor of Hairy-wing chromosomal insulator sequences in P[YEStes-lacZ] ( Figure 1 ) . However , it seems unlikely that these insulator sequences , which lie far outside of the transcriptional unit , would be involved in post-transcriptional regulation . Furthermore , putting the transgenes into a genetic background homozygous for a mutant suppressor of Hairy-wing allele had no effect on levels of β-galactosidase activity ( Figure S2 ) . The second difference is that P[YEStes-lacZ] contains the ocn 3′ untranslated region ( UTR ) ( Figure 1 ) . Although functional information for this 3′ UTR is lacking , the presence of two conserved sequence blocks suggests that it may play a role in the regulation of expression ( Figure S1 ) . Our finding that a testis-specific gene is not properly expressed when located on the X chromosome provides compelling experimental evidence for male germline X inactivation in Drosophila , something that was first proposed over thirty years ago [27] . It is also consistent with a selective explanation for the overabundance of retrotransposed genes that have moved from the X to the autosomes [8] . If such genes have a beneficial effect when expressed in testis ( especially in later stages of spermatogenesis ) , then selection would favor the maintenance of autosomal copies . The acquisition of expression late in spermatogenesis may even predispose a gene to adaptive evolution , as testis-expressed genes appear to be targets of positive selection more often than genes of other expression classes [42] . Our results also have relevance to the SAXI hypothesis [23] , which proposes that sexual antagonism leads to the selective relocation of male-beneficial genes expressed late in spermatogenesis to the autosomes . After all such genes have been relocated , selection could favor global inactivation of the X chromosome during spermatogenesis to prevent the expression of female-beneficial genes that have a harmful effect when expressed in males . Alternatively , the X may be inactive at this stage simply because it no longer contains genes with the proper regulatory sequences required for male germline expression . Our results are consistent with the former scenario , as the ocn promoter , which drives testis-specific expression on autosomes , does not function properly when relocated to the X chromosome . Two different expression vectors that combined the ocn promoter of D . melanogaster with the lacZ coding region of E . coli were generated using standard techniques [43] . For the first , we PCR-amplified a 150-bp fragment of D . melanogaster genomic DNA that spanned bases 25 , 863 , 383–25 , 863 , 532 of Chromosome 3R in genome release 5 ( http://www . fruitfly . org/sequence/release5genomic . shtml ) . The amplified region includes 80 bases of 5′ flanking sequence and 70 bases of 5′ UTR of the ocn gene , corresponding to bases −165 to −16 relative to the A in the ATG start codon . We chose to end the promoter fragment at −16 because the preceeding sequence presented a good target for PCR-primer design; we know of no functional reason to include or exclude the final 15 bp before the start codon . The PCR product was cloned directly into the pCR2 . 1-TOPO vector ( Invitrogen , http://www . invitrogen . com/ ) . The identity and orientation of the cloned fragment were confirmed by restriction analysis . A 3 . 5-kb NotI fragment containing the complete E . coli lacZ open reading frame was excised from the plasmid pCMV-SPORT-βgal ( Invitrogen ) and inserted into the NotI site of the above plasmid , just downstream of the ocn promoter and in the same orientation . A 3 . 6-kb fragment containing the ocn promoter and the lacZ coding region was then excised as an SpeI/XbaI fragment and cloned into the SpeI site of the pP[wFl] transformation vector . This vector is based on the P transposable element and contains the D . melanogaster white ( w ) gene as a selectable marker [44] . The final construct was designated pP[wFl-ocn-lacZ] ( Figure 1A ) . The second expression vector contained the ocn promoter described above as well as the ocn 3′ UTR sequence ( Figure S1 ) . The ocn promoter was excised from the pCR2 . 1-TOPO vector as a BamHI/XbaI fragment and inserted into the BamHI/XbaI sites of the plasmid pUC18 ( Invitrogen ) . The ocn 3′ UTR sequence was PCR-amplified from genomic DNA corresponding to bases 25 , 862 , 721–25 , 862 , 830 of chromosome arm 3R ( bases −16 to +93 relative to the T in the TGA stop codon ) and cloned into the pCR2 . 1-TOPO vector . After confirming the identity and orientation of the cloned fragment by restriction analysis , a HindIII fragment ( where one HindIII site was internal to the 3′ UTR fragment , occurring 30 bp from the 5′ end ) was extracted and inserted into the HindIII site of the pUC18 plasmid containing the ocn promoter , such that the promoter and 3′ UTR were in the same orientation . An SpeI fragment containing both the promoter and the 3′ UTR was then excised and cloned into the XbaI site of the YES vector [35] . This vector is also based on the P transposable element and contains the yellow ( y ) gene of D . melanogaster as a selectable marker . Additionally , it contains binding sites for the Suppressor of Hairy-wing protein that flank the inserted DNA and serve to insulate it from position effects caused by random insertion of the vector into the genome [35] . The resulting transformation vector was designated as YEStes ( YES vector for testes-specific expression ) and contains the ocn promoter and 3′ UTR separated by unique XbaI and NotI restriction sites . To complete the expression construct , a 3 . 5-kb NotI fragment of the plasmid pCMV-SPORT-βgal containing the complete lacZ open reading frame was cloned into the NotI site of the YEStes vector in the appropriate orientation . This final construct was designated pP[YEStes-lacZ] ( Figure 1B ) . Plasmid DNA of the above expression constructs was purified using the QIAprep Spin kit ( QIAGEN , http://www . qiagen . com/ ) and used for microinjection of early stage embryos of the y w; Δ2–3 , Sb/TM6 strain of D . melanogaster following standard procedures [45 , 46] . Because it carries both the y and w mutations , this strain could be used for both transformation vectors . The Δ2–3 P element on the third chromosome served as source of transposase [47] . Following transformation , all lines were crossed to a y w stock to remove the transposase source . In cases where the transgene insertion was linked to the Δ2–3 source of transposase , the inserts were immediately remobilized by crossing transformed males to y w females and selecting offspring carrying the transgene , but not the Δ2–3 element . These flies were then mated to y w flies of the opposite sex to establish stable transgenic lines . X-linked insertions were identified by crossing transformed males to y w females and following inheritance of the phenotypic marker ( y+ or w+ ) ; crosses in which all daughters , but no sons , showed the marker phenotype revealed X linkage . Some X-linked insertions were mobilized to the autosomes by the following procedure . Transformed females were mated to y w; Δ2–3 , Sb/TM6 males and the male offspring carrying both the transgene and the Δ2–3 source of transposase were mated to y w females . From this cross , we selected male offspring carrying the transgene ( which could not be on the X chromosome inherited from the mother ) . These males were mated to y w females to establish stable transformed lines with new autosomal insertions of the transgene . To map the intrachromosomal location of the transgene insertions , the genomic sequence flanking the P-element vector was determined by sequencing the products of inverse PCR [48] . Briefly , genomic DNA was extracted from insertion-bearing flies and digested with either HpaII or HinP1I . The digestion products were self-ligated and used as a template for PCR with primer pairs Pry1 ( 5′-CCTTAGCATGTCCGTGGGGTTTGAAT-3′ ) and Pry2 ( 5′-CTTGCCGACGGGACCACCTTATGTTATT-3′ ) ; Plac1 ( 5′-CACCCAAGGCTCTGCTCCCACAAT-3′ ) and Plac4 ( 5′-ACTGTGCGTTAGGTCCTGTTCATTGTT-3′ ) to determine 3′ or 5′ flanking sequences , respectively . PCR products were sequenced with BigDye v1 . 1 chemistry on a 3730 automated sequencer ( Applied Biosystems , http://www . appliedbiosystems . com/ ) using the PCR primers as sequencing primers . In all cases , the chromosomal locations assigned by inverse PCR were consistent with those determined by genetic crosses . To determine in vivo expression levels of our transgenic constructs , we measured the level of β-galactosidase activity in transformed flies . For all autosomal insert lines , transformed males were mated to y w females and offspring heterozygous for the transgene insertion were used for assays . For transformants with X-linked inserts , females were mated to y w males and offspring heterozygous ( female ) or hemizygous ( male ) for the transgene insertion were used for assays . In all cases , the offspring were collected shortly after eclosion and separated by sex until they were assayed at age 5–7 d . All flies were raised on cornmeal-molasses medium at 25 °C . For assays of β-galactosidase activity , five adult flies of the same sex were homogenized in 150 μl of a buffer containing 0 . 1 M Tris-HCl , 1 mM EDTA , and 7 mM 2-mercaptoethanol at pH 7 . 5 . After incubation on ice for 15 min , the homogenates were centrifuged at 12 , 000 g for 15 min at 4 °C and the supernatant containing soluble proteins was retained . For each assay , 50 μl of this supernatant were combined with 50 μl of assay buffer ( 200 mM sodium phosphate [pH 7 . 3] , 2 mM MgCl2 , 100 mM 2-mecaptoethanol ) containing 1 . 33 mg/ml o-nitrophenyl-β-D-galactopyranoside . β-Galactosidase activity was measured by following the change in absorbance at 420 nm over 30 min at 25 °C . β-Galactosidase activity units were quantified as the change in absorbance per minute multiplied by 1 , 000 ( mOD/min ) . For all transformed lines , we performed at least two technical and two biological replicates ( always in equal numbers ) , where the former used the same soluble protein extraction and the latter used extractions from independent cohorts of flies . The activity of each line was calculated as the mean over all replicates , with the variance and standard error calculated among replicates . For comparisons between chromosomes or vectors , we averaged over the means of the individual lines and used the among-line variation to calculate variance , standard error , and CV . This approach is conservative , as the among-line differences ( position effects ) tended to be the largest source of variation . Statistical tests for differences between groups were performed using nonparametric methods , such as the Mann-Whitney U test , that do not rely on estimates of variance . For our purposes this approach is conservative . For lines that showed β-galactosidase activity in adult males , we also performed assays on gonadectomized males . This was done following the above protocol , after removal of the testes by manual dissection . For visualizing β-galactosidase activity in whole tissues , we incubated dissected testes in the above assay buffer containing 1 mg/ml ferric ammonium citrate and 1 . 8 mg/ml of S-GAL sodium salt ( Sigma-Aldrich , http://www . sigmaaldrich . com/ ) for 6 h at 37 °C . To measure expression at the level of transcription ( mRNA abundance ) , we performed qRT-PCR using a TaqMan assay ( Applied Biosystems ) designed specifically to our transgene ( i . e . , spanning the junction between the ocn 5′ UTR and the lacZ coding region ) . For this , 1 μg of DNase I-treated total RNA isolated from heterozygous ( autosomal insertions ) or hemizygous ( X insertions ) males was reverse transcribed using Superscript II reverse transcriptase and random hexamer primers ( Invitrogen ) according to the manufacturer's protocol . A 1:10 dilution of the resulting cDNA was used as template for PCR on a 7500 Fast Real-Time PCR System ( Applied Biosystems ) . The average threshold cycle value ( Ct ) was calculated from two technical replicates per sample . Expression of the transgene was standardized relative to the ribosomal protein gene RpL32 ( TaqMan probe ID Dm02151827 ) . Relative expression values were determined by the ΔΔCt method according the formula 2− ( ΔCtx − ΔCtmin ) , where ΔCtx = Cttransgene − CtRpL32 for a given transformed line x , and ΔCtmin represents the corresponding value of the line displaying the lowest level of transgene relative to RpL32 expression . Statistical analyses were performed as described above for β-galactosidase activity . The FlyBase ( http://www . flybase . org/ ) accession numbers for the genes discussed in this article are janus B ( CG7931; FBgn0001281 ) , ocnus ( CG7929; FBgn0041102 ) , RpL32 ( CG7939; FBgn0002626 ) , white ( CG2759; FBgn0003996 ) , and yellow ( CG3757; FBgn0004034 ) .
During spermatogenesis , the X chromosome is inactivated in the male germline ( sperm cells ) , thereby silencing , or inactivating , genes residing on the X chromosome . X chromosome silencing is thought to be common among species with XY sex determination and has important implications for genome evolution . For example , genes with increased expression in the male tend to be under-represented on the X chromosome , and many testes-specific genes have been “retrotransposed , ” or moved , from the sex to autosomal chromosomes . However , compelling evidence for X chromosome inactivation in the fruit fly Drosophila has been lacking . Here , we used a transgenic technique to test for male germline X inactivation in this important model organism . We randomly inserted a “reporter gene” whose expression requires a regulatory element for an autosomal testis-specific gene into multiple autosomal and X-chromosomal locations . We found that autosomal insertions of the reporter gene have significantly higher expression in the male germline than X-linked insertions . This pattern holds for two different transgenes with nearly 50 independent insertions , providing strong evidence for X chromosome inactivation during spermatogenesis . The silencing of X-linked gene expression in the male germline may contribute to the observed paucity of male-expressed genes on the X chromosome and the excess of retrotransposed genes that have moved from the X chromosome to the autosomes .
You are an expert at summarizing long articles. Proceed to summarize the following text: Future HIV vaccines are expected to induce effective Th1 cell-mediated and Env-specific antibody responses that are necessary to offer protective immunity to HIV infection . However , HIV infections are highly prevalent in helminth endemic areas . Helminth infections induce polarised Th2 responses that may impair HIV vaccine-generated Th1 responses . In this study , we tested if Schistosoma mansoni ( Sm ) infection altered immune responses to SAAVI candidate HIV vaccines ( DNA and MVA ) and an HIV-1 gp140 Env protein vaccine ( gp140 ) and whether parasite elimination by chemotherapy or the presence of Sm eggs ( SmE ) in the absence of active infection influenced the immunogenicity of these vaccines . In addition , we evaluated helminth-associated pathology in DNA and MVA vaccination groups . Mice were chronically infected with Sm and vaccinated with DNA+MVA in a prime+boost combination or MVA+gp140 in concurrent combination regimens . Some Sm-infected mice were treated with praziquantel ( PZQ ) prior to vaccinations . Other mice were inoculated with SmE before receiving vaccinations . Unvaccinated mice without Sm infection or SmE inoculation served as controls . HIV responses were evaluated in the blood and spleen while Sm-associated pathology was evaluated in the livers . Sm-infected mice had significantly lower magnitudes of HIV-specific cellular responses after vaccination with DNA+MVA or MVA+gp140 compared to uninfected control mice . Similarly , gp140 Env-specific antibody responses were significantly lower in vaccinated Sm-infected mice compared to controls . Treatment with PZQ partially restored cellular but not humoral immune responses in vaccinated Sm-infected mice . Gp140 Env-specific antibody responses were attenuated in mice that were inoculated with SmE compared to controls . Lastly , Sm-infected mice that were vaccinated with DNA+MVA displayed exacerbated liver pathology as indicated by larger granulomas and increased hepatosplenomegaly when compared with unvaccinated Sm-infected mice . This study shows that chronic schistosomiasis attenuates both HIV-specific T-cell and antibody responses and parasite elimination by chemotherapy may partially restore cellular but not antibody immunity , with additional data suggesting that the presence of SmE retained in the tissues after antihelminthic therapy contributes to lack of full immune restoration . Our data further suggest that helminthiasis may compromise HIV vaccine safety . Overall , these findings suggested a potential negative impact on future HIV vaccinations by helminthiasis in endemic areas . Human immunodeficiency virus ( HIV ) and parasitic helminthic worm infections are highly prevalent and geographically overlap each other in sub Saharan Africa ( SSA ) [1 , 2] . A majority of inhabitants harbor at least one or more species of parasitic helminth infection [3–6] and an estimated 50% of the chronically infected individuals living in high-risk rural communities are co-infected with HIV [7] . Furthermore , re-infections after successful treatments are also very common in endemic areas . Therefore , it is very likely that successful future HIV vaccines will be administered to people who already have ongoing helminthiasis or have been previously infected and treated . Current HIV-1 vaccine research suggests that a successful HIV vaccine will need to induce effective T cell and functional antibody responses , where a key component of immune protection would be conferred through a T helper 1 ( Th1 ) immune pathway [8 , 9] . Induction of potent T cell mediated immune responses has previously been demonstrated using heterologous prime-boost vaccination strategies that utilise DNA and viral vaccine vectors such as modified Vaccinia Ankara ( MVA ) [10–14] , while induction of durable antibody immune responses may require immunisation with HIV envelope protein-based vaccines [15–18] . It is widely accepted that an ideal HIV vaccine should induce both anti-HIV cellular responses and HIV Env-specific antibodies to destroy virus-infected cells and neutralize viruses at portals of entry respectively in order to clear the virus before dissemination into the tissues or block viral entry at the mucosal sites [8 , 9 , 19 , 20] . During chronic schistosomiasis , parasite eggs are lodged in the liver and intestinal tissue [21 , 22] resulting in predominantly T-helper 2 ( Th2 ) immune responses [23–27] and the induction of anti-inflammatory regulatory T-cells ( Treg ) which suppress the innate and adaptive T- and B-cell responses [24 , 28 , 29] . This has been shown to lead to general hyporesponsiveness which may adversely impact standard immunizations , by suppressing immune responses to Th1-type vaccine and impairing the expansion of pathogen-specific cytotoxic T lymphocyte ( CTL ) responses [30–37] . Parasitic helminth infections are currently treated with chemotherapeutic drugs such as praziquantel ( PZQ ) for schistosomiasis [38–40] and ivermectin or mebendazole for geohelminths [41–43] , which are cost-effective interventions . However , re-infection after effective treatment is common and frequent in populations in endemic areas [44] . Several animal and clinical studies have reported that helminth infections impair the outcome of a variety of vaccines , including Salmonella [45]; BCG [30 , 46–48] , tetanus [46 , 49–51] , diphtheria toxoid [52] , HBV [53] , pneumococcal [54] and live attenuated oral cholera vaccines [55] . However , elimination of helminth infection has also been shown to at least partially restore this abrogation [56] . Furthermore , individuals treated with antihelminthics show higher frequencies of BCG-specific IFN-γ and IL-12 producing cells than untreated helminth infected individuals [30] . Previous HIV vaccines studies reported reduced vaccine-induced immunity in schistosome-infected mice [57] and partial restoration after elimination of helminths [58 , 59] . However , it is not clear if antibody responses are attenuated as these studies evaluated only cellular responses to Gag as they were monovalent candidate vaccines . Current vaccine candidates and future successful vaccines will likely include multiple immunogens , including Env , in order to broaden the vaccine targets and the capacity to cross-neutralise the majority of transmitted viruses . [60 , 61] . It is well-accepted that helminth-induced Th2 responses play an important role in host protection [62 , 63] . Since Th1 and Th2 display reciprocal antagonist , it would be anticipated that HIV vaccine-generated Th1 responses may reduce host immunity against helminth-associated pathology , thereby compromising the safety of an otherwise effective HIV T-cell vaccine . Poxvirus-vectored HIV vaccines are promising candidates for induction of T cell responses and therefore this is a relevant safety issue which remains under-investigated in the helminthic infection background . We have previously described the development of two multigene candidate vaccines , the SAAVI DNA-C2 and SAAVI MVA-C , which express matched HIV-1 subtype C proteins ( Gag , RT , Tat , Nef and Env ) [11 , 64–66] . These vaccine candidates have been evaluated further in nonhuman primates [18 , 67] and Phase 1 clinical trials [15 , 16] . Also , we have evaluated these vaccines in combination with an HIV-1C gp140ΔV2 Env protein [11 , 15 , 16 , 18 , 64 , 65 , 67] . In the current study , we investigated the impact of chronic schistosomiasis on the immunogenicity of these vaccines in a mouse model and whether the elimination of worms by antihelminthic chemotherapy prior to immunization benefits vaccination outcome . We further investigated whether the S . mansoni eggs ( SmE ) in the absence of active infection , which mimics the state whereby SmE remain trapped in the tissues shortly after antihelminthic treatment , has an adverse effect on vaccine immunogenicity . Lastly , we evaluated helminth-induced pathology to predict HIV vaccine safety in helminth endemic areas . Our findings show that mice infected with S . mansoni displayed reduced magnitudes of vaccine-specific cellular and humoral responses and anthelminthic treatment with PZQ failed to restore levels of anti-gp140 antibodies while partially reversing the adverse impact on cellular responses . Unexpectedly , vaccination with a T-cell based vaccine regimen was observed to worsen helminth-associated pathology suggesting potential safety concerns in future mass HIV vaccination in helminth endemic areas . To assess the impact of Sm-infection on systemic immune responses , we quantified systemic Th1 and Th2 immune responses in uninfected and Sm-infected mice following MVA+gp140 and DNA+MVA vaccination . Con A stimulation of splenocytes from Sm-infected mice resulted in a significantly reduced IFN-γ:IL-4 ratio in DNA+MVA ( p<0 . 05 ) and MVA+gp140 ( p<0 . 01 ) vaccine regimens compared to Sm-uninfected mice ( Fig 1A ) . Similarly , Con A-stimulated splenocytes from Sm-infected mice produced significantly lower ( p<0 . 05 ) levels of IFN-γ and IL-2 ELISpot responses compared to splenocytes from uninfected mice ( Fig 1B ) . Furthermore , Sm-infected mice had a reduced frequency ( p<0 . 05 ) of cytokine-producing CD4+ and CD8+ T cells after re-stimulation of splenocytes with PMA/Ionomycin compared to splenocytes from uninfected control mice ( Fig 1C ) . Moreover , vaccinated Sm-infected mice displayed an impaired type 1 antibody response , indicated by reduced amount of type 1 total antibody isotypes [ ( IgG2a ( p<0 . 001 ) , IgG2b ( p<0 . 001 ) ] and increased type 2 antibody isotype [ ( total IgG1 ( p<0 . 001 ) ( Type 2-associated antibodies ) , IgM ( p<0 . 001 ) ] compared to uninfected but vaccinated mice and uninfected control mice ( Fig 1D ) . Sm-infection was accompanied with increased levels of the regulatory cytokine IL-10 ( S1C and S1I Fig ) . To determine the effect of chronic Sm infection on the HIV-1 vaccine-specific T cell immunity , Sm-infected and uninfected mice were vaccinated with either DNA+MVA or MVA+gp140 vaccine regimens and vaccine-specific T cell responses were determined using ELISpot , CBA and flow cytometry . To determine if elimination of schistosome infection prior to vaccination could reverse the effect on those responses , groups of mice were treated with PZQ before vaccinations . Vaccination with DNA+MVA induced significantly higher cumulative HIV-1 specific IFN-γ ( 2014 ± 177 . 4 SFU/106 splenocytes ) and IL-2 ( 174 . 1 ± 71 . 13 SFU/106 splenocytes ) ELISpot responses in uninfected mice compared to Sm-infected mice ( IFN-γ: 1420 ± 61 . 54 SFU/106 splenocytes and IL-2: 0 SFU/106 splenocytes ) ( Fig 2A and 2B ) . Responses to the RT ( CD8 ) peptide induced the highest number of IFN-γ secreting CD8+ and CD4+ T cells ( 1019 ± 217 . 3 SFU/106 splenocytes ) compared to other peptides in uninfected mice vaccinated with DNA+MVA . However , IL-2 SFU/106 cells were similar among different peptides stimulations . Similarly , vaccination with MVA+gp140 induced significantly higher cumulative HIV-1 specific IFN-γ ( 1838 ± 173 . 3 SFU/106 splenocytes ) and IL-2 ( 197 . 7 ± 20 . 12 SFU/106 splenocytes ) ELISpot responses in uninfected mice compared to Sm-infected mice ( IFN-γ: 1166 ± 132 . 2 SFU/106 splenocytes ) and IL-2: 11 . 89 ± 5 . 951 SFU/106 splenocytes ) ( Fig 2A and 2B ) . Responses to the Env ( CD8 ) peptide induced the highest number of IFN-γ secreting CD8+ and CD4+ T cells ( 553 . 3 ± 55 . 86 SFU/106 splenocytes ) compared to other peptides in uninfected mice vaccinated with MVA+gp140 . However , IL-2 SFU/106 cells were similar among different peptides stimulations . Vaccination after PZQ treatment had varying effects on the magnitudes of ELISpot responses . For DNA+MVA vaccine regimen , the cumulative magnitude of IFN-γ but not IL-2 SFU/106 cells was still significantly lower in treated mice compared with uninfected mice indicating partial restoration of responses to normal magnitudes ( Fig 2A and 2B ) . In contrast , the cumulative magnitudes of both IFN-γ and IL-2 SFU/106 cells between PZQ-treated and vaccinated mice and uninfected mice were similar for MVA+gp140 vaccine regimen , indicating restoration to near normal SFU/106 cells ( Fig 2A and 2B ) . Th1 cytokine levels were significantly reduced in Sm-infected mice . As shown in Fig 2C–2E , significantly higher levels of net cumulative IFN-γ ( 6523 ± 282 . 0 pg/ml ) ; IL-2 ( 84 . 86 ± 0 . 3147 pg/ml ) and TNF-α ( 251 . 2 ± 30 . 33 pg/ml ) ( Fig 2C–2E ) were released by splenocytes from uninfected mice in the DNA+MVA vaccine regimen compared to lower levels of IFN-γ ( 1899 ± 244 . 6 pg/ml ) ; IL-2 ( 23 . 55 ± 4 . 094 pg/ml ) and TNF-α ( 122 . 9 ± 17 . 45 pg/ml ) released in Sm-infected vaccinated mice ( Fig 2C–2E ) . Similarly , for the MVA+gp140 vaccine regimen , significantly higher levels of net cumulative Th1 cytokines: IFN-γ ( 2416 pg/ml ) ; IL-2 ( 63 . 79 pg/ml ) and lower TNF-α ( 112 . 48 pg/ml ) were released from splenocytes of uninfected vaccinated mice compared to lower levels of IFN-γ ( 789 pg/ml ) ; IL-2 ( 4 . 0 pg/ml ) and higher TNF-α ( 123 . 87 pg/ml ) released in Sm-infected vaccinated mice ( Fig 2C–2E ) . After treatment with PZQ , the levels of IFN-γ and IL-2 were observed to be significantly higher compared to Sm-infected mice for both DNA+MVA and MVA+gp140 vaccine regimens but noticeably lower than those of uninfected vaccinated mice , indicating only partial restoration to normal magnitudes . Furthermore , the frequencies of vaccine-specific cytokine ( IFN-γ , IL-2 and TNF-α ) producing T cells as determined by flow cytometry showed a similar general trend whereby lower levels of HIV-specific T cells were detected in Sm-infected mice compared with uninfected animals ( Fig 2F and 2G ) . For both vaccine regimens , Pol- and Env- specific CD4+ T cells were undetectable in Sm-infected vaccinated mice whilst they were readily detected at similar levels in both uninfected and PZQ-treated vaccinated mice indicating restoration of cytokine responses by PZQ treatment ( Fig 2F ) . However , Pol- and Env- specific CD8+ T cells were detected in Sm-infected mice at similar levels as the uninfected and PZQ-treated vaccine group except in the MVA+gp140 vaccine regimen where a significantly higher percentage of cumulative cytokine-producing CD8+ T cells in response to the Pol and Env CD8 peptides stimulation was observed in uninfected vaccinated mice ( 1 . 79 ± 0 . 04% ) compared to Sm-infected vaccinated ( 1 . 49 ± 0 . 06% ) mice . ( Fig 2G ) . Most of the cytokine producing CD8+ and CD4+ T cells belonged to the effector memory phenotype ( Fig 2H ) and the profiles of the memory phenotypes were similar in both uninfected and PZQ-treated vaccinated groups . To determine the effect of helminth infection of the development of Env-specific antibody responses , mice were infected with Sm and vaccinated with MVA+gp140 and humoral responses were determined by ELISA . Uninfected and vaccinated mice produced higher amounts of gp140-specific IgG antibodies compared to Sm-infected vaccinated mice across all IgG isotypes ( IgG1 [1681 ± 373 . 9 vs 140 . 8 ± 29 . 42 AUs]; IgG2a [4746 ± 1154 vs 71 . 14 ± 15 . 98 AUs]; IgG2b [2247 ± 553 . 9 vs 45 . 23 ± 12 . 65 AUs] ) . Treatment of infected mice with PZQ did not restore vaccine-specific antibody responses in infected mice as indicate by significantly lower titers of gp140-specific IgG antibodies across all IgG isotypes ( IgG1 [287 . 0 ± 79 . 96 AUs] , IgG2a [866 . 1 ± 514 . 3 AUs] , IgG2b [126 . 3 ± 28 . 41 AUs] ) compared to uninfected vaccinated control mice ( Fig 3 ) . We next sought to investigate whether Sm eggs ( SmE ) alone are capable of attenuating HIV vaccine-specific responses in the absence of an active Sm infection . To achieve this , we sensitized mice with 2 500 SmE intraperitoneally , challenged them with 2 500 SmE intravenously 14 days later and vaccinated them with the MVA+gp140 vaccine regimen . Cumulative cellular responses to the HIV peptides were measured in the spleens using CBA and ELISpot ( Fig 4A–4E ) and gp140 Env-specific antibodies in the sera ( Fig 4F ) . Cumulative HIV-1 IFN-γ SFU/106 ( Fig 4A ) , and IL-2 SFU/106 ( Fig 4B ) in SmE-inoculated vaccinated mice were noticeably lower , but not significantly when compared to SmE-free vaccinated mice . Similarly , levels of cumulative IFN-γ; TNF-α and IL-2 secreted by splenocytes were noticeably lower in SmE-inoculated vaccinated mice compared to those secreted by splenocytes from SmE-free vaccinated mice ( Fig 4C–4E respectively ) . However , SmE-inoculated mice had significantly lower amounts of gp140-specific IgG1 ( 656 . 8 ± 177 . 1 versus 1203 ± 152 . 0 AUs ) , IgG2a ( 71 . 14 ± 15 . 98 versus 238 . 1 ± 34 . 33 AUs ) , and IgG2b antibodies ( 82 . 73 ± 18 . 20 versus 218 . 3 ± 41 . 86 AUs ) compared to SmE-free mice ( Fig 4F ) , indicating broad attenuation of gp140 Env-specific antibody responses . Furthermore , we confirmed that SmE alone , in the absence of active infection , is capable of skewing the Th1/Th2 profile towards a Th2 response . As shown in Fig 4G , at 9 weeks post inoculation , the IFN-γ:IL-4 ratio was significantly lower in vaccinated SmE-inoculated ( 367 . 0 ± 38 . 34 pg/ml ) compared to SmE-free ( 597 . 2 ± 72 . 93 pg/ml ) vaccinated mice after stimulation with Con A . Similarly , stimulation of splenocytes with SEA resulted in a trend towards reduced IFN-γ:IL-4 ratio for SmE-inoculated mice compared to uninfected but vaccinated mice ( S2 Fig ) . We investigated whether vaccination with DNA+MVA exacerbates helminth associated pathology in chronically infected mice by determining granuloma sizes and hydroxyproline content in mouse livers and assessing hepatosplenomegaly . We also investigated whether treatment with PZQ prior to vaccination with the DNA+MVA regimen ameliorates tissue pathology in infected mice . Sm-infected and vaccinated mice developed significantly larger ( 60 . 27 ± 2 . 37 mm2 ) granulomas when compared to all the other groups ( vaccinated Sm-infected-PZQ treated [41 . 29 ± 1 . 66 mm2]; Sm-infected alone [40 . 79 ± 2 . 38 mm2] and Sm-infected-PZQ treated vaccinated [42 . 36 ± 2 . 26 mm2] ) ( Fig 5A and 5B ) . Sm-infected mice that were either vaccinated or unvaccinated developed hepatosplenomegaly as indicated by larger spleens and livers compared to vaccinated infected mice , naïve mice and vaccinated Sm-infected-PZQ treated mice ( Fig 5C and 5D ) . No difference in hydroxyproline content was observed between unvaccinated Sm-infected and vaccinated Sm-infected mice ( Fig 5E ) . Surprisingly , high levels of hydroxyproline content were observed in unvaccinated Sm-infected-PZQ treated mice compared to Sm-infected ( Fig 5E ) . However , Sm-infected mice had significantly higher number of eggs per gram of liver compared to unvaccinated and vaccinated Sm-infected mice that were treated with PZQ ( Fig 5F ) . The present study investigated the impact of chronic schistosomiasis on the induction of T cell-mediated and antibody responses to candidate HIV vaccines in a mouse model , whether attenuation of vaccine responses can be reversed by pre-vaccination anti-helminthic treatment , and if vaccination with a T cell-based candidate vaccine has an adverse effect on helminth-associated pathology . Firstly , we sought to establish that the mouse model of chronic schistosomiasis worked well as to be expected on our hands . As we expected , our data confirmed that prior to vaccination , Sm-infected mice elicited predominantly Th2 responses and a decreased Th1 cytokine profile ( Fig 1A and 1B; S1 Fig ) , had impaired Th1 cytokine-producing CD8+ and CD4+ T cells ( Fig 1C ) and an increase in Th2 total antibodies in serum ( Fig 1D ) as well as enlarged spleens and livers ( Fig 5C and 5D ) compared to uninfected mice . These findings are consistent with previous reports that demonstrate that Sm infection and a host of other helminths skews the host’s immune responses from a Th1 towards a Th2 type with egg deposition and increased production of IL-4 as key driving forces [23 , 26 , 27 , 63 , 68–72] . Our data also agrees with previous studies that have demonstrated that IL-10 is also responsible for down-regulation of Th1 responses that is observed in schistosome infections [73 , 74] . IL-10 has been shown to mediate this down-regulation via an activation-induced cell death process resulting in apoptosis of CD4+ and CD8+ T cells which is also linked to the onset of egg-laying by the helminth parasite and formation of granulomas [75–77] . Our data also suggested a correlation between the decrease of cytokine-producing CD8+ T cells and levels of IgG2a and IgG2b antibodies observed in unvaccinated Sm and vaccinated Sm-infected mice . Previous studies have reported that cytokines produced by CD4+ and CD8+ T cells play an important role in the regulation of the humoral immune response and isotype switching [78–80] . The immunological consequence of a predominant Th2 biasing demonstrated in Sm-infected mice agrees with the concept of reciprocal antagonism between Th1 and Th2 as previously suggested [62 , 81] . Secretion of IFN-γ and IL-2 by T cells has been associated with suppression of viral replication in HIV-infected individuals and better proliferation of HIV-1-specific CD4+ and CD8+ T cells , suggesting that production of these Th1 cytokines by candidate HIV vaccines is a good indicator of vaccine-mediated immune protection [82] and good indications of polyfunctional CD4+ T cell responses [83] . In this study , we observed that the presence of Sm infection prevented optimal generation of vaccine-specific T cell responses following immunization with SAAVI vaccine candidates ( Fig 2 ) . As shown in Fig 2C–2E , vaccine-specific Th1 cumulative cytokine levels ( IFN-γ , IL-2 and TNF-α ) in recall responses to HIV peptides were significantly reduced in vaccinated Sm-infected compared to Sm-free mice vaccinated with DNA+MVA and MVA+gp140 , with exception to TNF-α in MVA+gp140 vaccinated groups which were similar in both vaccinated Sm-infected and Sm-uninfected but vaccinated mice . Similarly , the magnitudes of vaccine-specific cumulative IFN-γ and IL-2 T cell responses measured by ELISpot were significantly lower in Sm-infected vaccinated mice compared to Sm-uninfected mice vaccinated with DNA+MVA and MVA+gp140 ( Fig 2A and 2B ) . A similar decline has been reported in Sm-infected mice compared to uninfected controls following immunization with a DNA-vectored HIV-1 vaccine [57] . Also , the frequencies of vaccine-specific cytokine-producing CD4+ and CD8+ T cells in Sm-infected mice were observed in the current study ( Fig 2F and 2G ) , which may translate to a decrease in antibody population [84] . However , it was noted that the cytokine-producing T cells were predominantly of the effector memory phenotype in both Sm-infected and uninfected vaccinated mice ( Fig 2H ) . Vaccine-induced effector memory T cells have been associated with protection against mucosal SIV challenge in vaccinated rhesus monkeys [85] . In this study , the downregulation of these vaccine-specific cellular responses demonstrates the ability of Sm infection to negatively affect protective potential of candidate HIV-1 vaccines as have suggested by others [57 , 59] . The antibody responses to the gp140-Env protein were significantly impaired in Sm-infected vaccinated mice ( Fig 3 ) . The mean concentration of anti-gp140 antibodies in Sm-infected mice vaccinated with MVA+gp140 was significantly lower than that in Sm-free vaccinated control for all IgG isotypes ( IgG1; IgG2 and IgG2b ) . Antibody responses to specific HIV antigens have been proposed to correlate with protection [82 , 86]; thus , this was an interesting finding with far-reaching implications for future vaccine development . Elimination of helminth parasites with an antihelminth drug prior to immunization was expected to restore normal vaccine T cell responsiveness as previously demonstrated [59 , 87 , 88] . Our results show that treating mice with PZQ reversed tissue pathology as indicated by reduced spleen and liver weights sizes of granulomas and SmE deposition in the liver tissue in PZQ-treated mice compared with untreated mice ( Fig 5B–5D and 5F ) is consistent with earlier reports [89 , 90] . Treatment with PZQ has been shown to eliminate adult worms with no direct impact on the SmE already trapped in the tissues other than preventing continued egg deposition in treated subjects [38–40] and potentially restoring normal T cell immune responsiveness . However , our immunological data showed that treating mice prior to vaccination only partially restored the hosts’ vaccine-specific T cells responses ( Fig 2 ) . Surprisingly , the partial recovery of these responses did not translate in reduction of the magnitudes of Th2 cytokine responses ( S1 Fig ) . Anti-inflammatory cytokines such as IL-10 remained elevated despite treatment with antihelminth ( S1C and S1I Fig ) whilst previous studies in which PZQ was used reported similar findings [58 , 59] . However , it was unclear if the antibody responses were affected . In our study , Th1-type gp140-Env-specific antibody responses in Sm-infected mice were significantly lower despite treatment with PZQ ( Fig 3 ) . To our knowledge , no study has evaluated the ability of antihelminthic treatment in the restoration of antibody responses to HIV vaccines . However , it is has been suggested that the duration of infection prior and post treatment is an important factor which determines subsequent restoration of normal responses to vaccination [53 , 89 , 90] . A study by Chen et al . , showed a recovery of immune balance 16 weeks post-treatment [53] . Also , findings from the studies conducted by Da’dara’s group and Shollenberger’s groups , demonstrated that normal immune responses can be achieved 2–10 weeks post-treatment [58 , 59] . In contrast to our study , only a 1 . 5-week post-treatment period was allowed prior to commencing the vaccinations . Future studies should investigate varying post-treatment periods including multiple vaccinations to establish the optimal recovery durations to start vaccinations after antihelminthic treatment . This is particularly relevant if there will arise a need to integrate future HIV vaccinations with helminthic worms control programmes to improve the vaccination outcomes in helminth-endemic areas . This study went further to demonstrate that Th1 cellular responses elicited by DNA- and MVA- vectored HIV-1 vaccines exacerbated helminth-induced pathology . Sm-infected mice vaccinated with a DNA+MVA regimen had significantly larger granulomas as well as enlarged spleens and livers compared to Sm-infected unvaccinated groups ( Fig 5B and 5C ) . Treatment significantly reduced the pathology; however , a considerable number of eggs were still present in the liver tissues of treated mice ( Fig 5F ) . Surprisingly , the amount of hydroxyproline content , which is a measure of collagen content , was significantly higher in PZQ-treated uninfected mice compared with unvaccinated Sm-infected groups ( Fig 5E ) , suggesting that PZQ treatment may contribute to increased fibrosis of the liver ( Fig 5E ) as an adverse side effect . A recent study showed that a novel experimental drug ( Paeoniflorin ) used for treating schistosomiasis managed to control sclerosis better than PZQ [91] , pointing to a possible future replacement of PZQ as the antihelminthic drug of choice . Nevertheless , PZQ treatment resulted in reduced number of eggs per gram of liver tissue when compared with Sm-infected untreated mice ( Fig 5F ) . These findings highlighted the scientific challenges in the development of HIV vaccines for SSA , where parasitic helminthiasis is endemic . As discussed above , this study found lack of restoration of vaccine-specific responses upon PZQ-treatment prior to vaccinations while a substantial level of SmE burden was observed in PZQ-treated mice several weeks post-treatment . We , therefore investigated if Sm eggs in the absence of a live infection could result in downregulation of HIV-specific responses . Following an established Sm-egg model [92] , mice were inoculated with S . mansoni eggs and then vaccinated with candidate HIV vaccines to evaluate how these eggs affect vaccination outcomes . The IFN-γ:IL-4 ratio for the SmE-sensitized vaccinated mice was significantly smaller than the unsensitized vaccinated mice ( Fig 4G ) , indicating a considerable elevation of Th2 cytokines and down regulation of Th1 in SmE-inoculated mice comparable to SmE-unsensitized mice . However , this polarized Th2 immune responses appear to have had only partial effects on the vaccine-specific T cell responses ( Fig 4A–4E ) . Although reduced , the decrease in vaccine-specific cellular responses observed in SmE-inoculated mice was not significant . Surprisingly as with Sm live infection , antibody responses to HIV Env-gp140 were significantly reduced in the presence of SmE ( Fig 4F ) . As suggested previously [72] , this finding confirms that SmE trapped in the tissues play a critical role in attenuating the host’s vaccine-specific responses in Sm-infected individual and may explain why both cellular and antibody responses are still suppressed despite treatment in PZQ-treated groups . Thus , the possible mechanism by which Sm infection suppresses these HIV-specific cellular and humoral responses appear to involve the deposition of SmE in the tissues , which stimulates increased production of IL-4 and IL-10 with concomitant polarization of Th2 immune responses . This in turn may promote activation-induced apoptosis of HIV-specific CD4+ and CD8+ T cells resulting in attenuated induction of Th1 immune responses which are key components of HIV vaccine-specific responses . This finding further highlights another challenge that even after antihelminth treatment with PZQ , generation of optimal vaccine responses may not be achieved as helminth eggs left trapped in the tissues could still attenuate HIV vaccine-induced immune responses . In light of these findings , this study suggests that , whilst elimination of worms can offer an affordable and a simple means of antihelminthic treatment , only partially restoration of immune responsiveness to T cell-based vaccines for HIV-1 and other infectious diseases in helminth endemic settings may be achieved . Thus , it would be important to evaluate vaccine delivery systems that can potentially overcome the negative impact of concurrent helminthiasis as previously suggested [93] . An alternative avenue would be the discovery of antihelminthic drugs which are effective in elimination of SmE from the host’s tissues in addition to the elimination of the parasitic worms . Although , this study gives further information on the impact of helminth infection on the immunogenicity of HIV vaccines , not all immunological aspects could be elucidated . Thus , this study justifies further investigations with use of a nonhuman primate model such as baboons ( immune system is highly similar to humans ) to obtain a better understanding of these immune responses . The present study demonstrated that chronic helminth infection is associated with Th2-driven attenuation of both T cell and antibody response to HIV vaccines , and elimination of worm by chemotherapy partially restored T cell responses but not necessarily antibody responses . This study further demonstrated that vaccinating helminth-infected individuals with HIV vaccines that induce strong cellular responses may increase the pathology induced by the parasites , rendering the vaccine unsafe in helminth endemic areas . Lastly , this study suggests that the often-suggested integration of antihelminthic treatment programme with a successful future HIV vaccine might not result in improved vaccination outcome unless alternative antihelminthic drugs with a capacity to eliminate schistosome eggs from tissues are developed . In addition , we recommend that HIV vaccine development programs should consider designing vaccines that can overcome the adverse effects of helminth-induced immunity . Biomphalaria glabrata snails ( Strain NMRI , NR-21962 ) , infected with Schistosoma mansoni ( Strain NMRI ) were provided by the Schistosome Research Reagent Resource Center ( NIAID , NIH , USA ) and maintained in our laboratory for generation of live S . mansoni ( Sm ) cercariae that were used in this study . S . mansoni eggs ( SmE ) were purchased from the Theodor Bilharz Research Institute ( Schistosome Biological Supply Center , Egypt ) and stored at -80°C until use . The integrity and viability of the eggs were evaluated using a light microscope prior to use . Both DNA- and MVA-vectored HIV-1 vaccines have been shown to elicit strong T cell responses in mice [11] , nonhuman primates [18 , 67] and clinical trials [16] . Female BALB/c mice ( 6–8 weeks old ) were purchased from South African Vaccine Producers ( SAVP ) ( Johannesburg , South Africa ) , housed in an Animal Biosafety Level 2 facility at the University of Cape Town and maintained in accordance with the South African National Guidelines for Use of Animals for Scientific Purposes ( SANS Code 10386: 2008 ) which is also in line with EU Directive 2010/63/EU . Experimental protocols performed in this study were reviewed and approved by the Animal Ethics Committee of the University of Cape Town ( UCT AEC: protocol number: 014/026 ) and performed by qualified personnel in compliance with the South African Veterinary Council regulations . A mixture of ketamine hydrochloride and xylazine was used to anaesthesise mice for all procedures that involved intramuscular or intravenous injections , infection with live Schistosoma mansoni cercariae , collection of blood by cardiac puncture and preparation for euthanasia . Euthanasia was done by cervical dislocation while the animals were under anaesthesia ( induced with a mixture of ketamine and xylazine as describe above ) . The study comprised of three experiments . In Experiment 1 and 2 ( Table 1 ) , mice were randomly allocated to six groups ( 5–8 mice per group ) per experiment . Mice receiving exposure to Sm ( 4 groups ) were infected percutaneously via the abdomen with 35 live S . mansoni cercariae at the beginning of the experimentation . Those receiving antihelminthic treatment were given two doses of PZQ ( Sigma Aldrich , USA ) by oral gavage ( 500 mg/kg; diluted in water containing 2% Kolliphor EL [Sigma Aldrich , USA] ) three days apart , between 8 and 8 . 5 weeks post infection . Animals were vaccinated twice , 4 weeks apart , starting at 10 weeks post infection ( Table 1 ) . In Experiment 3 ( Table 2 ) , mice were allocated to 4 groups ( 5 mice per group ) . Two groups were inoculated twice with SmE ( 2500 eggs per mouse ) , 14 days apart , initially by intraperitoneal route , and subsequently by intravenous route . As in Experiments 1 and 2 , mice were vaccinated twice , 4 weeks apart , starting at 1 week after the second inoculation with SmE ( Table 2 ) . Vaccinations with DNA-vectored ( 100μg DNA per mouse ) and MVA-vectored ( 106 plaque forming units per mouse ) HIV-1 vaccines were given intramuscularly . Vaccination with HIV-1 gp140 Env ( 10μg protein per mouse formulated in Imject Alum adjuvant [Thermo Scientific , USA] ) was administered subcutaneously . DNA+MVA vaccine regimens were given as DNA prime and MVA boost vaccine regimens whilst MVA+gp140 Env were given concurrently . Twelve days following the last vaccination , blood was collected by cardiac punctured , mice were euthanised and spleens and livers were harvested for evaluation of HIV immune responses and helminth-induced pathology . Splenocytes were prepared using a standard protocol [95] and stimulated with HIV peptides or mitogen stimulant at 2μg/ml ( S1 Table ) . IFN-γ and IL-2 ELISpot assays were carried out as previously described [11] . Cytometric bead array ( CBA ) assays were carried out as previously described [96] . Intracellular cytokine staining ( ICS ) and flow cytometry analysis was performed as previously described [12 , 14] with minor modifications . Briefly , cells were stained with a viability dye , violet amine reactive dye ( ViViD; Invitrogen , USA ) , at a pre-determined optimal concentration before staining for cell surface molecules with the following fluorochrome-conjugated antibodies: anti-CD3-Alexa 700 , anti-CD4-PE-Cy7 , anti-αCD8-APC-Cy7 , anti-CD62L-APC , and anti-CD44-FITC diluted to 0 . 2μg in staining buffer ( BD Biosciences , USA ) . Further intracellular cytokine staining was done with pooled PE-conjugated anti-TNF ( 0 . 2μg ) anti-IL-2 ( 0 . 06μg ) and anti-IFN-γ ( 0 . 06μg ) antibodies diluted in Perm/Wash buffer ( BD Biosciences , USA ) . To measure the level of HIV gp140 Env-specific antibodies in mouse sera , a standardised ELISA assay was established as previously described [97] . Briefly , ELISA plates were coated with 0 . 5μg/ml of gp140 protein diluted in PBS and incubated overnight at 4°C . Test mouse sera ( diluted 1:1000 ) were tested in duplicates . Mouse sera obtained from unvaccinated mice was used as a negative control while a reference serum sample prepared from mice previously vaccinated with HIV gp140 protein was used in 12 two-fold dilutions , starting at 1:100 , to generate a standard curve . For detection , appropriate secondary anti-mouse antibodies conjugated with horseradish peroxidase were used including the three anti-mouse IgG isotypes ( IgG1; IgG2a and IgG2b; Southern Biotechnology ) . After colour development using tetramethyl-benzidine substrate ( TMB; KPL , USA ) , the optical density ( OD ) was measured at 450nm ( with a reference filter set at 540 nm ) using a microplate reader ( Molecular Devices Corporation , USA ) . Based on the constants of the standard curve generated from the serially diluted reference sample , the reciprocal dilution giving an OD value of 1 ( against gp140 ) was assigned a value of 1000 antibody units ( AUs ) . The negative control ( unvaccinated mouse serum ) was assigned a reciprocal dilution of 0 and zero AUs . A reference sample was used on each ELISA plate to generate a standard curve from which the assigned AUs were used to extrapolate for test samples at a fixed dilution of 1:1000 . A cut-off value for positive antibody responses was set at 2 x the OD value of the negative control serum ( unvaccinated ) and those below the cut of value were assigned an antibody unit of zero . Spleens and livers were weighed prior to processing for immunological evaluation in the laboratory to determine if HIV vaccination worsens helminth-associated pathology . Livers were then fixed in 4% ( v/v ) buffered formalin solution . The fixed samples were then embedded in wax and processed . Sections ( 5–7μm ) were stained with hematoxylin and eosin ( H&E ) ( Sigma Aldrich , USA ) to show aggregation of white blood cells around the Sm eggs and Chromotrop-aniline blue solution ( CAB ) ( Sigma Aldrich , USA ) and counterstained with Weigert's hematoxylin ( Sigma Aldrich , USA ) to stain for collagen . Micrographs of liver granuloma were captured using a Nikon 90i wide-field microscope using a 5 . 0 megapixel colour digital camera running Nikon’s NIS-Elements v . 4 . 30 software ( Nikon Instruments Inc . , USA ) . The area of each granuloma containing a single egg was measured with the ImageJ 1 . 34 software ( National Institutes of Health , USA ) . A total of 25–30 granulomas per slide per mouse were included in the analyses . Data was presented as a mean area of each granuloma containing a single egg . The number of eggs per gram of liver was determined by counting individual eggs from hydrolysed liver under a microscope . Hydroxyproline content , which is a direct measure of collagen content in liver was determined using a modified hydroxyproline protocol by Bergman and Loxley [98] . Briefly , liver samples were weighed , hydrolyzed and added to a 40mg Dowex/Norit mixture . The supernatants were neutralised with 1% phenolphthalein and titrated against 10 M NaOH . An aliquot was mixed with isopropanol and added to chloramine-T/citrate buffer solution ( pH 6 . 5 ) . Erlich’s reagent ( 95% ethanol containing dimethylaminobenzaldehyde ( DMAB ) and concentrated hydrochloric acid ) was added and absorbance was read at 570 nm . Hydroxyproline levels were calculated using 4-hydroxy-L-proline ( Sigma Aldrich , USA ) as a standard , and results were expressed as μmoles hydroxyproline per weight of tissue that contained 104 eggs . Statistical analysis was performed using Prism version 5 . 0 ( GraphPad Software , USA ) . The t-test for independent unpaired non-parametric comparisons was applied to assess the level of significance between means ±SEM . Three independent experiments were conducted and all tests were two-tailed . p values <0 . 05 were considered as significant . The false discovery rate ( FDR ) with Benjamini-Hochberg-adjusted p<0 . 05 was performed as previously described [99] .
Chronic parasitic worm infections are thought to reduce the efficacy of vaccines . Given that HIV and worm infections are common in sub-Saharan Africa ( SSA ) and their geographical distribution vastly overlaps , it is likely that future HIV vaccines in SSA will be administered to a large proportion of people with chronic worm infections . This study examined the impact of S . mansoni worm infections on the immunogenicity of candidate HIV vaccines in a mouse model . S . mansoni worm-infected animals had lower magnitudes of HIV vaccine responses compared with uninfected animals and elimination of worms by praziquantel treatment prior to vaccination conferred only partial restoration of normal immune responses to vaccination . The presence of S . mansoni eggs trapped in the tissues in the absence of live infection was associated with poor vaccine responses . In addition , this study found that effective immunization with some HIV vaccine regimens could potentially worsen worm-associated pathology when given to infected individuals . These novel findings suggest further research in HIV vaccines and future vaccination policies regarding the current clinical vaccines and future HIV vaccination with respect to parasitic worm infections especially in SSA .
You are an expert at summarizing long articles. Proceed to summarize the following text: In 2015 , the mosquito Aedes albopictus was detected in Rabat , Morocco . This invasive species can be involved in the transmission of more than 25 arboviruses . It is known that each combination of mosquito population and virus genotype leads to a specific interaction that can shape the outcome of infection . Testing the vector competence of local mosquitoes is therefore a prerequisite to assess the risks of emergence . A field-collected strain of Ae . albopictus from Morocco was experimentally infected with dengue ( DENV ) , chikungunya ( CHIKV ) , zika ( ZIKV ) and yellow fever ( YFV ) viruses . We found that this species can highly transmit CHIKV and to a lesser extent , DENV , ZIKV and YFV . Viruses can be detected in mosquito saliva at day 3 ( CHIKV ) , day 14 ( DENV and YFV ) , and day 21 ( ZIKV ) post-infection . These results suggest that the local transmission of these four arboviruses by Ae . albopictus newly introduced in Morocco is a likely scenario . Trial registration: ClinicalTrials . gov APAFIS#6573-201606l412077987v2 . Over the past decades , arboviruses caused acute emergences leading to global pandemics . Dengue viruses ( DENV; family Flaviviridae , genus Flavivirus ) are responsible for 390 million infections per year including 96 million symptomatic cases [1] . In 2005 , chikungunya virus ( CHIKV; family Togaviridae , genus Alphavirus ) emerged outside Africa producing devastated outbreaks in all continents [2] . While its importance was underestimated , zika virus ( ZIKV; family Flaviviridae , genus Flavivirus ) hit Brazil in 2015 causing several million cases in the Americas [3] and severe unusual symptoms such as Guillain-Barré syndrome and congenital microcephaly . Despite the availability of an efficient vaccine 17D , yellow fever virus ( YFV; family Flaviviridae , genus Flavivirus ) continues to cause human fatalities in South America and Sub-Saharan Africa . All four arboviruses share the same mosquito vectors: Aedes aegypti and Aedes albopictus . Ae . aegypti is an urban mosquito feeding exclusively on humans [4] and Ae . albopictus colonizes a larger range of sites and feeds on both animals and humans [5] . While Ae . aegypti took several centuries to invade most countries in the world [6] , Ae . albopictus took only a few decades to establish stable colonies worldwide [7] . Native to Southeast Asia , Ae . albopictus has invaded America , Africa and Europe during the last 40 years [8] . In Europe , it was introduced in 1979 in Albania and then in Italy in 1990 . It is now present in 20 European countries [9] . In Africa , Ae . albopictus was first reported in the early 1990s in South Africa [10] and Nigeria [11] . Thereafter , it was described in several West and Central African countries: Cameroon in 2000 [12] , Equatorial Guinea in 2003 [13] , Gabon in 2007 [14] , Central African Republic in 2009 [15] , and Republic of Congo in 2011 [16] . More recently , it was detected in Mali [17] , Mozambique [18] and São Tomé and Príncipe [19] . In North Africa , Ae . albopictus was detected in Algeria in 2010 [20] then in Morocco in 2015 [21] . Morocco is considered a low prevalent country for mosquito-borne diseases [22] . However , since 1996 , the country has faced West Nile virus ( WNV ) with three epizootic episodes: 1996 , 2003 and 2010 [23 , 24] . In 2008 , a serosurvey of wild birds confirmed the circulation of WNV in native birds [25] . Other arboviruses like Usutu virus and Rift valley fever virus ( RVFV ) have never been reported despite serological evidence of RVFV antibodies in camels at the border between Morocco and Mauritania [25–27] . Morocco is considered by several reports of the Intergovernmental Panel on Climate Change ( IPCC ) as a hotspot for climate change with its significant impact for several infectious diseases [28] . The introduction of an invasive species such as Ae . albopictus will likely cause a new public health problem . Moreover , Morocco is a tourist destination with more than 11 million visitors reported in 2017 {http://www . tourisme . gov . ma/fr/tourisme-en-chiffres/chiffres-cles} , increasing the risk of importing arboviral pathogens . In this work , we evaluate the ability of Ae . albopictus recently introduced in Morocco to transmit CHIKV , DENV , ZIKV and YFV , where the outcome of vector infection depends on specific genotype-by-genotype ( G x G ) interactions between a vector population and a pathogen lineage [29] . This measure of the vector competence of field-collected mosquitoes helps to assess the risk of arbovirus emergence . Animals were housed in the Institut Pasteur animal facilities accredited by the French Ministry of Agriculture for performing experiments on live rodents . Work on animals was performed in compliance with French and European regulations on care and protection of laboratory animals ( EC Directive 2010/63 , French Law 2013–118 , February 6th , 2013 ) . All experiments were approved by the Ethics Committee #89 and registered under the reference APAFIS#6573-201606l412077987 v2 . During the national surveillance plan implemented in 2016 to establish the geographical distribution of Ae . albopictus in Morocco , five ovitraps less than 500 m apart were placed on a street of the Agdal neighborhood in Rabat ( 33°59'20 . 9′′ N , 6°51′07 . 9′′W ) . Ovitraps were checked for eggs once a week from May to November 2016 and were brought back to the laboratory to be stored in humid chambers ( relative humidity of 80% ) before being sent to Institut Pasteur in Paris to perform the vector competence studies . After hatching , larvae were split into pans of 200 individuals and supplied every 2 days with a yeast tablet dissolved in 1L of dechlorinated tap water . All immature stages were reared at 26±1°C . Emerging adults were maintained at 28±1°C with a 16L:8D cycle , 80% relative humidity and supplied with a 10% sucrose solution . Females were fed twice a week on anaesthetized mice ( OF1 mice , Charles River laboratories , France ) . Resulting F2 adults were used for vector competence assays . It should be noted that variations of oral susceptibility to an arbovirus can be considered negligible in fewer than five laboratory generations [30] . CHIKV strain ( 06 . 21 ) was isolated from a patient on La Reunion Island in 2005 [31] . After isolation on Ae . albopictus C6/36 cells , this strain was passaged twice on C6/36 cells and the viral stocks produced were stored at -80°C prior to their use for mosquito oral infections . DENV-2 strain provided by Prof . Leon Rosen , was isolated from a human serum collected in Bangkok ( Thailand ) in 1974 [32] and had been passed only in different mosquito species ( 2 times in Ae . albopictus , 2 times in Toxorhynchites amboinensis , and one time in Ae . aegypti ) by intrathoracic inoculation . Viral stocks were obtained by inoculating C6/36 cells . ZIKV strain ( NC-2014-5132 ) originally isolated from a patient in April 2014 in New Caledonia was passaged five times on Vero cells; this strain belongs to the same genotype than the ZIKV strains circulating in Brazil in 2015 [33] . Lastly , a YFV strain ( S79 ) belonging to the West African lineage , was isolated from a human case in Senegal in 1979 [34] . YFV-S79 was passaged twice on newborn mice and two times on C6/36 cells . Six to eight batches of 60 7–10 day old females were exposed to an infectious blood meal containing 1 . 4 mL of washed rabbit erythrocytes and 700 μL of viral suspension . The blood meal was supplemented with ATP as a phagostimulant at a final concentration of 1 mM and provided to mosquitoes at a titer of 107 . 2 plaque-forming unit ( pfu ) /mL for ZIKV , 106 . 5 focus-forming unit ( ffu ) /mL for YFV and 107 ffu/mL for CHIKV and DENV , using a Hemotek membrane feeding system . Mosquitoes were allowed to feed for 15 min through a piece of pork intestine covering the base of a Hemotek feeder maintained at 37°C . Fully engorged females were transferred in cardboard containers and maintained with 10% sucrose under controlled conditions ( 28±1°C , relative humidity of 80% , light:dark cycle of 16 h:8 h ) for up to 21 days with mosquito analysis at 3 , 7 , 14 and 21 days post-infection ( dpi ) . For each virus , 21–30 mosquitoes were examined at each dpi . For each mosquito examined , body ( abdomen and thorax ) and head were tested respectively for infection and dissemination rates at 3 , 7 , 14 and 21 dpi . For this , each part was ground in 250 μL of Leibovitz L15 medium ( Invitrogen , CA , USA ) supplemented with 3% FBS , and centrifuged at 10 , 000×g for 5 min at +4°C . The supernatant was processed for viral titration . Mosquitoes examined previously were also tested for viral transmission by collecting saliva using the forced salivation technique [35] . Mosquitoes were anesthetized on ice and legs and wings were removed . The proboscis was then inserted into a pipette tip containing 5 μL of fetal bovine serum ( FBS ) . After 30 min , the tip content was transferred in 45 μL of L15 medium . Saliva was then titrated to estimate the transmission rate . CHIKV , DENV and YFV were titrated by focus fluorescent assay and ZIKV by plaque forming assay as ZIKV cannot produce distinct viral foci on mosquito cells . For mosquitoes challenged with CHIKV , DENV or YFV , saliva , head and body homogenates were titrated by focus fluorescent assay on Ae . albopictus C6/36 cells [36] . Samples were serially diluted and inoculated onto C6/36 cells in 96-well plates . After an incubation of 3 days for CHIKV , and 5 days for YFV and DENV-2 at 28°C , cells were stained using hyper-immune ascetic fluid specific to each virus as the primary antibody ( CHIKV: provided by the French National Reference Center for Arbovirus at the Institut Pasteur , YFV: OG5 NB100-64510; Novusbio , CO , USA , and DENV: Ms X Dengue complex MAB 8705 , Millipore , MA , USA ) and Alexa Fluor 488 goat anti-mouse IgG ( Life Technologies , CA , USA ) as the secondary antibody . Saliva titers were expressed as ffu/saliva . For ZIKV , body and head suspensions were serially diluted and inoculated onto monolayers of Vero cells in 96-well plates . Cells were incubated for 7 days at 37°C then stained with a solution of crystal violet ( 0 . 2% in 10% formaldehyde and 20% ethanol ) . Presence of viral particles was assessed by CPE detection . Saliva was titrated on monolayers of Vero cells in 6-well plates incubated 7 days under an agarose overlay . Saliva titers were expressed as pfu/saliva . Means , standard deviations , 95% confidence interval were calculated and statistical analyses were performed using the Stata software ( StataCorp LP , Texas , and USA ) . The effect of virus and dpi on infection , dissemination and transmission rates was evaluated using Fisher’s exact test . The titer of viral particles in mosquito saliva was compared across groups using a Kruskall-Wallis non parametric test . P-values<0 . 05 were considered statistically significant . Heatmaps were built under R ( v 3 . 3 . 1 ) ( https://www . R-project . org ) . Mosquito females were exposed to four separate infectious blood meals containing CHIKV , DENV , ZIKV or YFV . The first step after the ingestion of the infectious blood meal is the infection of the midgut which is appraised by calculating the infection rate ( IR ) corresponding to the proportion of mosquitoes with an infected midgut . At 3 dpi , Ae . albopictus Morocco were more infected with CHIKV ( Fig 1; Fisher’s exact test: p<10−4 , df = 3 ) with an IR reaching 93% ( N = 30 ) whereas with the 3 other viruses , IRs were lower than 20% ( N = 30 ) . At 7 dpi , the IR with CHIKV reached 100% ( N = 30 ) and remained significantly lower with DENV ( 60%; N = 30 ) , ZIKV ( 60%; N = 30 ) and YFV ( 26 . 7; N = 30 ) ( Fisher’s exact test: p<10−4 , df = 3 ) . At 14 dpi , mosquitoes become more infected with DENV reaching 90% ( N = 30 ) close to CHIKV ( 86 . 7% , N = 30 ) ( Fisher’s exact test: p = 0 . 69 , df = 3 ) but significantly higher than with ZIKV ( 66 . 7% , N = 30 ) , and YFV ( 20% , N = 30 ) ( Fisher’s exact test: p<10−4 , df = 30 ) . At 21 dpi , the same pattern was observed: IRs were higher with CHIKV ( 90% , N = 30 ) and DENV ( 100% , N = 21 ) than with ZIKV ( 69 . 6% , N = 23 ) and YFV ( 53 . 3% , N = 30 ) ( Fisher’s exact test: p<10−4 , df = 3 ) . IRs with all viruses increased along with dpi except with CHIKV which remained high ( >86% ) very early from 3 dpi . The lowest IRs were obtained with YFV fluctuating from 6 . 7% at 3 dpi to 53 . 3% at 21 dpi . Once the midgut is infected , viral particles can disseminate from the midgut to internal organs and tissues . The dissemination rate ( DR ) gives the number of mosquitoes with infected heads among mosquitoes with infected midgut . At 3 dpi , only CHIKV was detected in mosquito heads ( Fig 2; 28 . 6% , N = 28 ) . At 7 dpi , DR with CHIKV reached 53 . 3% ( N = 30 ) and only 5 . 5% ( N = 18 ) with DENV ( Fisher’s exact test: p<10−4 , df = 3 ) . At 14 dpi , DRs with CHIKV ( 65 . 4% , N = 26 ) and DENV ( 59 . 2% , N = 27 ) were higher and similar ( Fisher’s exact test: p = 0 . 65 , df = 1 ) compared to YFV ( 33 . 3% , N = 6 ) and ZIKV ( 25% , N = 20 ) which were both lower and comparable ( Fisher’s exact test: p = 0 . 69 , df = 1 ) . At 21 dpi , DRs for each virus were significantly different ( Fisher’s exact test: p<10−4 , df = 3 ) and slightly higher than the DRs at 14 dpi . Viral dissemination started earlier with CHIKV at 3 dpi while it was only at 7 dpi with DENV and 14 dpi with YFV and ZIKV . The lowest DRs were obtained with ZIKV maintained at 25% at 14 and 21 dpi . After the virus has spread into the general cavity of the mosquito and infected the salivary glands , the virus must be excreted in saliva for subsequent transmission . The transmission rate ( TR ) is defined as the proportion of mosquitoes delivering infectious saliva among mosquitoes having disseminated the virus ( Fig 3A ) . At 3 and 7 dpi , viral particles could be detected in saliva of mosquitoes infected with CHIKV , with TRs of 37 . 5% ( N = 8 ) and 68 . 7% ( N = 16 ) respectively . At 14 dpi , TR with YFV ( 50% , N = 2 ) predominated over TRs with CHIKV ( 35 . 3% , N = 17 ) and DENV ( 11 . 1% , N = 18 ) , TR with ZIKV remaining at 0%; no significant difference was observed among all TRs ( Fisher’s exact test: p = 0 . 14 , df = 3 ) . At 21 dpi , transmission with ZIKV became detectable with a TR of 50% ( N = 4 ) , not significantly different from TRs with DENV ( 26 . 3% , N = 19 ) , CHIKV ( 17 . 4% , N = 23 ) , and YFV ( 10% , N = 10 ) ( Fisher’s exact test: p = 0 . 36 , df = 3 ) . Transmission started early at 3 dpi with CHIKV , at 14 dpi with DENV and YFV , and at 21 dpi with ZIKV with respectively , a mean of 2 . 06±0 . 60 Log10 ffu/saliva ( N = 3 ) , 0 . 87±0 . 38 Log10 ffu/saliva ( N = 2 ) , 1 . 53 Log10 ffu/saliva ( N = 1 ) , and 2 . 71±0 . 01 Log10 pfu/saliva ( N = 2 ) ( Fig 3B ) . No significant difference was detected between all viruses at 14 dpi ( Kruskal-Wallis test: p = 0 . 47 , df = 2 ) and 21 dpi ( Kruskal-Wallis test: p = 0 . 10 , df = 3 ) . The highest number of viral particles was detected in saliva of mosquitoes infected with YFV and examined at 21 dpi: TR of 50% ( 2 among 4 mosquitoes with viral dissemination ) , 2 females delivering 2 . 70 Log10 pfu ( 500 ) and 2 . 72 Log10 pfu ( 530 ) infectious particles . Whereas IR , DR and TR measure the efficiency of the midgut and salivary glands barriers to modulate , respectively , viral dissemination and transmission , the transmission efficiency ( TE ) gives an overview of transmission potential of mosquitoes tested; it corresponds to the proportion of mosquitoes with infectious saliva among all mosquitoes examined ( presenting or not a viral dissemination with infected heads ) . Fig 4 shows that , the highest TE was detected at 7 dpi with CHIKV , at 21 dpi with DENV , at 14/21 dpi with YFV , and at 21 dpi with ZIKV . Collectively , Ae . albopictus Morocco were more susceptible to CHIKV and secondarily , to DENV , ZIKV and YFV . To summarize the vector competence corresponding to the overall ability of a mosquito population to be infected , to ensure the viral dissemination and to transmit the virus , heatmaps were built ( Fig 5 ) . Ae . albopictus Morocco were better infected with CHIKV from 3 dpi than with DENV and ZIKV ( Fig 5A ) . Mosquitoes ensured an early dissemination ( Fig 5B ) and transmission ( Fig 5C ) with CHIKV ( from 3 dpi ) than with DENV and ZIKV . The species was less susceptible to YFV . Altogether , vector competence of Ae . albopictus Morocco depends on the virus and the dpi: it is more susceptible to CHIKV and susceptibility increases along with the dpi . Using experimental infections , we show that the recently-introduced Ae . albopictus in Morocco were susceptible to all four viruses tested , CHIKV , DENV , YFV and ZIKV . Viral transmission was detected at 3 dpi with CHIKV , 14 dpi with DENV and YFV , and only 21 dpi with ZIKV . Even if DENV , YFV and ZIKV belong to the same genus , they behave differently in Ae . albopictus mosquitoes . Infection of the midgut increases gradually from 3 dpi: DENV infects more efficiently mosquitoes than YFV and ZIKV , YFV remaining the less successful . Dissemination of DENV from the midgut to the mosquito general cavity started at 7 dpi as observed with most populations of Ae . albopictus [37]; it takes a shorter time with Ae . aegypti , i . e . from 5 dpi [38] . DENV dissemination is more strongly inhibited at early dpi than later meaning that the role of midgut as a barrier is diminished with dpi . Transmission of DENV was observed from 14 dpi suggesting an intrinsic incubation period higher than 7 dpi , likely around 10 dpi [37] . With ZIKV and YFV , dissemination was observed only at 14 dpi , YFV spreading at a higher rate than ZIKV suggestive of a stronger role of the midgut barrier with YFV . Transmission was detected at 14 dpi with YFV as observed with other Ae . albopictus populations [39] and 21 days with ZIKV which is longer than expected [40] . CHIKV presents a different profile . This alphavirus infects , disseminates and is transmitted more intensively and more quickly than the three other viruses . This viral strain presents an amino acid substitution ( A226V ) in the envelope glycoprotein E1 [31] favoring the viral transmission by Ae . albopictus [41 , 42] . Importantly , exposure of infected mosquitoes to lower temperatures ( lower than 25°C ) compatible to values recorded in Morocco can modulate transmission [37] . It has been demonstrated that Ae . albopictus were able to better transmit CHIKV at a temperature lower than 28°C [43] . These assessments of vector competence of Ae . albopictus from Morocco to CHIKV , DENV , ZIKV and YFV are important for appraising the risk of local transmission . ZIKV shows the longer extrinsic incubation period ( EIP ) which refers to the time between the uptake of the virus during the blood feeding and the delivery of the virus by vector bite after successful infection and dissemination in the mosquito . If the EIP is longer than the daily survival rate of the mosquito , the risk of transmission is low . By shortening mosquito lifespan , vector control measures reduce disease transmission [44] . However , other factors such as environmental factors , e . g . the temperature , may influence the vector competence [43] . The vector competence and the EIP both contribute to estimating the vector capacity which describes the basic reproductive rate of a pathogen by a vector [44] . A high abundance of the vector [45] , increased contacts between the vector and humans ( i . e . anthropophily of mosquitoes ) [5] and a high proportion of immunologically naïve humans , are also factors that should be considered in estimating the risk of emergence . Introductions of viremic travelers from endemic countries for all these viruses may initiate local transmission and outbreaks . Therefore surveillance of travelers must be reinforced .
The Asian tiger mosquito Aedes albopictus is responsible for the transmission of several arboviruses such as dengue and chikungunya viruses . In 30 to 40 years , it has extended its geographical distribution in both tropical and temperate regions of all continents . The species was first detected in September 2015 , in Rabat , Morocco . Using experimental infections , we demonstrated that Ae . albopictus Morocco are competent to transmit zika and yellow fever viruses in addition to the transmission of dengue and chikungunya viruses . Our results are central to suggest developing the most effective national surveillance program and to designing the most suitable control strategy to avoid the mosquito spreading beyond its point of entry in Morocco .
You are an expert at summarizing long articles. Proceed to summarize the following text: Human malaria parasites proliferate in different erythroid cell types during infection . Whilst Plasmodium vivax exhibits a strong preference for immature reticulocytes , the more pathogenic P . falciparum primarily infects mature erythrocytes . In order to assess if these two cell types offer different growth conditions and relate them to parasite preference , we compared the metabolomes of human and rodent reticulocytes with those of their mature erythrocyte counterparts . Reticulocytes were found to have a more complex , enriched metabolic profile than mature erythrocytes and a higher level of metabolic overlap between reticulocyte resident parasite stages and their host cell . This redundancy was assessed by generating a panel of mutants of the rodent malaria parasite P . berghei with defects in intermediary carbon metabolism ( ICM ) and pyrimidine biosynthesis known to be important for P . falciparum growth and survival in vitro in mature erythrocytes . P . berghei ICM mutants ( pbpepc- , phosphoenolpyruvate carboxylase and pbmdh- , malate dehydrogenase ) multiplied in reticulocytes and committed to sexual development like wild type parasites . However , P . berghei pyrimidine biosynthesis mutants ( pboprt- , orotate phosphoribosyltransferase and pbompdc- , orotidine 5′-monophosphate decarboxylase ) were restricted to growth in the youngest forms of reticulocytes and had a severe slow growth phenotype in part resulting from reduced merozoite production . The pbpepc- , pboprt- and pbompdc- mutants retained virulence in mice implying that malaria parasites can partially salvage pyrimidines but failed to complete differentiation to various stages in mosquitoes . These findings suggest that species-specific differences in Plasmodium host cell tropism result in marked differences in the necessity for parasite intrinsic metabolism . These data have implications for drug design when targeting mature erythrocyte or reticulocyte resident parasites . The malaria-causing apicomplexan parasites Plasmodium spp . have a dynamic life cycle which is reflected in stage-specific morphologies , transcriptomes , proteomes and metabolomes [1–8] . These changes , particularly in their metabolome , reflect the nutritional needs and biological processes of the parasite during intracellular development that in turn influences , or is influenced by , the physiological state of the host cell [6] . Perhaps due to their parasitic life-style , Plasmodium spp . have a simplified and reduced metabolic capacity when compared to higher non-parasitic organisms . They are auxotrophic for purines , vitamins and many amino acids [9 , 10] , but have retained core pathways of carbon metabolism such as glycolysis [11] , the citric acid cycle [7 , 12] , lipid synthesis [13 , 14] , the pentose phosphate pathway [15] , pyrimidine biosynthesis [16] and glycosylation [17] . Plasmodium spp . are obligate intracellular parasites and their metabolism is interlinked with that of their host cell and is heavily dependent on the availability of external nutrients . As a result , intracellular Plasmodium establish systems such as the new permeation pathways with the purpose of accessing host cell and environmental nutrients [18]; in fact the parasite genome encodes >120 predicted membrane transport proteins , a subset of which are located on the plasma membrane [19] . Erythrocyte invasion is a prerequisite for establishment of infection by Plasmodium merozoites and the roles of different merozoite and host surface proteins in this invasion process have been intensively studied [20–25] . Multiple partially overlapping erythrocyte invasion pathways have been described in P . falciparum with consequent functional redundancy [26] . Many Plasmodium spp . including P . falciparum preferentially invade reticulocytes [27] which is also capable of invading and replicating within all stages of erythrocyte development including mature cells . However , P . vivax has a strict requirement for growth in reticulocytes , expresses reticulocyte binding proteins [28] and requires a host Duffy blood group glycoprotein for invasion [29] . P . vivax infection causes accelerated remodelling of very young reticulocytes , a process that normally takes 24 hours in uninfected reticulocytes [30] . The rodent model malaria parasite , P . berghei is also 150 times more likely to invade reticulocytes and establish infection in the presence of equal numbers of mature erythrocytes and reticulocytes [31] and has therefore been long thought of as a suitable model for P . vivax blood stage biology [32] . Mature erythrocytes , comprising almost 98% of the circulating red blood cells , can be considered “simplified” cells; they are metabolically active but lack intracellular organelles found in the bone marrow erythroid precursors cells [33] and enucleated reticulocytes ( maturing erythrocytes ) that are present in peripheral circulation [34] . Reticulocytes undergo many changes after their release into the peripheral circulation as they mature and this is associated with a 20% decrease in total surface area and acquisition of a biconcave shape with consequent increase in shear membrane resistance , the progressive loss of organelles ( mitochondria , ribosomes and lysosomes ) , the loss or reduced abundance of up to 30 membrane proteins , and decreased levels of membrane cholesterol [34 , 35] . This maturation process is associated with a general streamlining of cellular metabolism; mature erythrocytes are highly dependent on glycolysis [36] and the pentose phosphate pathway [37] for both energy and redox balance and lack many other pathways of carbon metabolism , such as citric acid cycle [38] . Reticulocytes are thus expected to contain a richer repertoire of carbon sources and other essential nutrients than mature erythrocytes which might be exploited or even required by reticulocyte preferent Plasmodium spp . Limited comparative metabolomics of the erythroid lineage has been attempted before but focussed on sickle cell disease and cord blood reticulocyte physiology [39 , 40] . Therefore , in order to establish whether there are metabolic differences between reticulocytes and mature erythrocytes that could influence the tropism of different Plasmodium spp . , we undertook a non-targeted , high coverage , comprehensive analysis of the metabolomes of these host cells . Comparison of the metabolomes of very young , uninfected rat and human reticulocytes and their mature erythrocyte counterparts revealed major biochemical differences that could be exploited by intracellular parasite stages . This was tested using reverse genetics to disrupt parasite metabolism and establish the broad ability of P . berghei to utilise the products of reticulocyte metabolism and ( in part ) explain differing profiles of drug susceptibility between parasites in mature erythrocyte and reticulocyte environments . Induction of reticulocytosis was achieved through administration of phenylhydrazine-HCl ( PHZ , 100 mg/kg body weight ) to Wistar rats and cells were harvested when the percentage of reticulocytes in the peripheral blood reached a maximum at day 5 ( ~35% reticulocytes ) . This was monitored by FACS analysis using the reticulocyte surface marker transferrin receptor ( CD71 ) , which is lost as reticulocytes mature[35] . More than 90% of the 35% reticulocyte population generated by PHZ treatment were CD71-high at the time of harvest ( Fig A-A in S1 Text ) corresponding to the youngest of the four forms of reticulocytes that have been identified [39] and are from here on referred to as Reticulocyte enriched Erythrocyte Population ( REP ) Material was also collected for comparison with blood from non-enriched ( ~1% reticulocytes ) animals- wild type Erythrocyte Population ( wtEP ) ( Fig 1A ) . All samples were uniformly depleted of leucocytes . Metabolite extracts of REP and wtEP were analysed in parallel by liquid chromatography mass spectrometry ( LC-MS ) and gas chromatography mass spectrometry ( GC-MS ) , providing overlapping , as well as complementary coverage of the metabolomes of wtEP and REP . LC-MS data was processed using XCMS , MZMatch and IDEOM while GC-MS data was processed using PyMS matrix generation and Chemstation Electron Ionisation ( EI ) spectrum match analysis ( described in detail in methods ) . A total of 333 metabolites were provisionally identified from a total of 4 , 560 mass features and peaks . The volcano plot in Fig 1B shows the distribution of abundance of detected metabolites in REP compared to wtEP . Almost half of all detected metabolites ( 147 , ~45% ) were found to be more than 2-fold more abundant in REP ( with a p<0 . 05 ) ( Fig 1B and A-C in S1 Text and S1 Table ) . Only 5 ( ~1% ) metabolites were over 2-fold more abundant in wtEP than in REP ( with p<0 . 05 ) . The rest of the metabolites did not show a significant difference between REP and wtEP . Similar changes were observed when all mass features and peaks ( ~4 , 560 peaks ) were included in the analyses . Specifically , of the ~4 , 230 unassigned mass features/peaks , 1 , 051 ( ~23% ) were up-regulated and 91 peaks ( ~2% ) down regulated in REP ( Fig A-B in S1 Text ) . As the blood from reticulocytosis-induced rats still contained a major fraction of mature erythrocytes ( 1:2 final ratio of reticulocytes to mature erythrocytes ) the level of metabolite enrichment in reticulocytes was actually much greater ( column four , S1 Table ) . 20 representative metabolites up-regulated in rodent REP showed a similar ‘trend’ towards up-regulation in very young human reticulocytes grown in vitro from CD34+ stem cells [41] analysed using LC-MS ( Fig 1C ) , except carnitine derivatives . All identified metabolites were charted on metabolic pathways known to exist in Plasmodium and mammalian host cell from biochemical studies [6 , 7 , 12 , 42 , 43] and genomic data [44] , although it is expected that not all detected metabolites are endogenously synthesised , as plasma metabolites from other tissues , the microbiome , the diet and environment may also accumulate in erythrocytes . Cell fractions from rodent REP contained elevated levels of glycolytic , pentose phosphate pathway and TCA cycle intermediates ( S1 Table ) . The presence of the latter indicates that reticulocytes have a functional TCA cycle and associated intermediary carbon metabolism , consistent with the presence of a residual population of mitochondria in reticulocytes that are largely lost in mature erythrocytes [34] . Increases in the levels of intermediates of the purine and pyrimidine metabolic pathways in reticulocytes presumably originate either from biosynthesis in the preceding erythropoiesis stages or from catabolism of nucleic acid to their constituent nucleobases [45] . A number of intermediates of phospholipid metabolism were also elevated in reticulocytes compared to mature erythrocytes . Other notable changes included elevated levels of intermediates in glutathione and arginine metabolism in reticulocytes ( S1 Table ) . In addition , many carnitine derivatives were found to be up-regulated in rodent ( although interestingly not in human ) reticulocytes which may relate to fatty acid catabolism by β-oxidation in the mitochondria or peroxisomes of these cells . Although decreased levels of carnitines have previously been found in human erythrocytes derived from normal subjects compared to individuals with Sickle-Cell ( HbSS ) disease [40] , the procedures used for production of rodent reticulocytes ( in vivo ) and human reticulocytes ( in vitro ) cannot be ruled out as the reason for this difference observed between the two species as carnitine is produced in mammalian tissues ( skeletal muscle , heart , liver , kidney , and brain ) [46] a contributory factor missing in in vitro conditions . Almost 65% of the other metabolite ions detected in the HbSS study were also found to be present in erythrocytes in our analysis ( S1 Table ) and around 17% of metabolites detected in our analysis were also reported in erythrocytes in that study [40] . This difference in coverage could be due to the chromatographic and detection methods which differ between the analyses . Taken together these data demonstrate that the reticulocyte contains elevated levels of many metabolites that could potentially be scavenged by the invading malaria parasite . Furthermore , there was a marked overlap in metabolic pathways observed in the reticulocyte and those predicted in the parasite [43 , 44] . Common pathways might therefore be uniquely dispensable to Plasmodium during its growth in the reticulocyte compared with that in mature erythrocytes . To test this hypothesis , we used reverse genetics to target several metabolic pathways in intermediary metabolism and pyrimidine biosynthesis in P . berghei whose intermediates were significantly up-regulated in reticulocytes . Asexual red blood cell stages of Plasmodium spp . catabolize glucose via the intermediary carbon metabolic pathways depicted in Fig 2A and express the cytosolic enzymes , phosphoenolpyruvate carboxylase ( pepc PBANKA_101790 ) , malate dehydrogenase ( mdh PBANKA_111770 ) and aspartate amino transferase ( aat PBANKA_030230 ) . De novo synthesis of aspartate is likely to be important for nucleic acid synthesis as this amino acid is utilised in both purine salvage and as a carbon skeleton in pyrimidine biosynthesis [47] and inhibition of aat has been shown to be lethal to P . falciparum [48] . Malate produced by these pathways either enters mitochondria to participate in the TCA cycle or is excreted [7 , 42] . Metabolites involved in TCA cycle and intermediary carbon metabolism ( ICM ) , including malate and aspartate , were found to be substantially higher in REP compared to wtEP ( Fig 2A ) . The elevated levels of these intermediates may possibly explain the previous observation that disruption of the TCA cycle in P . berghei blood stages through deletion of flavoprotein ( Fp ) subunit of the succinate dehydrogenase , pbsdha ( PBANKA_051820 ) , had little effect on parasite viability in blood stage forms , although ookinete development was impaired [49] . To further explore the possibility that P . berghei has potential access to the anapleurotic substrates of reticulocyte ICM , attempts were made to delete pepc , mdh and aat in P . berghei and assess the importance of these parasite enzymes throughout the life cycle ( Fig 2A ) . P . berghei mutants lacking both pepc and mdh were generated ( Fig B in S1 Text ) , while deletion of aat proved refractory . Both the pepc- and mdh- mutant parasites caused severe cerebral malaria in CD57/B6 mouse model with similar dynamics to wt parasites ( Fig 3B ) . Interestingly , the growth of the pepc- mutant was compromised compared to wild type parasites , as the pepc- mutant , but not the mdh- mutant was overgrown by the wt parasite in an in vivo sensitive single host competitive growth assay ( Fig 3A and C-A in S1 Text ) . The number of merozoites observed in mature schizont stages in both pepc- ( 17 . 02 ± 1 . 8 ) and mdh- ( 17 . 41 ± 1 . 7 ) mutants are similar to wt ( 17 . 4±1 . 8 ) ( Fig C-C in S1 Text ) . Scrutiny of the growth phenotype detected in the pepc- mutants showed that they have a prolonged asexual cycle ( 4 h longer than wt ) ( p<0 . 05 ) ( Fig C-B in S1 Text ) . The number of gametocytes formed in blood stages was also reduced in pepc- mutants by almost 50% but unaffected in mdh- ( p>0 . 05 ) ( Fig 3C ) with no notable difference in male to female ratio in either mutant . Further phenotypic analyses showed reduction of exflagellation ( pepc- mutants 84% less than wt , p<0 . 0005; mdh- mutants 56% less than wt , p<0 . 005 ) ( Fig 3D ) . DNA replication in male gametocytes as observed by FACS analysis was reduced by 50% compared to wt at the 8 minute time point and further delayed taking up to 16 minutes to complete ( Fig C-E and C-D in S1 Text ) . Ookinete development in in vitro cultures of pepc- mutants was also severely affected while in mdh- mutants , ookinetes were formed but the number was reduced by about 50% compared to wt ( Fig 4A ) . To determine if this defect was sex specific , crosses of pepc- and mdh- were performed with P . berghei lines RMgm-348 ( Pb270 , p47- ) which produces viable male gametes but non-viable female gametes and RMgm-15 ( Pb137 , p48/45- ) which produces viable female gametes but non-viable male gametes [50] . Mutants of pepc- were found to produce severely reduced numbers of ookinetes in either cross suggesting that gametes of both genders are affected and that the activity of the protein is essential for viable gamete formation . This was not the case for mdh- mutants where although crossing experiments showed that lack of MDH protein affected both genders , they mimicked the parental phenotype producing 50% fewer mature ookinetes ( Fig 4B ) . The pepc- parasites were defective in development within the mosquitoes , forming small numbers of oocysts in mosquito midguts and no salivary gland sporozoites . However , parasites lacking mdh could complete transmission through the mosquito and infect mice generating blood stage asexual forms in 48–72 hours similar to wt despite producing reduced numbers of oocysts when compared to wt ( Fig 4C and 4D and D-A and D-B in S1 Text ) . Overall , these results suggest that two key enzymes in P . berghei ICM are at least partially redundant during stages of infection in which the parasites resides primarily in reticulocytes , but that they become essential as parasite differentiates and proliferates within other host or vector cell types . Plasmodium spp . are heavily dependent on nucleic acid synthesis during blood stage asexual growth and either salvage ( i . e . purines ) or synthesize ( i . e . pyrimidines ) the requisite bases . A schematic representation of the pyrimidine biosynthesis pathway is given in Fig 2B . Five out of six enzymes of this pathway have been shown to be essential for P . falciparum growth in standard in vitro cultures , based on pharmacological studies [51] . Interestingly , most of these inhibitors are markedly less potent in the in vivo P . berghei model , a feature that has been attributed to reduced bio-availability of inhibitors in mice or apparent differences in target enzyme structures [52 , 53] . However , increased resistance to pyrimidine biosynthetic inhibitors could also reflect higher concentrations of pyrimidine precursors ( bar glutamine ) in the reticulocyte population selectively colonized by this species ( Fig 2B ) [16 , 51] . To investigate this possibility we attempted to delete in P . berghei 6 genes encoding enzymes involved in pyrimidine biosynthesis; carbamoyl phosphate synthetase II ( cpsII ) ( PBANKA_140670 ) , aspartate carbamoyltransferase ( act ) ( PBANKA_135770 ) , dihydroorotase ( dhoase ) ( PBANKA_133610 ) , dihydroorotate dehydrogenase ( dhodh ) ( PBANKA_010210 ) , orotate phosphoribosyltransferase ( oprt ) ( PBANKA_111240 ) and orotidine 5′-monophosphate decarboxylase ( ompdc ) ( PBANKA_050740 ) . While the first four enzymes in this pathway were refractory to deletion , the last two enzymes in pyrimidine biosynthesis , orotate phosphoribosyltransferase ( oprt ) and orotidine 5′-monophosphate decarboxylase ( ompdc ) could be deleted ( Fig B in S1 Text ) . The oprt- and ompdc- mutant parasites grew slowly ( asexual cycle prolonged by approximately 4–5 hours compared to wt ( p<0 . 05 ) ) , were rapidly outgrown in a competition growth assay with wt parasites ( Fig 3A ) and based on gray value-1 of staining intensity as observed by Giemsa staining ( p<0 . 0005 ) , seem to invade very young reticulocytes ( Fig C-E in S1 Text ) . However , these infected reticulocytes could not be classified as CD71-high possibly due to the accelerated loss of the CD71 as observed with P . vivax infected reticulocytes [30] . Furthermore , both oprt- mutants ( 15 . 9 ± 2 . 0 , p<0 . 0005 ) and ompdc- mutants ( 15 . 2 ± 2 . 5 , p<0 . 0005 ) were found to generate , on average , significantly fewer merozoites than wt parasites ( 17 . 5 ± 1 . 8 ) per schizont ( counted after completion of asexual cycle ) ( Fig C-C in S1 Text ) and the asexual parasites also took longer to mature to schizonts ( Fig C-B in S1 Text ) . Both mutants showed altered lethality in the C57/B6 mouse model as the mice infected with the mutants did not manifest the symptoms of experimental cerebral malaria ( ECM ) but died between days 14–20 as a result of severe anaemia and hyperparasitemia ( Fig 3B ) . The process of transmission was also affected by the loss of ompdc and oprt . Gametocytemia was significantly reduced only in oprt- parasites ( Fig 3C ) but no change was seen in male- female ratio . Exflagellation ( the production of mature male gametes ) was found to be severely affected in oprt- and completely blocked in ompdc- parasites ( Fig 3D ) and DNA replication during male gametogenesis was severely reduced ( Fig 3E ) . Consistent with the defects in male gametogenesis , very few ookinetes were formed in in vitro cultures in oprt- parasites and no ookinetes were observed in ompdc- ( Fig 4A ) . Genetic crosses of oprt- and ompdc- mutants were performed as above with P . berghei lines RMgm-348 and RMgm-15 which showed that viable male gametes ( from RMgm-348 ) were able to rescue the ookinete conversion defect in both mutant lines suggesting that formation of male gametes is impaired in both oprt- and ompdc- mutant parasites while female gametes remain unaffected ( Fig 4B ) . Infectivity to the mosquito was significantly reduced in oprt- and completely blocked in ompdc- mutants as seen by observing oocysts in infected mosquito midguts and salivary gland sporozoites ( Fig 4C and 4D and D-C and D-D in S1 Text ) and infection to naïve mice was found to be completely blocked . However , when ookinetes from p47- x oprt- or ompdc- crosses were fed to mosquitoes , they failed to develop into mature oocysts ( Fig E in S1 Text ) hence , did not complete sporogony indicating that lack of both oprt and ompdc in the female lineage results in an allelic insufficiency in a growing oocyst . We also tested the effect of a previously published inhibitor of pyrimidine biosynthesis 5-fluoroorotate ( 5FOA ) [54] on asexual growth of both P . falciparum and P . berghei . The comparisons were carried out in vitro to prevent bioavailability of the inhibitor confounding in vivo data in mice . We tested the activity and found that the IC50 value of 5FOA in vitro was almost 90-fold higher in P . berghei ( 32 . 2 ± 0 . 9 nM ) compared to P . falciparum ( 0 . 37 ± 0 . 01 nM ) ( Fig 5 ) . A dihydroartemisinin control showed no major difference in inhibition between P . berghei ( 6 . 6 ± 0 . 1 nM ) and P . falciparum ( 2 . 8 ± 0 . 2 nM ) . These data strongly suggest that P . berghei can access pyrimidine precursors from the reticulocyte and are consistent with a role of host cell metabolism in the differential activity of 5FOA , although differences in sensitivity of P . falciparum [55] and P . berghei [56] thymidylate synthase or differences in drug uptake could also contribute to the differential lethality . Although a small number of metabolomics studies have been undertaken on erythrocytes , including a comparison of normal and HbSS erythrocytes , the number and range of metabolites detected in these studies were relatively small ( 20–90 ) [39 , 40] . Here we used complementary LC-MS and GS-MS analytical platforms to maximise coverage , generating the most comprehensive coverage of REP and wtEP undertaken to date ( 333 metabolites ) . These studies revealed a much higher degree of metabolic complexity in reticulocytes compared to mature erythrocytes covering nearly all major pathways in central carbon metabolism . Whilst glycolysis is the main pathway for carbon metabolism in erythrocytes [36] , both human [57] and rodent [58] erythrocytes retain a residual proteomic signature of TCA cycle and ICM enzymes and our metabolomics data suggests that these pathways are much more active in reticulocytes , leading to elevated levels of TCA intermediates ( including citrate , malate ) and ICM products ( e . g . aspartate ) . The functional significance of increased metabolic complexity in reticulocytes was subsequently tested by generating P . berghei mutants with specific defects in metabolism showing that the increased availability of complementary metabolites in reticulocytes can explain the non-essential nature of the P . berghei pepc and mdh genes , which are involved in regulating intracellular levels of oxaloacetate and malate . In contrast , PEPC is essential for normal intra-erythrocytic survival of P . falciparum in vitro , although this can be bypassed by malate supplementation of P . falciparum infected mature erythrocytes [42] . It should be noted that whilst the P . berghei pepc- mutant retained its virulence , it still showed a significant growth defect compared with wild type parasites ( similar to the P . falciparum mutant [42] ) resulting at least in part from a prolongation of the asexual blood stage cycle as revealed by our sensitive single host competitive growth assay . It would be interesting to use this assay to compare asexual growth dynamics of other available metabolic mutants such as the pbsdha- with wild type which might reveal additional defects to those reported [49] . The P . berghei pepc- mutant also failed to complete transmission through mosquitoes as a result of defects in gametocyte production , male gamete formation , female gamete viability resulting in trace oocyst formation and failure to enter sporogony , which extends our understanding of the importance of this metabolic enzyme for parasite development beyond the asexual blood stages previously investigated [42] . A possible explanation for this phenotype is that the pepc- mutant is unable to by-pass the need for de novo synthesized aspartate for nucleotide biosynthesis by salvage from different host cells during its sexual and asexual life cycle ( Fig 2A ) . The demonstration of pbpepc- growth in reticulocytes suggests that the equivalent P . falciparum mutant might be a suitable candidate for an attenuated slow growing parasite vaccine that would permit generation of significant anti-parasitic immune responses . In line with this suggestion , we were unable to delete pbaat which is also required for de novo synthesis of aspartate . The essential nature of pbaat suggests that either the apparently higher levels of aspartate in reticulocytes are insufficient to meet the demands of a growing asexual stage parasite or that , as in P . falciparum intra-erythrocytic stages , P . berghei is not readily able to access host cytoplasmic pools of aspartate [59] . Production of aspartate in Plasmodium pepc- mutants can still be achieved through generation of the oxaloacetic acid precursor by mitochondrial malate: quinone oxidoreductase ( MQO ) or the reverse reaction of cytosolic MDH . However , this is apparently a suboptimal solution for the pepc- parasite resulting in slow growth in the blood stage and failure to develop in the mosquito . Plasmodium AAT can also generate methionine from aspartate , glutamate and other amino acids which can act as effective amino group donors [60] and regulate glutamine/glutamate metabolism . These functions may not be rescued by simple aspartate salvage from the host and further support the essentiality of aat as a key enzyme for the parasite and a possible drug target . The P . falciparum gene encoding MDH has proved refractory to deletion under any circumstances so far , suggesting that it is essential for these parasites . In marked contrast , the P . berghei mutants lacking pbmdh were readily generated , suggesting that this species may scavenge reticulocyte pools of malate or other intermediates in the TCA cycle . The pbmdh mutant exhibited a very modest growth phenotype and was able to develop into mosquito infective stages , although it produced 30% fewer oocysts than wt parasites . The continued viability of the pbmdh- mutants during transmission in the absence of reticulocyte-based compensatory sources of the metabolite can be explained by continued TCA derived production of malate and NADH+ H+ reducing equivalents given the increased flux through the TCA metabolism in gametocytes and probably later sexual stages [7 , 12] . Conditional silencing or disruption of pfmdh or degradation of PfMDH in mature gametocytes or later stages of P . falciparum would establish if MDH is required for transmission of the human parasite and that the essential nature of this enzyme is merely blood stage specific . Plasmodium spp . salvage their purine requirements from the host cell , but retain the ability to synthesise pyrimidines [61] . Purine nucleosides are taken up by the parasite PfNT1 and other , as yet , unidentified AMP transporters [62] after they are delivered to the parasitophorous vacuole via the action of erythrocyte nucleoside transporters [51 , 63] and a non-selective transport process [61 , 64] . In contrast , while other Apicomplexans ( i . e . Cryptosporidium spp . , Toxoplasma spp . ) retain the capacity to salvage pyrimidines [16] , Plasmodium spp . are thought to lack enzymes required for host pyrimidine salvage [44] . Although , Plasmodium proteins have been implicated in transporting some pyrimidine precursors [65 , 66] , presumably due to very limited availability of pyrimidines in the host cell , Plasmodium parasites have been thought to be completely dependent on de novo pyrimidine synthesis for growth in asexual stages [51] . The survival of both oprt-and ompdc- mutants could be the result of two possibilities that are not mutually exclusive . The first possibility is that the mutants directly utilize reticulocyte pools of pyrimidines which are nonetheless limiting leading to a reduction in number of merozoites produced . Alternatively , mutant parasites could synthesize orotate which is secreted into the host cytoplasm and converted to UMP by host UMP synthase before being salvaged . Both outcomes require transport of nucleosides or nucleotides from the host cytoplasm to the parasite and how this is achieved is not clear . Both pyrimidine biosynthesis mutants survive only in the youngest reticulocytes which might reflect either adequacy of supply of host UMP ( or derivatives ) or the capacity of the youngest reticulocytes to convert parasite-derived orotate . Indeed enzymes involved in the later stages of pyrimidine biosynthesis , nucleoside diphosphate kinase B , CTP synthase and ribonucleotide reductase large subunit have been identified in rodent and human erythrocytes [57 , 58] . The possibility that host pyrimidine enzymes may have redundant functions with the parasite enzymes catalysing late steps in pyrimidine biosynthesis is supported by the apparent essentiality of the P . berghei genes encoding the first four steps of pyrimidine biosynthesis . A simplified illustration of life cycle stages of P . berghei development showing the characteristics of mutant parasites at various points in the life cycle is shown in Fig G in S1 Text . The REP metabolome also explains other species-specific differences between P . berghei and P . falciparum . Glutathione biosynthesis occurs in erythrocytes [67] and the enzymes for this pathway have been shown to be present in both human [57] and rodent [58] erythrocytes . Plasmodium employs its own glutathione redox system [68] to counter oxidative stress ( Fig F-A in S1 Text ) . Both ɣ-glutamylcysteine synthetase ( ɣ-gcs ) and glutathione synthetase ( gs ) are essential for parasite survival in P . falciparum [69] yet ɣ-gcs and glutathione reductase ( gr ) can be deleted in P . berghei and intra-erythrocytic asexual growth is unaffected although mosquito stage development is arrested at the oocyst stage [70 , 71] . The REP and wtEP metabolomes demonstrated that the levels of glutathione synthesis intermediates were higher in reticulocytes than in mature erythrocytes ( Fig F-B in S1 Text ) providing a mechanistic explanation for the normal growth of P . berghei ɣ-gcs and gr mutant asexual stages in reticulocytes . Also , the inhibitor of ɣ-gcs , buthionine sulphoximine ( BSO ) inhibits P . falciparum growth with an IC50 value of ~60 μM [69] yet concentrations as high as 500 μM BSO in vitro had no inhibitory effect on P . berghei parasites in in vitro cultures ( Fig F-C in S1 Text ) consistent with the reticulocyte mediated rescue of chemical disruption of the glutathione synthesis pathway in P . berghei , although it has not been investigated whether there is a difference in BSO sensitivity against the mouse or P . berghei ɣ-gcs compared to P . falciparum or human enzymes . Enzymes involved in Plasmodium intermediary carbon metabolism [12 , 42] and pyrimidine biosynthesis [51] are considered attractive targets for drug development . The metabolome surveys and drug inhibition data presented here suggest that caution should be used before extrapolating conclusions regarding gene essentiality in reticulocyte preferent parasites such as P . berghei as part of any drug discovery pathway that has been based initially upon screens in mature erythrocytes . Bioavailability in mouse models and/or drug penetration into the reticulocyte and difference in target enzyme structures between species have been proposed as reasons for the relative ineffectiveness of drugs when tested in vivo using P . berghei [52 , 53] . An alternative view is that the reticulocyte metabolome ( at least in part ) provides a reservoir of metabolites downstream of the point of action of a drug rendering the drug less effective . This has a number of consequences: Rodent reticulocyte enrichment was done in rats by administering phenylhydrazine-HCl dissolved in 0 . 9% NaCl ( w/v ) at a dose of 100 mg/kg body weight and collecting reticulocyte enriched peripheral blood on day 5 post injection . Metabolite extraction was done as using chloroform/methanol/water ( 1:3:1 v/v ) and samples were analysed using LC-MS and GC-MS . See S1 supplementary materials and methods for details . CD34+ cells obtained from blood from human volunteers were cultured in a three-stage protocol based on the methods of [41] . Cultured reticulocytes and mature erythrocytes from matching donors were used for metabolite extraction with chloroform/methanol/water ( 1:3:1 v/v ) and samples were analyzed using LC-MS . See S1 supplementary materials and methods for details . Infection of laboratory mice , asexual culture of P . berghei stages and generation of knockout parasites was done as before [73] . Asexual growth competition assay was done by mixing wt and mutant parasites expressing different fluorescent markers and injecting them intravenously into recipient mice and monitoring the growth of the two populations by flow cytometry as done before [74] . Lethality of mutant P . berghei parasites was checked by injecting infected RBCs ( 104 ) into C57/B6 mice and monitoring parasitaemia , disease pathology and mortality over 21 days . Gametocyte conversion was monitored by flow cytometry in mutants generated in parent line ( 820cl1m1cl1 ) expressing GFP in male gametocytes and RFP in female gametocytes [75] . DNA quantification during exflagellation was also monitored by flow cytometry in mutant P . berghei parasites . Development of ookinetes in wild type , mutants and sexual crosses was observed in standard in vitro cultures maintained at 21°C . Mosquito transmission experiments were done in 5–8 days old mosquitoes used for infected blood feeds at 21°C and monitored for oocyst and sporozoite development . See S1 supplementary materials and methods for details . Inhibitors were used to perform in vitro drug susceptibility tests in standard cultures of synchronized P . berghei and P . falciparum blood stages . For testing P . berghei inhibiton , inhibitors were used at increasing concentrations to culture ring stage P . berghei for 24 hours and parasite development to schizont stage was analyzed by flow cytometry after staining iRBCs with DNA-specific dye Hoechst-33258 . P . falciparum 3D7 strain was used for determining IC50 values of inhibitors in in vitro cultures by measuring 3H-Hypoxanthine incorporation in the presence of inhibitors in increasing concentrations . See S1 supplementary materials and methods for details . All animal work was approved by the University of Glasgow’s Animal Welfare and Ethical Review Body and by the UK’s Home Office ( PPL 60/4443 ) . The animal care and use protocol complied with the UK Animals ( Scientific Procedures ) Act 1986 as amended in 2012 and with European Directive 2010/63/EU on the Protection of Animals Used for Scientific Purposes . Blood from human volunteers was supplied by the Australian Red Cross Blood Service and experiments were approved by the Walter and Eliza Hall Institute Human Research Ethics Committee , Australia . As a part of standard Australian Red Cross Blood Service practice , blood was collected from healthy donors who were informed about this study and potential risks to them and gave written consent when they donated blood .
Malaria , caused by the Apicomplexan parasites Plasmodium spp . , is a deadly disease which poses a huge health and economic burden over many populations in the world , mostly in sub-Saharan Africa and Asia . To design new intervention strategies and to improve upon existing drugs against malaria , it is useful to understand the biochemistry of the Plasmodium parasite and its metabolic interplay with the host . Some species of Plasmodium such as P . vivax grow exclusively in reticulocytes ( immature erythrocytes ) whereas others e . g . P . falciparum will also readily multiply in mature erythrocytes . We asked the questions , do these two classes of host cell offer different resources for parasite survival and could these resources influence antimalarial drug efficacy ? We used metabolomics to compare rodent reticulocytes and mature erythrocytes and identified that the metabolome of the former is more diverse and enriched . Gene disruption in the reticulocyte preferring rodent malaria parasite P . berghei was used to demonstrate that Plasmodium can utilise the elements of the metabolic reserves of reticulocytes that mature erythrocytes cannot provide . Our data suggests that the availability of the reticulocyte metabolome might reduce or block the efficacy of antimalarial drugs that target parasite metabolism and drugs tested against P . falciparum might have significantly reduced activity against P . vivax .
You are an expert at summarizing long articles. Proceed to summarize the following text: Encoding properties of sensory neurons are commonly modeled using linear finite impulse response ( FIR ) filters . For the auditory system , the FIR filter is instantiated in the spectro-temporal receptive field ( STRF ) , often in the framework of the generalized linear model . Despite widespread use of the FIR STRF , numerous formulations for linear filters are possible that require many fewer parameters , potentially permitting more efficient and accurate model estimates . To explore these alternative STRF architectures , we recorded single-unit neural activity from auditory cortex of awake ferrets during presentation of natural sound stimuli . We compared performance of > 1000 linear STRF architectures , evaluating their ability to predict neural responses to a novel natural stimulus . Many were able to outperform the FIR filter . Two basic constraints on the architecture lead to the improved performance: ( 1 ) factorization of the STRF matrix into a small number of spectral and temporal filters and ( 2 ) low-dimensional parameterization of the factorized filters . The best parameterized model was able to outperform the full FIR filter in both primary and secondary auditory cortex , despite requiring fewer than 30 parameters , about 10% of the number required by the FIR filter . After accounting for noise from finite data sampling , these STRFs were able to explain an average of 40% of A1 response variance . The simpler models permitted more straightforward interpretation of sensory tuning properties . They also showed greater benefit from incorporating nonlinear terms , such as short term plasticity , that provide theoretical advances over the linear model . Architectures that minimize parameter count while maintaining maximum predictive power provide insight into the essential degrees of freedom governing auditory cortical function . They also maximize statistical power available for characterizing additional nonlinear properties that limit current auditory models . Encoding models provide a powerful , objective means to evaluate our understanding of how sensory neural systems represent complex natural stimuli [1 , 2] . An encoding model describes any time-varying neural signal ( single- or multiunit activity [3 , 4] , local field potential [5] , hemodynamic activity [6] , or behavior [7] ) as a function of the input stimulus , and it can predict the neural response to an arbitrary novel stimulus , including complex natural sounds . Prediction accuracy provides a quantitative measure of how well a model describes sensory-evoked activity; a completely accurate model should predict neural responses to any stimulus without error . More accurate models of sensory neural activity provide insight into algorithms that can be integrated into automated systems , such as speech recognizers and image classifiers . In the auditory system , the linear spectro-temporal receptive field ( STRF ) , implemented as a finite impulse response ( FIR ) filter , is the established “standard model” for neural representation [2 , 4 , 8–13] . This filter forms the core of generalized linear models ( GLMs ) applied to the auditory system [14 , 15] , and models sharing the same analytical form as the FIR STRF have been developed for studying visual [16–18] , somatosensory [19 , 20] , and olfactory systems [21] . Despite its widespread use , careful assessments of how well the linear STRF actually describes auditory neural activity are limited [22] . A few studies have shown that the linear STRF can explain only a limited portion of sound-evoked activity in cortex , especially for complex natural stimuli [9 , 23] . Others have argued that nonlinear variants of the classical linear STRF can improve predictive power [3 , 24–33] . These nonlinear variants of the STRF show improved predictive power under specific experimental conditions . However , the more complex models are difficult to estimate reliably when experimental data are limited [1 , 18 , 22] , especially for natural stimuli [12 , 23 , 34] . Difficulties associated with fitting and testing have prevented any single alternative from replacing the linear STRF as a new standard . The challenges encountered when evaluating alternatives to the FIR STRF highlight the trade-off between model performance , how accurately it predicts neural activity , and complexity , the degrees of freedom governing the stimulus-response relationship [35 , 36] . In order to completely describe a system’s function , an encoding model must account for all the degrees of freedom of the actual system . If the system is not well understood , some degrees of freedom in a model are likely to be mismatched to the system’s function . Any mismatched complexity does not provide additional explanatory power , but it does introduce noise into model parameter estimates . Because this complexity does not improve performance , there should exist a model with fewer degrees of freedom that can perform as well as the more complex model . In this study we focus on the problem of complexity . Rather than simply seeking the model that performs best , we identify the simplest possible model that attains a minimum level of performance . Specifically , we ask , can we produce a low-dimensional approximation of the linear STRF that performs as well as the full FIR STRF ? The idea of improving STRF performance by dimensionality reduction has been proposed previously . Isolated studies have shown benefits of low-rank approximations of the STRF [28 , 31 , 37 , 38] . In the visual system , several studies have also proposed low-dimensional , system-specific parameterizations [18 , 29 , 39–43] . Despite the many parameterizations that have been proposed , however , direct comparisons between them have been limited , especially for natural stimuli . Thus it remains difficult to identify the important features of these different models . We approached the complexity problem directly by systematic comparison of a large set of alternative parameterizations . We generated a collection of models that instantiate a variety of low-dimensional approximations to the FIR STRF . We then compared their performance on single-unit data collected from primary auditory cortex during presentation of natural vocalizations . By exploring the performance of this family of models , we were able to identify the minimal essential components required by linear STRFs that best described the data and to study the relationship between the amount of data available and optimal model complexity . We found that the standard FIR STRF is suboptimal according to the complexity criterion . Instead , a much simpler model , which defines the STRF as a product of three Gaussian-tuned spectral filters and biphasic temporal filters , outperformed the FIR STRF , while requiring only about 10% of the parameters ( 29 vs . 276 free parameters ) . These results indicate that , for the average A1 neuron , a model with about 30 free parameters is able to capture its linear filter properties . The total degrees of freedom of a comprehensive nonlinear model is likely to be higher , but our minimally complex linear STRF provides a starting point for developing better-performing nonlinear models . We recorded single-unit neural activity from the auditory cortex ( A1 ) of awake , passively listening ferrets during presentation of natural ferret vocalizations . The same set of 42 3-second vocalizations was presented during recordings from all neurons ( N = 176 ) . We then fit a large number of encoding models with different architectures to data from each neuron and compared their performance . Data for each neuron were grouped into an estimation data set ( 40 vocalizations ) , which was used for fitting , and a validation data set ( 2 vocalizations ) , which was used only to test how well each fit predicted responses to a novel stimulus ( Fig 1A ) . Our primary performance metric was prediction correlation , i . e . , the correlation coefficient ( Pearson’s R ) between the actual peri-stimulus time histogram ( PSTH ) , r ( t ) , and the PSTH predicted by the model , p ( t ) ( Fig 1C ) . Other commonly used performance metrics showed the same pattern of results ( e . g . , log-likelihood and mutual information , see below ) . Models were structured as a sequence of signal transformations , or functional modules , corresponding to the block diagram in Fig 1B , x 0 ( t ) → f 1 ( · ) x 1 ( t ) → f 2 ( · ) ⋯ → f n ( · ) y ( t ) ( 1 ) where the output , xi ( t ) , of each module , fi ( ⋅ ) , provides the input into the subsequent module . The final module produced the predicted time-varying spike rate , y ( t ) . In most models tested , this sequence consisted of three modules , a cochlear filterbank [26 , 44] , followed by a linear spectro-temporal filter [8 , 9 , 11 , 12] , and finally an output nonlinearity to account for spike generation thresholds [13 , 17] . Alternative model architectures were compared by replacing one or more modules in Eq 1 , while keeping the others the same . Thus the impact of the choice for each module on model performance could be tested individually ( see Fig 2C ) . Using this empirical approach , we selected optimal modules for the cochlear filterbank ( Eqs 11–13 ) and output nonlinearity ( Eq 14 ) for the same linear filter module ( FIR filter , see below , Eq 3 ) . These modules were then held constant while we compared performance for the different formulations of the linear filter module that follow . Models were fit using an iterated coordinate descent ( a . k . a . boosting ) algorithm [34] . On each iteration , the algorithm cycled through each module sequentially and performed a few steps of coordinate descent within that module before moving on to the next one ( see Methods ) . We have previously demonstrated that this coordinate descent algorithm is able to accurately recover linear STRFs in simulation [30 , 34] . Because datasets are finite , the performance of any model will be limited by sampling noise . This noise impacts the analysis at two stages: producing error in the estimation of model parameters and in validation of prediction accuracy [18 , 22 , 45] . Accounting for the first problem is a nuanced issue: more complex models that require a large number of parameters are more susceptible to noise than simpler models . We address the issue of finite estimation data in a later section ( see Parameterized models perform similarly to FIR models in the limit of infinite data , below ) . To account for the latter problem , measures of prediction correlation were normalized by a factor reflecting response reliability in the validation stimulus ( Eq 23 , [45] ) . This factor was fixed for an individual neuron’s validation data . Thus it does not affect the performance of one model relative to another . Numerically , this correction increased prediction correlations in A1 by a mean of 20% ( ranging from 3% to 39% for individual neurons ) . Model complexity is often factored into cost functions for model fitting , in order to positively weigh simpler models [35 , 46] . Our goal was to study in depth the relationship between model complexity and performance . Thus , rather than combining them into a single cost function , we studied the trade-off between these criteria in detail , exploring the family of solutions that are optimal with respect to both . This optimal set of solutions is known as the Pareto front [36 , 47] . Formally , all items belonging to this front are non-dominated in the Pareto sense [47] which means that for all pairs of models on the front , one is less complex while the other fits more closely to the data . All models below the Pareto front are non-optimal: there is at least one model on the front that is both less complex and more accurate . We generated Pareto plots for the 1061 different linear STRF architectures tested , comparing model parameter count against average prediction correlation for estimation data ( Fig 2A ) and validation data ( Fig 2B ) . Most models lie under the Pareto front ( red line ) and are suboptimal relative to models that are less complex , better performing , or both . More complex models tend to perform better for estimation data , but they do not necessarily predict novel validation data more accurately . The differences between estimation and validation plots illustrate the problem of overfitting when available estimation data are finite . Among the more complex models , the FIR STRF falls below the Pareto front for the validation data ( black point , Fig 2B ) . Instead , best performance in the current dataset is achieved by a model requiring just 29 parameters ( orange point ) . In the following sections , we discuss in detail the subset of 260 architectures in which only the linear filtering module was varied , while all other modules ( cochlear filterbank , input nonlinearity , output nonlinearity ) and the fitting algorithm were held constant ( dark gray points , Fig 2A and 2B ) . Our focus is on identifying model architectures that fall on or near the Pareto front , making them optimal for a given level of complexity . The remaining models were generated by manipulating one or more modules other than the linear filter ( Fig 2C ) . Varying the other modules had less dramatic effects on model complexity and performance , but they provide a dense sampling of the complexity-performance space . A complete list of architectures evaluated is included in the supplementary materials ( S1 Table ) . Parameterized STRFs are approximations of the FIR STRF . Thus , in theory , the FIR STRF should perform as well as or better than any parameterized STRF . In practice , however , data available for estimation are finite , and simpler models can be estimated more accurately than the full FIR STRF . Thus simpler models are able to perform better than the FIR STRF in our analysis ( Fig 6 ) . The results so far demonstrate a clear practical advantage of the factorized and parameterized models , but they do not answer the question of whether any simpler model fully accounts for the linear STRF . Such a question can only be answered by comparing the relative performance of these models in the limit of infinite estimation data [18 , 22] . Extrapolating performance to infinite estimation data is challenging because there is no widely agreed upon model of variability in sensory-evoked neural activity . We made a simplifying assumption that prediction error from estimation noise is additive and inversely proportional to the square root of the number of samples used to estimate the STRF , T ( see Methods , Eq 31 , [18 , 45] ) . When these assumptions hold , then the effect of noise on model variance explained ( square of prediction correlation , R2 ) also decreases proportionally to T . We varied T by subsampling the available estimation data ( 10%–75% ) and measured the average RT across neurons for models fit with the different data subsets . We then fit the free parameters in Eq 31 to determine the theoretical limit on performance for each model , Rinf . We measured the asymptotic performance limit of four model architectures , ranging from high to low complexity: the full FIR model ( FIR , 276 parameters ) , D = 3 factorized model ( Factorized x3 , 109 parameters ) , D = 3 Gaussian spectral/P3Z1 temporal parameterization ( P3Z1x3 , 29 parameters ) , and D = 1 Gaussian spectral/P3Z1 temporal parameterization ( P3Z1x1 , 13 parameters ) . We removed very noisy data and focused on the subset of 124 neurons that produced reliable auditory-evoked responses ( SNR > 0 . 005 , see Methods , Eq 21 ) . For all models , performance improved as more estimation data became available ( Fig 8A ) . As expected , the lower-dimensional models performed better for small data sets and neared asymptotic performance sooner than higher-dimensional models . Consistent with this observation , performance of the FIR STRF showed the greatest improvement in the asymptote ( Rinf = 0 . 63 , Fig 8B ) . However , performance of the Factorized x3 ( Rinf = 0 . 63 ) and P3Z1x3 models ( Rinf = 0 . 62 ) was not significantly different from the FIR STRF ( jackknifed t-test ) . Thus within the precision we could achieve with this analysis , both models captured the essential features of the FIR STRF . Error bars on asymptotic performance are relatively large , especially for the FIR STRF , so a strong conclusion about relative performance of these models is difficult . However , asymptotic performance of the P3Z1x1 model was significantly worse than the other models ( Rinf = 0 . 56 , p < 0 . 001 ) , indicating a failure of this very simple model to capture the full linear model . For comparison with a previous analysis [22] , we also measured asymptotic performance for the FIR STRF with no output nonlinearity . This model performed better than the standard FIR STRF for smaller estimation sets , presumably due to its reduced complexity , but its advantage diminished for larger datasets . Asymptotic performance was slightly lower than the standard FIR STRF that included an output nonlinearity ( Rinf = 0 . 61 , p < 0 . 05 , Fig 8B ) . In addition to outperforming the FIR model in finite data conditions , reduced-dimensionality factorized and parameterized STRFs demonstrated several other benefits over the FIR STRF , which we detail below . For brevity in this section , factorized model refers specifically to the D = 2 factorized model , and parameterized model refers to the D = 3 Gaussian spectral parameterization with P3Z1 temporal parameterization . These models were chosen because they represent the best-performing models , respectively , among the factorized and parameterized models tested ( Fig 6C ) . The finite impulse response ( FIR ) STRF represents the current standard model for stimulus-response filtering in the auditory system [2 , 4 , 8–13] . Our results agree with previous findings that , as a general architecture , the linear STRF accounts only partially for the neural response to natural sounds in A1 [9 , 23] . However , we find that the same level of performance can be achieved by much simpler models . A model requiring fewer than 30 parameters not only matches performance of the FIR STRF ( > 250 parameters ) but actually outperforms it for large but finite datasets . The simplest parameterization that works optimally for a neural population provides insight into the neural circuitry underlying system function [36] . According to this logic , the average linear STRF of an A1 neuron can be captured by the sum of three channels with Gaussian spectral tuning and an IIR temporal filter . When data are finite , a critical issue is that a simpler model with fewer free parameters will be less susceptible to estimation noise than a more complex model . Thus the simpler model may perform better , even if it fails to account for important degrees of freedom in the more complex one . Accounting for the impact of estimation noise on model performance is difficult , as it requires extrapolation to the condition where data are infinite [18 , 22] . By assuming that estimation noise is additive , we found that a simple inverse relationship between estimation set size and prediction error accurately described performance for several different architectures ( Fig 8A ) . In the limit of infinite data and under these assumptions , the FIR STRF did not perform significantly better than the simple parameterized model . These results should be confirmed with a larger dataset , but the current analysis suggests that the essential degrees of freedom for the linear STRF are much closer to 29 than to the 276 specified by the FIR STRF . The average linear STRF in A1 may be described by about 30 parameters , but STRFs for individual neurons do vary substantially in their complexity . Some neurons require only one spectral channel for optimal performance while others require four or more channels ( Fig 12B ) . The fact that only a minority of neurons were best described by a single dimension argues that most linear STRFs are not frequency-time separable [37 , 51] . At the other extreme , even STRFs with four or five spectral channels required substantially fewer parameters than the standard FIR STRF . This low dimensionality generalizes across other natural and synthentic stimuli in A1 , but our analysis of data from the belt area dPEG indicates that more complex models are required for non-primary cortex ( Fig 11 ) . Moreover , even in A1 , the full dimensionality of encoding models is likely to be greater than what is required to specify the linear STRF . As demonstrated by the enhanced performance of the nonlinear STP STRFs ( Fig 11 ) , introducing additional dimensionality that extends outside of the linear STRF architecture can improve model performance . How well can the linear STRF actually describe sensory responses in A1 ? Issues surrounding finite sampling of experimental data again make it difficult to answer this question definitively [18 , 45] . After implementing our estimation noise model , we found that the FIR STRF is able to account for 40% of A1 response variance on average ( i . e . , variance explained is 100R2 for R = 0 . 63 , Fig 8 ) . Factorized and parameterized STRFs very nearly matched performance of the FIR model ( 39% of response variance ) , indicating that these approximations capture the essential features of the more complex model , despite requiring only about 50% and 10% of the parameters , respectively . These measurements establish baseline performance by the linear STRF that must be surpassed by any more accurate model . At the Pareto frontier , a better model must either produce more accurate predictions or require fewer parameters and perform as well . Only one previous study has attempted to answer this question rigorously , using activity driven by random chord stimuli in anesthetized mice [22] . Although we focused primarily on models that included an output nonlinearity [13 , 14] , we also computed asymptotic performance of STRFs without this nonlinear term in order to make a more direct comparison to the previous analysis of asymptotic performance . Without a spiking nonlinearity , the average FIR STRF was able to account for about 37% of response variance . This result falls in the range of 18–40% reported previously [22] , although several factors make a direct comparison difficult . In the current study , recordings were performed in awake ferrets and used natural vocalizations rather than anesthetized mice and noise stimuli . Anesthesia can impact auditory neural activity [58 , 59] , and natural sounds evoke nonlinear response properties in a different functional domain than noise stimuli [9 , 60] . The number of models tested here was relatively large , but they are still likely to be suboptimal compared to as-yet-untested parameterizations . The current study explored only two spectral parameterizations ( Gaussian and Morlet functions ) and the pole-zero family of IIR temporal filters . Numerous other basis functions could be considered , including Gabor wavelets [42 , 61] or empirically-derived basis functions [29 , 31 , 33] . There is a clear trade-off between basis function complexity and the number of spectral dimensions needed . Better-performing temporal kernels like the P3Z1 filter reach their peak performance when D = 3 , while simpler kernels like P1Z0 need D ≥ 4 to reach the same performance . Thus the interaction between channel count and basis function complexity will be relevant to identifying optimal parameterizations . The efficiency of estimating parameterized STRFs allows the introduction of new , nonlinear terms that can account for encoding properties that are not captured by the linear model [30 , 31] . When nonlinear short-term plasticity was introduced to the FIR STRF , it did not change model performance , but when it was introduced to the parameterized model , it improved predictive power . Thus the benefits of nonlinear terms may only become apparent when sufficient statistical power is available in the current dataset . The family of models used in this study incorporate static nonlinearities that are commonly part of STRFs . This include log-compression of the input spectrogram to account for basilar membrane mechanics [25 , 26] and an output nonlinearity to account for spike threshold and saturation [13 , 14] . Other studies have incorporated nonlinear terms into the core computation of the filter . Some use general Volterra series expansions to account for second- and higher-order nonlinearities [3 , 27 , 57 , 62] . Others incorporate more specific terms aimed at capturing contextual influences [28 , 29] or mimicking biological circuit elements [26 , 31] . These additional nonlinear terms can be incorporated into the parameterized framework , potentially providing substantial improvements in predictive power . Neurons also undergo plasticity at multiple timescales due to stimulus context [12 , 30 , 63 , 64] , changes in behavioral state [50 , 65 , 66] , and learning [49 , 67] . In many experimental settings , the quantity of data available in a single behavioral state may provide a critical limitation on statistical power . Low-dimensional parameterized models may be particularly beneficial for exploring changes in spectro-temporal response properties in these experimental settings . From a general analytical perspective , parameterization is similar to regularization during model estimation [1 , 12 , 46 , 68] . In both cases , pre-existing knowledge or a hypothesis about the system’s function is used to constrain model fits . The idea that sensory receptive fields should vary smoothly in space and time has motivated the use of priors for smoothly varying STRFs [46 , 68] . Similarly , the idea that receptive fields should have a relatively small number of non-zero parameters has motivated a sparse prior on model fits [14 , 46] . Constraining the STRF to have analytical form of the factorized or parameterized models serves the same purpose of imposing a prior on the fit [38] . In the current study , the spectral and temporal parameterizations constrain both sparseness ( limiting the model’s degrees of freedom ) and smoothness ( Gaussian spectral tuning and exponential temporal tuning ) . To simplify model comparisons in this study , we used a single fit algorithm across all models . Thus it was not optimized specifically for the FIR STRF . Incorporating stricter stop criteria and sparseness constraints improve FIR STRF performance , but even after tuning the cost function , it did not match the performance of the parameterized model . The factorized and parameterized models were less sensitive to details of the fit algorithm such as the stop criterion , emphasizing the benefits of regularization effectively built into parameterization . Most real world optimization problems involve the simultaneous minimization of several objectives [69] . Thus when comparing different model architectures , it may be helpful to consider trade-offs separately along different dimensions [36 , 70 , 71] . The current study focused in particular on the trade-off between model prediction accuracy and parameter count . In general , however , such an approach can be used to define an N-dimensional Pareto front containing the best models according to numerous other measures , including alternative performance metrics ( Fig 13 , see also [72] ) , alternative model complexity metrics [73 , 74] , data required to fit ( Fig 8 ) , computational cost [75] , or model plausibility [76] . Pareto fronts are extensively used in the context of multiobjective optimization for the formulation of heuristics [69] . Given the complexity of performing a search on the space of model architectures , we relied here on inspection of the Pareto front to guide model design . While developing new analytic models to test , we found it most helpful to generate new models by adding to or discarding from a model on the current Pareto front . Variants of non-Pareto-optimal models rarely improved performance or provided insight into the relevancy of new parameters . Of particular note , the FIR implementation falls far from the Pareto front ( Fig 2B ) , making it difficult to test variants based on the FIR STRF . Single-unit neural activity was recorded from five awake , passively listening ferrets . For the main analysis of responses to vocalizations , a total of 176 single units were recorded in primary auditory cortex ( A1 ) and 130 units in belt auditory cortex ( dPEG ) . For one analysis ( Fig 11C and 11D ) , responses were analyzed for 808 A1 units recorded during the presentation of continuous speech ( reanalyzed from a previous publication [9] ) and for 139 A1 units recorded during the presentation of 1/f noise [56] . Data used in this study will be made publicly available online via the Neural Prediction Challenge ( http://neuralprediction . berkeley . edu/ ) . The relationship between the time-varying input auditory stimulus , x ( t ) , and simultaneously recorded single-unit firing rate response , y ( t ) , is described by the spectro-temporal receptive field ( STRF [8 , 9 , 11 , 12] ) or , more generally , any function that maps x to y . In the current study , this mapping was cast as a sequence of functional modules , in which each function was applied to the output of the previous one ( Eq 1 , Fig 1C ) . The series of functions maps roughly to the physical elements that transmit auditory information to cortex . A detailed list of all models tested in this framework is included in S1 Table . For most models , stimulus and response data were binned at 10 ms ( 100 Hz ) and averaged across repetitions . Stimulus binning was applied after transformation to the spectrogram . Data recorded from each neuron were divided into two subsets , one used only for model estimation ( 4–6 repetitions of 40 3-sec vocalization sequences ) and the other for validation ( 20 repetitions of 2 3-sec sequences ) . Model parameters were fit using an iterated , greedy version of boosting that minimized mean-squared error prediction of the neural PSTH in the estimation dataset ( details below ) . Each model was then evaluated based on its ability to predict the time-varying PSTH response in the reserved validation data set . Prediction accuracy was measured as the correlation coefficient ( Pearson’s R ) between the predicted and observed PSTH [12 , 34] . The correlation coefficient provides a useful metric because it scales performance between 0 ( completely random ) and 1 ( perfect correlation ) . Model performance can be variable across single neurons . Thus to compare models we focused on average performance across the entire set of neurons studied , using the nonparametric Wilcoxon signed rank test ( sign test ) to assess significant differences in performance . Error bars for average prediction correlation plots were computed on the difference between prediction correlation for each model and the FIR STRF fit to the same neuron . Computing error bars based on the difference per neuron removed variability in overall neural response SNR ( e . g . , Figs 4A and 10B ) and revealed model differences commensurate with the sign test . Our goal was to compare the ability of different analytical model structures to describe the neural data . Ideally , the details of the fitting algorithm used to fit the different models should not be relevant to this comparison , but in practice , there is no single algorithm that can be applied to different models without some bias [1] . Thus , the best fitting algorithm and model analytical structures are not separable in practice . We tested a variety of fit algorithms ( Fig 2 , S1 Table ) , but we focused on a single algorithm that performed best , on average , across all the models tested . The fit algorithm consisted of nested iterations through each STRF module , initially optimizing each module with a conservative stop criterion . Once all modules had converged for the current stop criterion , its value was reduced and procedure was repeated for the smaller criterion . When fitting each module , two different coordinate descent algorithms were used . For non-parameterized modules ( FIR filter , factorized spectral filter , and factorized temporal filter ) , a standard coordinate descent algorithm was used . For the remaining , parameterized modules ( including the input filterbank and spike nonlinearities ) , greedy coordinate descent was used . The details of the fit algorithm are as follows: In general , we found that fitting parameters separately within modules and iterating through modules with progressively smaller stop criteria helped avoid local minima . Fitting first without the spike nonlinearity also helped avoid local minima . The greedy algorithm increased the risk of overfitting complex models , but on average greatly improved predictions for models with nonlinear and parameterized modules . The non-greedy algorithm worked best for non-parameterized modules where all parameters are of similar scale . Experimental procedures were approved by the Oregon Health and Science University Institutional Animal Care and Use Committee and conformed to standards of the National Institutes of Health .
Understanding how the brain solves sensory problems can provide useful insight for the development of automated systems such as speech recognizers and image classifiers . Recent developments in nonlinear regression and machine learning have produced powerful algorithms for characterizing the input-output relationship of complex systems . However , the complexity of sensory neural systems , combined with practical limitations on experimental data , make it difficult to apply arbitrarily complex analyses to neural data . In this study we pushed analysis in the opposite direction , toward simpler models . We asked how simple a model can be while still capturing the essential sensory properties of neurons in auditory cortex . We found that substantially simpler formulations of the widely-used spectro-temporal receptive field are able to perform as well as the best current models . These simpler formulations define new basis sets that can be incorporated into state-of-the-art machine learning algorithms for a more exhaustive exploration of sensory processing .
You are an expert at summarizing long articles. Proceed to summarize the following text: The Ebola virus causes an acute , serious illness which is often fatal if untreated . However , factors affecting the survival of the disease remain unclear . Here , we investigated the prognostic factors of Ebola virus disease ( EVD ) through various statistical models . Sixty three laboratory-confirmed EVD patients with relatively complete clinical profiles were included in the study . All the patients were recruited at Jui Government Hospital , Sierra Leone between October 1st , 2014 and January 18th , 2015 . We first investigated whether a single clinical presentation would be correlated with the survival of EVD . Log-rank test demonstrated that patients with viral load higher than 106 copies/ml presented significantly shorter survival time than those whose viral load were lower than 106 copies/ml ( P = 0 . 005 ) . Also , using Pearson chi-square test , we identified that chest pain , coma , and viral load ( >106 copies/ml ) were significantly associated with poor survival of EVD patients . Furthermore , we evaluated the effect of multiple variables on the survival of EVD by Cox proportional hazards model . Interestingly , results revealed that patient’s age , symptom of confusion , and viral load were the significantly associated with the survival of EVD cases ( P = 0 . 017 , P = 0 . 002 , and P = 0 . 027 , respectively ) . These results suggest that age , chest pain , coma , confusion and viral load are associated with the prognosis of EVD , in which viral load could be one of the most important factors for the survival of the disease . In the year of 2014 , Ebola virus disease ( EVD ) was quickly widespread and caused the whole world to pay attention [1 , 2] . By the end of 2014 , more than eleven thousand cases were reported from West African countries such as Guinea , Sierra Leone , Liberia , Senegal , Nigeria , and Mali [1 , 2] . However , Ebola did not stop in West Africa only , it has gone globally as cases were diagnosed in the United States and Spain [1 , 2] . The Ebola outbreak had many clinical management challenges due to its high fatality rate ( 45–90% ) and easy transmission [3 , 4] . Because of this , health care professionals are put at great risk when helping Ebola infected patients [5] . Such risks are greatly higher than regular daily practices . Although supportive care such as the use of antibiotics and administrating intravenous fluids is believed to be helpful , there is no clinically approved treatment to Ebola [6 , 7] . The survival rate of EVD can increase in places with advanced medical care because of constant maintenance of blood pressure , body fluids volume and hydration [8] . However , the most severely affected countries , Guinea , Liberia and Sierra Leone , have very weak health systems , lack human and infrastructural resources , and have only recently emerged from long periods of conflict and instability , which makes it extremely difficult to prevent and treat the disease . Previous studies have provided some information regarding the prognosis of EVD [9 , 10] . Bah et al . reported that EVD patients who were 40 years of age or older had a higher risk of death compared with those under the age of 40 years using Poisson regression analysis [9] . Also , the viral load appeared to be higher in non-survivors compared to survivors by univariate analyses [9] . Schieffelin et al . showed that EVD patients under the age of 21 years had a lower case fatality rate than those over the age of 45 years , and patients presenting with fewer than 105 EBOV copies/ml had a lower case fatality rate than those with 107 EBOV copies/ml [10] . Also , weakness , dizziness , and diarrhea were the symptoms that were significantly associated with a fatal outcome [10] . In addition , Towner et al . presented that viral load could be correlated with disease outcome [11] . However , these studies were conducted using relatively small number of patients . Therefore , independent research is required to confirm these findings . Moreover , since it is extremely difficult to obtain completely detailed patient information during the outbreak [12 , 13] , and it is biased and wasting of data to rule out patients with relatively uncompleted information , it would be necessary to apply COX's proportional hazard model to deal with this kind of data set . In this study , we investigated the prognostic factors of EVD using various statistical models . The institutional review board at Beijing 302 Hospital and the Sierra Leone Ethics and Scientific Review Committee approved this project . These committees waived the requirement to obtain informed consent during the West African Ebola outbreak . From Oct . 1st , 2014 to Jan . 18th , 2015 , the Chinese Medical Team in the Jui Government Hospital , Sierra Leone admitted 661 patients and 269 of them were diagnosed EVD . Noticeably , Jui Government Hospital was positioned as a holding center from Oct 1st , 2014 to Dec 31st , 2014 , and upgraded as an Ebola Treatment Center ( ETC ) on Jan 1st , 2015 . The admission of patients was coordinated by the National Emergency Response Center ( NERC ) . First of all , venous blood of the patients was immediately taken for sampling . They were then hospitalized and the samples were examined in the Chinese portable biosafety level 3 laboratories within the Jui Government Hospital . While waiting for the test results , patients were treated based on their clinical presentations . All the patients were given oral rehydration salts as a routine treatment , and the dose was dependent on the severity of dehydration . Intravenous administration of supplements was given under certain conditions . Patients with headache and/or muscle pain were given Acetaminophen or Ibuprofen . Patients with fever were given Cefixime or anti-infective Ciprofloxacin and anti-malaria Compound Naphthoquine Phosphate Tablets . Patients with upper abdominal pain or burning sensation were given antacid drugs such as Ranitidine or Omeprazole . Patients feeling fretful or insomnia were given Diazepam . A few patients were offered intravenous lactated Ringer’s solution . All the treatments were performed in compliance with ‘Clinical management of patients with viral haemorrhagic fever A pocket guide for the front-line health worker’ ( http://www . who . int/csr/resources/publications/clinical-management-patients/en/ ) and ‘Manual for the care and management of patients in Ebola Care Units/ Community Care Centres Interim emergency guidance’ ( http://www . who . int/csr/resources/publications/ebola/patient-care-CCUs/en/ ) . Patients confirmed with Ebola infection were reported to NERC , and were transported to other treatment centers before December 31st , 2014 , or stayed in the special treatment zone of Jui Government Hospital after the hospital was upgraded to ETC . Ebola-negative patients were arranged for corresponding treatments or discharge . Sierra Leone is one the most severely affected countries . However , due to its weak health system and lack of human resources , it was extremely difficult to obtain complete patient data during the outbreak . Holding center faced even worse situation because EVD confirmed patients were transferred to different ETCs after the diagnosis . Of the 269 EVD cases , 201 subjects were diagnosed when Jui Government Hospital functioned as a holding center . Only 7 patients were obtained relatively complete information . Sixty eight patients were confirmed EVD between January 1st and January 18th , 2015 after the hospital was upgraded to ETC , in which 56 cases provided relatively complete information , and the other 12 patients died soon after the admission and did not give enough information for the study . Patient data included demographic information such as gender , age and job; epidemiological history of attending traditional funerals; history of contacting with EVD patients; symptoms; clinical signs , etc . Patients were sent to the hospital ward after the healthcare workers evaluated the general situations and filled out the Ebola case investigation form or viral hemorrhagic fever case investigation form formulated by NERC . The EVD Patient Observation Sheet was completed daily after routine check , and all the sheets were transported either via closed-circuit surveillance system or by WiFi after being photographed . In order to keep the results accurate , all the data was recorded and compared separately in the Excel database by two healthcare professionals . Discharged patients were followed by calling randomly to patients themselves or their families or related treatment centers and the information of their conditions ( whether they were cured/ discharged or died , and the date of cure or death ) were being accessed . Viral load was examined upon the admission to the hospital . Detection of virus was performed by the Chinese-CDC portable biosafety level 3 laboratories based on previously published method [14] . These laboratories accepted specimens from the Jui Government Hospital as well as specimens transported by NERC from other ETC and holding centers . After recording the sign-in information in the biosafety level 3 laboratory , technicians inactivated the specimens by incubating in the water bath at 60 degree for 1 hour . Then 50 ul of inactivated specimen was used to isolate RNA by Viral RNA Isolation Kit ( Life Technologies , Grand Island , NY , USA ) and DNA by Automatic DNA Extractor ( Life Technologies , Grand Island , NY , USA ) . The isolated RNA and DNA were tested in biosafety level 2 laboratory . Thermal cycling parameters of the real-time reverse transcription-polymerase chain reaction were 30 min at 42°C followed by 10 min at 95°C and a 40 cycles of amplification . The range of cycle threshold was between 18 . 34 and 35 . 81 and the range of viral load was between 103 and 3 . 9x108 copies/ml in our study population . Data were analyzed by PASW statistics 18 . 0 software . Pearson's chi-squared test was applied to compare dichotomous variables . The Yates’s correction was applied when the expected ( theoretical ) frequency was <5 and ≥ 1 . The survival curve was estimated by Kaplan-Meier method . The Cox proportional hazard model was used for multi-factor analysis . In our study population , businessman ( 17 cases ) and students ( 13 cases ) were the two major professions . Of the 63 patients , 27 subjects were non-survivors and 36 subjects were survivors; 35 subjects had clear contact history , in which 11 cases were through funerals and 10 cases were through family members . As shown in Table 1 , common symptoms included fever ( 80 . 95% ) , diarrhea ( 49 . 21% ) , vomiting ( 50 . 79% ) , fatigue ( 90 . 48% ) , anorexia ( 87 . 30% ) , abdominal pain ( 49 . 21% ) , chest pain ( 55 . 56% ) , muscle pain ( 74 . 60% ) , joint pain ( 77 . 78% ) , headache ( 65 . 08% ) , cough ( 39 . 68% ) , difficulty in breathing ( 41 . 27% ) , difficulty in swallowing ( 42 . 86% ) , sore throat ( 38 . 10% ) , jaundice ( 26 . 98% ) , redeye ( 28 . 57% ) , skin rash ( 6 . 35% ) , hiccups ( 19 . 05% ) , pain behind eye ( 12 . 70% ) , coma ( 7 . 94% ) , confusion ( 14 . 29% ) , and bleeding ( 9 . 52% ) . We first investigated patient’s age and the prognosis of EVD . Based on WHO and other literatures [9] , patients were divided into two groups ( ≥40 years old and <40 years old ) . Log-rank test showed that the ≥40 years old group had moderately shorter survival time than the <40 years old group ( P = 0 . 087 ) . We further examined the viral load and the survival of EVD patients . Since 106 is the closest integer to the median value of the viral load of our study population , we divided the cases into two subsets according to the value . Data showed that patients with viral titer higher than 106 copies/ml presented significantly shorter survival time than those whose viral titer were lower than 106 copies/ml ( P = 0 . 005 , Fig 1 ) . We also examined the correlation between clinical symptoms and the survival of EVD . Using Pearson chi-square test , we found that fever , diarrhea , vomiting , fatigue , anorexia , abdominal pain , chest pain , muscle pain , joint pain , headache , cough , difficulty in breathing , difficulty in swallowing , sore throat , jaundice , redeye , skin rash , hiccups , pain behind eye , coma , confusion , and bleeding were not significantly associated with EVD death , whereas symptoms such as chest pain , coma and confusion showed significantly association with EVD mortality ( P = 0 . 040 , P = 0 . 007 , and P = 0 . 022 , Table 1 ) . However , value of confusion lost statistical significance after corrected for continuity ( P = 0 . 055 , Table 1 ) . Furthermore , we analyzed the positive predictive values ( PV+ ) and negative predictive values ( PV- ) of chest pain , coma and confusion . Data showed that chest pain presented 54 . 3% of PV+ and 71 . 4% of PV-; coma presented 100% of PV+ and 62 . 1% of PV-; confusion presented 60 . 6% of PV+ and 76 . 7% of PV- . In addition , we investigated whether coma , chest pain , confusion , and viral load were associated with each other using Pearson's chi-squared test . No significant difference was identified ( coma vs . chest pain , p = 0 . 371; coma vs . confusion , p = 0 . 704; chest pain vs . confusion , p = 0 . 469 , coma vs . viral load , p = 0 . 286; chest pain vs . viral load , p = 0 . 269; confusion vs . viral load , p = 0 . 847 ) . One-way ANOVA analysis sometimes cannot reflect the combined effect of multiple variables on EVD . Since it is difficult to obtain complete patient information during the outbreak of EVD , whereas it is biased and wasting of data to rule out patients with relatively non-detailed information , applying COX's proportional hazard model to deal with this kind of data set is necessary . We found that that patient’s age , the symptom of confusion , and viral load were the significantly associated with the survival of EVD cases ( P = 0 . 017 , P = 0 . 002 , and P = 0 . 027 , respectively , Table 2 ) . In this study , we identified significant differences between survivors and non-survivors in terms of chest pain and coma . Moreover , the p value was close to 0 . 05 for symptoms such as diarrhea , anorexia and fever . These data indicate that the current Ebola outbreak is similar to previous ones [15 , 16] . Meanwhile , we observed some differences . First of all , EVD used to be called Ebola hemorrhagic fever , but most cases did not show bleeding or just had little fever in the current Ebola outbreak . It might be due to different strains of the Ebola virus . Secondly , occurrence of chest pain , coma and confusion were statistically significantly correlated with EVD death by Pearson’s chi-squared test ( Table 1 ) , whereas only confusion showed correlation to the survival of EVD by Cox proportional hazard model ( Table 2 ) . There could be two reasons causing the discrepancy: 1 ) sample size was too small; 2 ) Cox proportional hazard model takes survival time into consideration . Different survival time had different effects on the model , even though the clinical outcome of the patients was the same , whereas one-way ANOVA analysis only considers the clinical outcome of the patients . In terms of age and the prognosis , we found that the survival time was shorter and the mortality was higher in older people through various statistical analyses . These data suggest that we should pay more attention to elderly patients and give them more efficient treatments . In terms of viral load and prognosis , we found that shorter survival time and higher mortality happened to the patients with higher viral load , when the patients were at similar age . These results suggest that it might be important to increase the efficiency of anti-viral treatment in order to lower the mortality and improve prognosis . Although the existing anti-viral drug such as Zmapp seems to be effective , we need larger scale clinical trials to prove it . We treated the patients mainly with oral therapy . Although we evaluated the severity of dehydration by asking and observing the amount of urine , the amount of vomiting , heart rate , the condition of peripheral limbs circulation and skin elasticity , it was difficult to adjust the dosing of the drugs without biochemical test and blood routine test . This also happened in other treatment centers [17 , 18] . There were several reasons causing the lack of intravenous therapy . First of all , we had a shortage of experienced healthcare personnel . Similar to previous disease outbreak , many healthcare staff , especially the well-trained and skilled nurses , got infected at the beginning of the outbreak , and were forced to leave their positions [19] . Secondly , different from routine medical work , it was mandatory for the healthcare staff to wear multi-layered personal protective equipment ( PPE ) when treating the patients . The multi-layered gloves and goggles made the intravenous injection much more difficult . There are some difficulties in conducting the research . At the beginning of the Ebola outbreak , the Jui Government Hospital was in paralysis as lots of experienced professionals left their positions . The data were messy as there was no formulated mode to record patients’ information . It turned better after we trained the medical team repeatedly . However , it was difficult to statistically compare the mortality with that from other reports since the outcome of the patients remained largely unknown . Also , as some of the local people don’t speak English , the communication with the Chinese doctors was through the translation from Sierra Leone nurses , which increased the possibility of misunderstanding and led to misjudgment of some symptoms . In addition , before upgraded to ETC , Jui Government Hospital transported most of the patients confirmed with Ebola virus to other ETCs , which made it difficult to get access to the treatment and outcome of these patients . In conclusion , our data indicate that clinicians should pay close attention and give efficient treatment for elderly EVD patients and whose with high viral load . Future studies should focus on how to carry out intravenous therapy efficiently and safely as well as to develop novel antiviral drugs .
The current outbreak of Ebola virus disease ( EVD ) in West Africa is the largest and most complex Ebola outbreak since the virus was first discovered in 1976 . Factors affecting the survival of the disease remain unclear . Here , we investigated the prognostic factors of EBV from 63 cases with relatively complete clinical profiles in Sierra Leone . Using different statistical models , we found that age , chest pain , coma , confusion and viral load were associated with the prognosis of EVD , in which viral load could be one of the most important factors for the survival of the disease .
You are an expert at summarizing long articles. Proceed to summarize the following text: In response to impending anoxic conditions , denitrifying bacteria sustain respiratory metabolism by producing enzymes for reducing nitrogen oxyanions/-oxides ( NOx ) to N2 ( denitrification ) . Since denitrifying bacteria are non-fermentative , the initial production of denitrification proteome depends on energy from aerobic respiration . Thus , if a cell fails to synthesise a minimum of denitrification proteome before O2 is completely exhausted , it will be unable to produce it later due to energy-limitation . Such entrapment in anoxia is recently claimed to be a major phenomenon in batch cultures of the model organism Paracoccus denitrificans on the basis of measured e−-flow rates to O2 and NOx . Here we constructed a dynamic model and explicitly simulated actual kinetics of recruitment of the cells to denitrification to directly and more accurately estimate the recruited fraction ( ) . Transcription of nirS is pivotal for denitrification , for it triggers a cascade of events leading to the synthesis of a full-fledged denitrification proteome . The model is based on the hypothesis that nirS has a low probability ( , h−1 ) of initial transcription , but once initiated , the transcription is greatly enhanced through positive feedback by NO , resulting in the recruitment of the transcribing cell to denitrification . We assume that the recruitment is initiated as [O2] falls below a critical threshold and terminates ( assuming energy-limitation ) as [O2] exhausts . With = 0 . 005 h−1 , the model robustly simulates observed denitrification kinetics for a range of culture conditions . The resulting ( fraction of the cells recruited to denitrification ) falls within 0 . 038–0 . 161 . In contrast , if the recruitment of the entire population is assumed , the simulated denitrification kinetics deviate grossly from those observed . The phenomenon can be understood as a ‘bet-hedging strategy’: switching to denitrification is a gain if anoxic spell lasts long but is a waste of energy if anoxia turns out to be a ‘false alarm’ . Denitrification is a key process in the global nitrogen cycle and is also a major source of atmospheric N2O [2] . A plethora of biogeochemical models have been developed for understanding the ecosystem controls of denitrification and N2O emissions [3] . A common feature of these models is that the denitrifying community of the system ( primarily soils and sediments ) in question is treated as one homogenous unit with certain characteristic responses to O2 and concentrations . This simplification is fully legitimate from a pragmatic point of view , but in reality any denitrifying community is composed of a mixture of organisms with widely different denitrification regulatory phenotypes [4] . Modelling has been used to a limited extent to analyse kinetic data for various phenotypes ( See [5] and references therein ) and for understanding the accumulation of intermediates [6] . To our knowledge , however , no attempts have been made to model the regulation during transition from aerobic to anaerobic respiration in individual strains , despite considerable progress in the understanding of their regulatory networks . It would be well worth the effort , since the regulatory phenomena at the cellular level provide clues as to how denitrification and NO and N2O emissions therefrom are regulated in intact soils [7] . Explicit modelling of the entire denitrification regulatory network , however , would take us beyond available experimental evidence , with numerous parameters for which there are no empirical values . Considering this limitation , here we have constructed a simplified model to investigate if a stochastic transcriptional initiation of key denitrification genes ( nirS ) could possibly explain peculiar kinetics of e−-flow as Paracoccus denitrificans switch from aerobic to anaerobic respiration [4] , [8] . Although denitrification is widespread among bacteria , the α-proteobacterium Pa . denitrificans is the ‘paradigm’ model organism in denitrification research . Recent studies [4] , [8] , [9] have indicated a previously unknown phenomenon in this species that , in response to O2 depletion , only a marginal fraction ( ) of its entire population appears to successfully switch to denitrification . In these studies , however , is inferred from rates of consumption and production of gases ( O2 , NOx , and N2 ) , and a clear hypothesis as to the underlying cause of the low is also lacking . To fill these gaps , we formulated a refined hypothesis addressing the underlying regulatory mechanism of the cell differentiation in response to O2 depletion . On its basis , we constructed a dynamic model and explicitly simulated the actual kinetics of recruitment of the cells from aerobic respiration to denitrification . The model adequately matches batch cultivation data for a range of experimental conditions [4] , [8] and provides a direct and refined estimation of . The exercise is important for understanding the physiology of denitrification in general and of Pa . denitrificans in particular and carries important implications for correctly interpreting various denitrification experiments . Generally , the transcription of genes encoding denitrification enzymes is inactivated in the presence of O2 . A population undertaking denitrification typically responds to full aeration by completely shutting down denitrification and immediately initiating aerobic respiration [10] . Thus , O2 controls denitrification at transcriptional as well as metabolic level , and both have a plausible fitness value . The transcriptional control minimises the energy cost of producing denitrification enzymes , and the metabolic control maximises ATP ( per mole electrons transferred ) because the mole ATP per mole electrons transferred to the terminal e−-acceptor is ∼50% higher for aerobic respiration than for denitrification [10] . Denitrification enzymes produced in response to an anoxic spell are likely to linger within the cells under subsequent oxic conditions ( although , this has not been studied in detail ) , ready to be used if O2 should become limiting later on . However , these enzymes will be diluted by aerobic growth , since the transcription of their genes is effectively inactivated by O2 . Hence , a population growing through many generations under fully oxic conditions will probably be dominated by the cells without intact denitrification proteome . When confronted with O2 depletion , such a population will have to start from scratch , i . e . , transcribe the relevant genes , translate mRNA into peptide chains ( protein synthesis by ribosomes ) and secure that these chains are correctly folded by the chaperones , transport the enzymes to their correct locations in the cell , and insert necessary co-factors ( e . g . , Cu , Fe , or Mo ) . In E . coli grown under optimal conditions , the whole process from the transcriptional activation to a functional enzyme takes ≤20 minutes [11] and costs significant amount of energy ( ATP ) . Synthesis of denitrification enzymes is rewarding if anoxia lasts long and NOx remains available , but it is a waste of energy if anoxia is brief . Since the organisms cannot sense how long an impending anoxic spell will last , a ‘bet-hedging strategy’ [12] where one fraction of a population synthesises denitrification enzymes while the other does not may increase overall fitness . Most , if not all , denitrifying bacteria are non-fermentative and completely rely on respiration to generate energy [13] , [14] . This implies that their metabolic machinery will run out of energy whenever deprived of terminal e−-acceptors . When [O2] falls below some critical threshold , the cells will ‘sense’ this and start synthesising denitrification proteome , utilising energy from aerobic respiration [10] . However , if O2 is suddenly exhausted or removed , the lack of a terminal e−-acceptor will create energy limitation , restraining the cells from enzyme synthesis , hence , entrapping them in anoxia . This was clearly demonstrated by Højberg et al . [15] , who used silicone immobilised cells to transfer them from a completely oxic to a completely anoxic environment . Such a rapid transition is unlikely to occur in nature; however , the experiment illustrates one of the apparent perils in the regulation of denitrification: the cells that respond too late to O2 depletion will be entrapped in anoxia , unable to utilise alternative electron acceptors for energy conservation and growth . Højberg et al . 's [15] observations have largely been ignored in the research on the regulation of denitrification , and it is implicitly assumed that , in response to O2 depletion , all cells in cultures of denitrifying bacteria will switch to denitrification . Contrary to this , however , Bergaust et al . [4] , [8] , [16] followed by Nadeem et al . [9] proposed that in batch cultures of Pa . denitrificans , only a small fraction of all cells is able to switch to denitrification . During transition from oxic to anoxic conditions , they observed a severe depression in the total e−-flow rate ( i . e . , to O2+NOx , see Fig . 1 ) , which was estimated on the basis of measured gas kinetics . Had all of the cells switched to denitrification as O2 exhausted , the total e−-flow rate would have carried on increasing , without such a depression . The depression was followed by an exponential increase in the e−-flow rate , which was tentatively ascribed to anaerobic growth of a small ( fraction recruited to denitrification ) . It was postulated that this fraction escaped entrapment in anoxia by synthesising initial denitrification proteins within the time-window when O2 was still present , whereas the majority of the cells ( ) failed to do so , thus remained unable to utilise NOx . To represent the batch cultivation conducted by Bergaust et al . [4] , [8] , the model explicitly simulates growth of two sub-populations , one with denitrification enzymes ( ) and the other without ( ) ; both equally consume O2 , but cannot reduce NOx to N2 . Once oxygen concentration in the liquid falls below a critical level [22] , the cells within are assumed to initiate nirS transcription ( and thereby ensure recruitment to ) with a rate described by a probabilistic function: ( cells h−1 ) , where is assumed to be an dependent probability ( h−1 ) for any cell within to initiate nirS transcription ( leading to a full denitrification capacity ) . When falls below , triggers and holds a constant value as long as is above a critical minimum . For , is zero ( assuming the inactivation of NNR by O2 ) ; is also zero for ( assuming the lack of energy for protein synthesis ) . The recruitment of to is simulated as an instantaneous event; thus , the model does not take into account the time-lag between the initiation of nirS transcription and the time when the transcribing cell has become a fully functional denitrifier . This simplification is based on the evidence that this lag is rather short . Experiments with E . coli [11] under optimal conditions suggest lags of ∼20 minutes between the onset of transcription and the emergence of a functional enzyme . In Pa . denitrificans [8] , [22] , the lag observed between the emergence of denitrification gene transcripts and the subsequent gas products suggests that the time required for synthesising the enzymes is within the same range . In a series of experiments with denitrifying bacteria ( Pseudomonas denitrificans , Pseudomonas fluorescens , Alcaligenes eutrophus and Paracoccus pantotrophus ) [24]–[26] , oxic cultures were sparged with N2 to remove O2 and were monitored by measuring optical density ( OD550 ) . All the strains except Ps . fluorescens went through a conspicuous ‘diauxic lag: a period of little or no growth’ [26]; the OD remained practically constant during the lag period , lasting 4–30 hours , which was eventually followed by anaerobic growth . To understand the diauxic lag , Liu et al . [24] used the common assumption that all cells would eventually switch to denitrification . They constructed a simulation model based on the assumption that all the cells contained a minimum of denitrification proteome ( even after many generations under oxic conditions ) . This minimum would allow them to produce more denitrification enzymes when deprived of O2 , albeit very slowly due to energy limitation . The time taken to effectively produce adequate amounts of denitrification enzymes ( = the diauxic lag ) was taken to be a function of the initial amounts of these enzymes per cell . Although their model may possibly explain short time-lags , it appears unrealistic for lag phases as long as 10–30 hours [25] because to produce such long lags , conceivably , the initial enzyme concentration would be less than one enzyme molecule per cell , which is mathematically possible but biologically meaningless . The model presented in this paper provides an alternative explanation for the apparent diauxic lags: a sudden shift from fully oxic to near anoxic conditions ( by sparging with N2 ) would leave the medium with only traces of O2 , which would be quickly depleted due to aerobic respiration . As a consequence , the available time for initiating the synthesis of denitrification proteome would be marginal , allowing only a tiny fraction ( ) of the cells to switch to denitrification . This marginal fraction would grow exponentially from the very onset of anoxic conditions , but it would remain practically undetectable as measured ( OD ) for a long time , creating the apparent 4–30 h lag . The length of the lag depends on the fraction of the cells switching to denitrification . To demonstrate this alternative explanation , we adjusted our model to the reported conditions and simulated the experiment of Liu et al [24] . The model produced qualitatively similar ‘diauxic lags’ in the simulated cell density ( OD ) , although the time length of the lag could be anything ( depending on assumptions regarding the residual O2 after sparging , which was not measured ) . Bergaust et al . [4] , [8] studied aerobic and anaerobic respiration rates in Paracoccus denitrificans ( DSM413 ) . The cells were incubated ( at 20°C ) as stirred batches in 120 mL gastight vials , containing 50 mL Sistrom's medium [27] ( Fig . 3 ) . The medium was supplemented with various concentrations of KNO3 or KNO2 . Prior to inoculation , air in the headspace was replaced with He to remove O2 and N2 ( He-washing ) , followed by the injection of no , 1 , or 7 headspace-vol . % O2 . Finally , each vial was inoculated with ∼3×108 aerobically grown cells . The model effectively represents the physical phenomena mentioned above , so as to ensure that the simulation results match the measured data for the right reasons . Net effect of sampling ( dilution and leakage ) is included in the simulation of O2 kinetics at the reported sampling times . Transport of O2 between the headspace and the liquid is modelled using an empirically determined transport coefficient and the solubility of O2 in water at 20°C . To simulate the metabolic activity ( O2 consumption and N2 production ) and growth , the model divides the cells into two sub-populations: one without and the other with denitrification enzymes ( and pools , respectively , see Fig . 3 ) . Both equally consume O2 if present , but cannot reduce to N2 . Those cells that , in response to O2 depletion , are able to initiate nirS transcription ( see Fig . 2 ) are recruited to the pool , where = 0 prior to the recruitment . The recruitment rate ( ) is modelled according to a probabilistic function described below ( Eqs . 7–8 ) . The model ignores sampling effect on N2 ( leakage and loss ) , thus calculating the cumulative N2 production as if no sampling took place . That is because the experimentally determined N2 accumulation ( which is to be compared with the model predictions ) was already corrected for the net sampling effect . The model is developed in Vensim DSS 6 . 2 Double Precision ( Ventana Systems , Inc . http://vensim . com/ ) using techniques from the field of system dynamics [29] . The model is divided into three sectors: I . O2 kinetics , II . Population dynamics of and , and III . Denitrification kinetics ( Fig . 4 ) . Structural-basis for the O2 kinetics is mapped in Fig . 4A: the squares represent the state variables , the circles the rate of change in the state variables , the shaded ovals the auxiliary variables , the arrows mutual dependencies between the variables , and the edges represent flows into or out of the state variables . Briefly , Fig . 4A ( left to right ) shows that O2 in the vial's headspace ( ) is transported ( ) to the liquid-phase ( ) , where it is consumed ( ) by both the and populations ( lacking and carrying denitrification enzymes , respectively ) in proportion to an identical cell-specific velocity of O2 consumption ( ) . represents net marginal changes in due to sampling . Below we present equations and a detailed explanation of the structural components shown for this sector . Fig . 4B represents the structure governing the population dynamics of and . Briefly , the figure shows that both the populations are able to grow by aerobic respiration ( and , respectively ) . Initially , = 0 and is populated through recruitment ( ) of the cells from the pool , where the recruitment is a product of and an [O2] dependent specific-probability ( h−1 ) of the recruitment ( , see Eqs . 7–8 ) . The growth rate of is primarily based on denitrification ( ) , but the cells that are recruited before O2 is completely exhausted also grow by consuming the remaining traces of O2 . Below we present equations and a detailed explanation of the structural components shown for this sector . The structure controlling the denitrification kinetics is mapped in Fig . 4C . Briefly , the figure shows that the cells with denitrification proteome ( ) control the consumption rate of ( ) , recovered as , in proportion to a cell-specific velocity of consumption ( ) . The denitrification intermediates NO and N2O are not explicitly modelled , as they accumulated to miniscule concentrations only [4] , [8] . Most of the parameter values used in the model are well established in the literature ( See Table 2 ) . However , somewhat uncertain parameters include , , , and the assumed parameter : To test the assumption of a single homogeneous population , we forced our model to achieve 100% recruitment to denitrification by setting = 1 h−1 . In consequence , the simulated N2 accumulation ( molN vial−1 ) showed gross overestimation as compared to the measured for all the treatments ( as illustrated for some randomly selected ones in Fig . 6 ) . To find a more adequate value , was calibrated to produce the best possible match between the simulated and measured N2 through optimisation . ( The optimisation was carried out in Vensim DSS 6 . 2 Double Precision , http://vensim . com/ ) . Table 4 presents the optimal for each treatment; no consistent effect of initial [O2] and [] was found on the optimal results . The average for all the treatments = 0 . 0052 , which appears to give reasonable fit between the simulated and measured N2 ( See Figs . 7 , 8 , and 9 ) . This indicates that the simulations with = 0 . 0052 should provide a reasonable approximation of ( the fraction recruited to denitrification ) during the actual experiment . To investigate whether the recruitment of a small fraction of the cells to denitrification could explain the ‘diauxic lag’ observed by Liu et al . [24] , we used our model to simulate the conditions they reported for their experiment . In short , Liu et al . [24] incubated Ps . denitrificans ( ATCC 13867 ) in oxic batch cultures , which were sparged with N2 as the cultures had reached different cell densities ( OD550 = 0 . 05–0 . 17 ) . The sparging resulted in apparent diauxic lags , i . e . , periods with little or no detectable growth . The length of such lags increased with the cell density present at the time of sparging . Two sensitivity analyses were run to investigate the system's response to initial O2 in the headspace , : one corresponding to a range of initial [O2] in the liquid-phase below ( see Eqs . 7–8 ) and the other for a range much higher than . All other model parameters and initial values remained as listed in Tables 2 and 3 , respectively . The exercise helps illustrate the relative importance of aerobic growth versus the recruitment ( ) in determining the time taken to deplete the pool . The prevailing wisdom in denitrification research is that , under impending anoxic conditions , all cells in a batch culture of denitrifying bacteria will switch to denitrification . However , recent experiments with batch cultures of Pa . denitrificans have provided evidence that , in response to O2 depletion , only a small fraction ( ) of the entire population is able to switch to denitrification [4] , [8] , [9] . The evidence is based on indirect analyses of e−-flow rates to O2 and NOx during the transition of the cells from aerobic to anaerobic respiration . To provide a direct and refined estimation of , we constructed a dynamic model and directly simulated kinetics of recruitment of the cells to denitrification . We first formulated a hypothesis as to the underlying regulatory mechanism of cell differentiation under approaching anoxia . Briefly , it is that the low is due to a low probability of initiating transcription of the nirS genes , but once initiated , the transcription is greatly enhanced through autocatalytic positive feedback by NO , resulting in the recruitment of the transcribing cell to denitrification . Then , as we implemented this hypothesis in the model , the simulation results showed that the specific-probability ( ) of 0 . 0052 ( h−1 ) for a cell to switch to denitrification is sufficient to robustly simulate the measured denitrification gas kinetics . The model estimated the resultant between 3 . 8–16 . 1% only ( average = 8 . 2% ) . The phenomenon may be considered as a ‘bet-hedging’ regulation ‘strategy’ [12]: the fraction switching to denitrification benefits if the anoxic spell is long and NOx remains available , whereas the non-switching fraction benefits , by saving energy required for the protein synthesis , if the anoxic spell is short . The strategy has important implications for the interpretation of numerous experiments on Pa . denitrificans and other denitrifying organisms , as this study has illustrated by presenting a more plausible explanation of the apparent diauxic lags [24] on the basis of the low .
In response to oxygen-limiting conditions , denitrifying bacteria produce a set of enzymes to convert / to N2 via NO and N2O . The process ( denitrification ) helps generate energy for survival and growth during anoxia . Denitrification is imperative for the nitrogen cycle and has far-reaching consequences including contribution to global warming and destruction of stratospheric ozone . Recent experiments provide circumstantial evidence for a previously unknown phenomenon in the model denitrifying bacterium Paracoccus denitrificans: as O2 depletes , only a marginal fraction of its population appears to switch to denitrification . We hypothesise that the low success rate is due to a ) low probability for the cells to initiate the transcription of genes ( nirS ) encoding a key denitrification enzyme ( NirS ) , and b ) a limited time-window in which NirS must be produced . Based on this hypothesis , we constructed a dynamic model of denitrification in Pa . denitrificans . The simulation results show that , within the limited time available , a probability of 0 . 005 h−1 for each cell to initiate nirS transcription ( resulting in the recruitment of 3 . 8–16 . 1% cells to denitrification ) is sufficient to adequately simulate experimental data . The result challenges conventional outlook on the regulation of denitrification in general and that of Pa . denitrificans in particular .
You are an expert at summarizing long articles. Proceed to summarize the following text: Leprosy Type-1 Reactions ( T1Rs ) are pathological inflammatory responses that afflict a sub-group of leprosy patients and result in peripheral nerve damage . Here , we employed a family-based GWAS in 221 families with 229 T1R-affect offspring with stepwise replication to identify risk factors for T1R . We discovered , replicated and validated T1R-specific associations with SNPs located in chromosome region 10p21 . 2 . Combined analysis across the three independent samples resulted in strong evidence of association of rs1875147 with T1R ( p = 4 . 5x10-8; OR = 1 . 54 , 95% CI = 1 . 32–1 . 80 ) . The T1R-risk locus was restricted to a lncRNA-encoding genomic interval with rs1875147 being an eQTL for the lncRNA . Since a genetic overlap between leprosy and inflammatory bowel disease ( IBD ) has been detected , we evaluated if the shared genetic control could be traced to the T1R endophenotype . Employing the results of a recent IBD GWAS meta-analysis we found that 10 . 6% of IBD SNPs available in our dataset shared a common risk-allele with T1R ( p = 2 . 4x10-4 ) . This finding points to a substantial overlap in the genetic control of clinically diverse inflammatory disorders . A clear temporal separation from the different stages of leprosy pathogenesis identifies the endophenotype Type-1 Reactions ( T1Rs ) as a well-delineated example for host pathological inflammatory responses in humans . An endophenotype , as defined by John and Lewis in 1966 , is a microscopic and internal phenotype that is not easily identified in the presence of an exophenotype , which is the dominating phenotype that is more easily recognized [1] . In the context of our study we refer to the term endophenotype as a condition ( T1R ) that occurs in some but not all persons displaying the necessary exophenotype ( leprosy ) diverging from the original concept of John and Lewis . Of note , T1R shares immune-pathological similarities with immune reconstitution inflammatory syndrome of HIV patients undergoing highly active antiretroviral therapy [2] , and paradoxical reactions in patients with Buruli ulcer undergoing anti-microbial therapy [3 , 4] . T1Rs are a major challenge of current leprosy control since the hyper-inflammatory immune response triggered by Mycobacterium leprae , the etiological agent of leprosy , frequently leads to permanent nerve damage [5] . A prompt identification of T1R cases and rapid clinical intervention are essential to prevent lasting neurological damage [6] . While acute neuritis is a hallmark of T1R , the detailed mechanisms that link hyper-inflammation to neuropathy are not known . Depending on the epidemiological setting , 30% to 50% of leprosy cases develop at least one T1R episode [5 , 7–10] . Why only a fraction of leprosy-infected individuals undergo T1R is not known but the description of a transcriptome signature in response to M . leprae antigen strongly supported a genetic predisposition to T1R [11] . In addition , genetic variants in a few number of candidate genes ( TLR1 , TLR2 , NOD2 , LRRK2 and TNFSF15/TNFSF8 ) were found to be associated with T1R [12–17] . Independently , variants in several of these genes had also been implicated in susceptibility to leprosy per se raising the possibility of an overlapping genetic control of intensity of pathway activation between protective and pathological host responses [18] . To contrast the genetic control of leprosy and its clinical subtypes from the genetic control of the pathological immune responses typical for T1R , we designed a genome-wide association scan ( GWAS ) to identify novel genes or variants associated solely with T1R . This may lead to predictive biomarkers for early recognition of T1R and possibly indicate novel pharmacological interventions that reduce the need for potentially adverse long-term corticoid treatment in T1R . We evaluated the association of host genetic factors with T1R by conducting a family-based GWAS in 221 families with 229 T1R-affect offspring followed by stepwise replication in independent population-based case-control samples ( Fig 1 ) . For the discovery phase , approximately 6 . 3 million genotyped and imputed variants ( SNPs and INDELs ) that passed quality control were tested for association in both T1R-affected and T1R-free family sets . In T1R-affected families , a suggestive association with T1R was detected on chromosome region 10p21 . 2 ( Fig 2A and 2B ) . Among the 103 SNPs located in the interval and strongly associated with T1R leprosy ( pDiscovery < 0 . 001 ) , SNP rs7916086 ( pDiscovery = 8 . 2x10-7 ) displayed the strongest evidence of association . Applying a linkage disequilibrium ( LD ) threshold of r2 > 0 . 9 , the 103 SNPs located between the two recombination hot-spots in the 10p21 . 2 locus could be grouped into seven SNP bins ( Fig 2C , S1 Table ) . None of the SNPs in the 10p21 . 2 locus located outside this hot spot showed evidence for association below p < 0 . 001 . The tag SNP that presented the lowest p value for the association with T1R in each of the seven SNP bins was selected as the leading variant for its particular bin . When the 220kb region comprising the T1R-risk locus was evaluated in the T1R-free families no signal of association was detected ( Fig 2C , S1 Table ) . The formal heterogeneity test confirmed preferential association of T1R with the seven SNP bins reported in the discovery phase with p Heterogeneity ranging from 0 . 009 to 5 . 0x10-04 ( S1 Table ) . Of note , an additional 4372 variants located throughout the genome displayed p < 0 . 001 in the T1R-affected subset and are given in S2 Table . A multivariable analysis including the leading variant of each SNP bin ( r2 = 0 . 9 ) associated with T1R selected rs7916086 as the single signal of association in the 10p21 . 2 chromosomal region ( S1 Table ) . However , due to high LD among SNPs of the investigated bins , alternative models could not be excluded ( S1 Fig ) . Therefore , we selected the seven leading variants for each of the SNP bins ( r2>0 . 9 ) described above for further confirmative analyses in independent populations . The leading SNP in the discovery phase , rs7916086 , showed borderline evidence for association with T1R in the Vietnamese replication sample ( p = 0 . 04 ) . However , association of rs7916086 with T1R was not validated in the Brazilian sample ( p = 0 . 26 ) ( S3 Table ) . The leading SNPs in four additional SNP bins , namely rs10509110 , rs11006600 , rs10826329 and rs10763614 , did not show consistent evidence for significant association across the Vietnamese and Brazilian populations ( S3 Table ) . In contrast , SNP rs1875147 displayed strong replicated and validated evidence of association with T1R ( Table 1 ) . SNP allele “C” of rs1875147 was identified as global risk factor for T1R with an odds ratio ( OR ) = 1 . 37; confidence interval of the one-sided test ( uniCI ) 95% = 1 . 11; p = 0 . 006 , in the Vietnamese replication sample and , OR = 1 . 47; uniCI 95% = 1 . 15; p = 0 . 005 , in the Brazilian validation sample ( Table 1 ) . In addition , the tag SNP for a second bin , rs10826321 , was associated with T1R in the Vietnamese replication ( p = 0 . 003 ) and the Brazilian validation sample ( p = 0 . 04 Table 1 ) . In Vietnam , SNPs rs1875147 and rs10826321 were highly correlated ( r2 ≈ 0 . 8 ) capturing the same signal of association with T1R . However , compared to the Vietnamese , the LD between rs1875147 and rs10826321 was lower in Brazilians ( r2 = 0 . 21; S1 Fig ) . Since the 7 SNP were tested for replication and validation we did not apply a Bonferroni correction . To investigate the independent effect of rs1875147 and rs10826321 in Brazilians we performed a multivariable analysis . SNP rs1875147 maintained the association with T1R ( p = 0 . 009 ) while rs10826321 lost significance ( p = 0 . 49 ) . Next , we investigated the combined effect of rs10826321 and rs1875147 by conducting a haplotype analysis in the Brazilian sample . We found that the haplotype with the T1R-risk allele in both SNPs ( G-C alleles for rs10826321 and rs1875147 respectively ) was significantly associated with T1R ( p- = 0 . 04; S4 Table ) consistent with results obtained by multivariable analysis supporting the non-independent association of rs1875147 and rs10826321 with T1R . Interestingly , the haplotype ( A—C ) containing the alternative allele for rs10826321 and the T1R-risk allele for rs1875147 showed a trend towards association with T1R in Brazilians ( p = 0 . 06; S4 Table ) . This observation supported rs1875147 as the main cause of association of T1R with the 10p21 . 2 region . When a combined analysis was performed to summarise all study phases , only SNPs rs1875147 surpassed the genome wide threshold for significant association with T1R ( Table 1 , Fig 2D ) . In a fixed-effect meta-analysis SNPs rs1875147 presented an OR = 1 . 54; CI 95% = 1 . 32–1 . 80 , p = 4 . 5x10-08 for the C-allele . As modest levels of population heterogeneity were observed for the T1R-risk SNPs in a complementary fixed-effect model ( Table 1; S3 Table ) , we performed a random-effect meta-analysis . The seven SNPs showed similar levels of significance between the fixed and random-effect ( S5 Table ) . For the rs1875147 the random-effect model resulted in a risk-effect of OR = 1 . 54; CI 95% = 1 . 28–1 . 86 , p = 6 . 4x10-08 for the C-allele . The locus validated for association with T1R mapped within two recombinational hot spots where a single long non-coding RNA ( lncRNA ) was located ( Fig 2D ) . The novel lncRNA presented two isoforms , one encoded by the ENSG00000235140 ( a . k . a . RP11-135D11 . 2 ) gene and another encoded by the uncharacterized LOC105378318 ( Fig 2D ) . The two T1R-risk variants , rs1875147 and rs10826321 , are located at 6 . 5 kb and 8 . 7 kb , respectively , upstream of the transcription start site of the ENSG00000235140 gene . The rs10826321 variant alters the binding motif of a CTCF transcription factor in a CTCF binding site in 83 cell types ( S2A Fig ) . The rs10826321 T1R-risk G-allele is more commonly observed in CTCF binding than the alternative A-allele ( S2A Fig ) . SNP rs1875147 is reported as an expression quantitative trait locus ( eQTL ) for ENSG00000235140 in the transverse colon where the T1R-risk allele C is correlated with higher gene expression ( S2B Fig ) [19] . The eQTL effect for rs1875147 was also nominally significant in the terminal ileum of the small intestine and in the spleen in a smaller sample size ( S2C Fig ) . Both rs1875147 and rs10826321 are conserved loci across species [20] . Certain SNP alleles associated with T1R-risk had previously been shown to be susceptibility factors for inflammatory bowel disease ( IBD ) [21–23] . To investigate if there was an enrichment of risk alleles between T1R and IBD , we systematically compared evidence of association with T1R in the Vietnamese discovery set with evidence for association in a recent GWAS meta-analysis for IBD [24] ( Fig 3A ) . Of 232 independent top SNPs that had been associated with IBD by meta-analysis , 208 were available in the T1R-affected and T1R-free GWAS datasets [24] . For 22/208 SNPs ( 10 . 6% ) the IBD risk allele was associated at the 0 . 05 level with risk of T1R/leprosy . ( Fig 3A , S6 Table ) . This observed proportion of shared risk-alleles between T1R leprosy and IBD is significantly non-random ( p = 2 . 4x10-4 ) . Importantly , none of the 22 SNPs showed significant evidence of association with T1R-free leprosy while 9 SNPs displayed significant heterogeneity between leprosy and T1R indicating an enrichment of stringently defined T1R SNPs among IBD SNPs ( p = 1 . 9x10-3; Fig 3 , S6 Table ) . Similar analyses in T1R-free families , failed to detect an enrichment of leprosy risk alleles among IBD SNPs . Indeed , while several genes with known overlap of IBD and leprosy were detected ( i . e . RIPK2 , LACC1 and IL23R ) , there was no genome-wide statistical enrichment for IBD risk alleles in T1R-free leprosy ( p = 0 . 09; Fig 3A ) . As additional control , we evaluated three non-immunity phenotypes for which recent GWAS meta-analyses were available ( Schizophrenia [25] , human height [26] and human blood metabolites [27] ) for an overlap of genetic risk factors with T1R . There was no significant enrichment of either leprosy or T1R risk alleles with SNP alleles of any of the three control phenotypes ( Fig 3B to 3D ) . Among the 22 IBD SNPs associated with T1R leprosy , 17 are cis eQTL for one or more genes ( S3 Fig ) . Similarly , 7 of the 9 SNPs significantly heterogeneous between T1R and leprosy were eQTLs in either whole blood , rs3774937 ( NFKB1 ) , rs10065637 ( ANKRD55 ) , rs11150589 ( ITGAL ) and rs2836878 ( lncRNA ENSG00000235888 ) or multiple tissues , rs4664304 ( LY75 ) , rs113653754 ( HLA-DQB1 ) and rs4768236 ( LRRK2; S4 Fig ) [19 , 28] . SNPs that were eQTL in multiple tissues displayed some of the strongest associations with T1R ( S6 Table ) . Since the LY75 gene encodes a major endocytic receptor of dendritic cells and HLA-DQB1 gene expression is also modulated by a risk SNP , our results highlight the critical role of antigen presentation in dysregulated immunity of both IBD and T1R . In summary , we have conducted the first GWAS for pathological inflammatory responses in leprosy using the largest collection of T1R-affected individuals to date . Our stepwise replication study in ethnically independent populations led to the description of an eQTL ( rs1875147 ) for the lncRNA gene ( ENSG00000235140 ) as a global risk-factor for T1R . Moreover , we have observed an enrichment of shared risk-alleles between leprosy/T1R and IBD but not for IBD and leprosy per se . We have shown previously for the PARK2 gene that testing only the leading SNP of the discovery phase in ethnically independent populations without considering population differences in the LD structure may result in false negative associations [29] . Here , the leading SNP in the Vietnamese discovery phase , rs7916086 , could not be validated for the association with T1R; but rather , two SNPs highly correlated with rs7916086 in the Vietnamese population ( namely rs1875147 and rs10816321 ) were T1R-risk factors in Brazilians . The lower LD conservation in Brazilians enabled us to narrow down the T1R association signal in the 10p21 . 2 region to a single SNP , rs1875147 , which presented a pre-established regulatory function . Since we used the 1000 Genomes data to impute SNPs for the analysis and chose a high r2 cut off for SNP bin definition , it is unlikely that another common SNP in strong LD with rs1875147 would provide a stronger signal of association . However , we cannot rule out a combination of rare variants as cause of the association signal . Combined , our results highlight the strength of employing different ethnicities in the validation phase since the genetic effects of rs7916086 , rs10826321 and rs1875147 could not be disentangled in the Vietnamese sample . An association with leprosy was previously reported for chromosome region 10p21 . 2 [30] . The reported peak of association with leprosy per se encompassed the ADO and EGR2 genes . The leading variant in the ADO/EGR2 locus , rs58600253 , is located at approximately three mega bases upstream of the T1R associated locus . When the imputed variant rs58600253 ( Info = 0 . 992 ) was evaluated in the T1R-affected and T1R-free families we observed no significant signal of association ( p = 0 . 25 and p = 0 . 22 , respectively ) . Moreover , no correlation of rs58600253 with the T1R signal tagged by rs187514 was detected using the best call genotypes ( r2 = 0 . 04 ) . These results indicated that the T1R locus on region 10p21 . 2 is independent of the leprosy per se ADO/EGR locus . Moreover , a recent GWAS meta-analysis by Wang et al . identified four novel loci associated with leprosy [31] . While none of the leading SNPs reported by Wang et . al . were significant in our T1R GWAS , we observed independent variants associated with leprosy in two out of the four newly reported loci . The rs4684104 SNP near the PPARG gene ( p = 2 . 4 x 10−6; p Heterogeneity = 5 . 4 x 10−4 ) and the rs10239102 near the BBS9 gene ( p = 4 . 2 x 10−4 , p Heterogeneity = 0 . 07 ) were T1R-specific and T1R-non-specific , respectively . The functional annotation for the rs1875147 T1R-risk alleles argues that upregulation of ENSG00000235140 transcription may contribute to T1R susceptibility . However , this lncRNA gene has not been found to be commonly expressed in all tissues . The ENSG00000235140 gene was detected mostly in the sexual organs , gastro intestinal tract , and in the lungs of healthy individuals [19 , 32] . These tissues usually do not harbor M . leprae , but are a reservoir for other mycobacteria such as M . avium paratuberculosis ( colon ) and M . tuberculosis ( lungs ) . The limited knowledge about the role of ENSG00000235140 in health and disease limits our understanding of this lncRNA in T1R pathogenesis . Notwithstanding , our data present the ENSG00000235140 gene as a prime candidate to unravel the riddle of pathological immune responses in T1R and possibly inflammatory disorders in general . An overlap regarding the genetic control of leprosy per se and IBD has been previously suggested [21–23 , 33] . Although the SNPs associated with IBD and leprosy are frequently the same the risk-allele are less consistent . This factor hinders the establishment of a shared biological mechanism for IBD and leprosy . As T1R affects a considerable proportion of leprosy cases it is possible that , at least partially , the genetic overlap proposed between IBD and leprosy is due to the T1R phenotype . Here our strategy was to evaluate if T1R and IBD shared additional risk-alleles . Although , our approach focusing only on the leading SNP per IBD locus was conservative , the enrichment for shared risk-alleles in IBD and T1R was strong and may represent only part of the shared biological mechanisms . The results reported here strongly support the view that susceptibility to IBD involves a genetic predisposition to mount dysregulated inflammatory immune responses as exemplified by the T1R phenotype in leprosy . In complex traits , precise phenotype definition is key for the detection of genetic associations . For example , we have previously shown for variants of the TNFSF15/TNFSF8 genes that leprosy patients with the T1R endophenotype are largely the cause of association with the leprosy exophenotype [16 , 17] . Consequently , the replication of the TNFSF15/TNFSF8 association in samples of leprosy patients with a low proportion of T1R is expected to display low power . Equally important , accurate phenotype definition directs the interpretation of detected associations . Assigning genes to the exophenotype leprosy that impact on the endophenotype T1R may lead to wrong conclusions about the pathology of leprosy . Hence , a notable strength of our study is the focus on a well-defined endophenotype which is directly connected to a major problem of current leprosy control . This increases the power for detection of genetic effects while at the same time opening a translational link for control of nerve damage . Despite these strengths , our study also had limitations . For example , we only tested an additive model , since T1R is highly prevalent in leprosy ( 30 to 50% of all cases ) ; dominant and recessive models of inheritance could unveil additional novel associations . Moderate levels of population heterogeneity were observed in the combined analysis ( I2 values ≈ 30 to 50; Table 1 , S3 table ) . The population heterogeneity was likely driven by a winner's curse phenomenon , a bias that inflates risk estimates for newly identified SNPs when a study lacks statistical power [34] . Because of the possible effect of winner’s curse , the combined risk effect should be consider as a summary of our study and the real risk-effect for variants in the 10p21 . 2 region are likely closer to the effect of the replication and validation phase . A second limitation is the pleiotropic analysis of IBD and leprosy/T1R . As a consequence of the T1R/leprosy sample size , intermediary to low frequency variants with modest genetic effect would not have been detected by our study . This might have led to an increased type II error and an under-estimation of the true overlap in the genetic control of IBD and T1R/leprosy . Hence , studies employing larger numbers of T1R/leprosy patients might provide better estimates of the overlap in the genetic control of these two inflammatory conditions . The study was conducted according to the principles expressed in the declaration of Helsinki . Written informed consent was obtained for all adult subjects participating in the study . All minors assented to the study , and a parent or guardian provided the informed consent on their behalf . The study was approved by the regulatory authorities and ethics committees of the participating centers . Namely , Comissão Nacional de Ética em Pesquisa ( CONEP; 12638 ) for Goiania; The Research Ethics Committee at Fiocruz ( CEP-Fiocruz Protocol 151/01 ) for Rio de Janeiro; The Research Ethics Committee at Institute Lauro de Souza Lima for Rondonópolis ( 172/09 ) ; the Research Ethics Board at the RI-MUHC in Montreal ( REC98-041 ) , and the regulatory authorities of Ho Chi Minh City ( So3813/UB-VX and 4933/UBND-VX ) for the Vietnamese population . The subjects included in the study where followed up for a minimum of three years to confirm the presence or absence of T1R episodes . T1R-affected and T1R-free leprosy cases were mainly selected from the borderline class of Ridley and Jopling clinical scale of leprosy as T1R affects predominantly these cases that present an immunologically unstable immune response against M . leprae infection [7 , 35] . For the discovery phase , two sets of families of Vietnamese ( Kinh ) origin with leprosy-affected offspring were selected: the T1R-affected set comprised of 229 offspring belonging to 221 families and a T1R-free set comprised of 229 offspring in 209 families . The T1R-free set was matched to the T1R-affected set by the offspring’s leprosy clinical subtype . In the discovery phase , a transmission disequilibrium test ( TDT ) was applied to the T1R-affected and the T1R-free families independently . Next , the results of the individual TDTs were compared to investigate heterogeneity between both samples . The genetic heterogeneity test between T1R-affected and T1R-free subsets was tested by means of the FBATHet statistic and is detailed in the statistical approach section [36] . Variants that were associated in the T1R-affected set and showed heterogeneity with the T1R-free set were considered as T1R-specific and were investigated in the next phases of the study . The initial association results were followed up employing a replication and a validation phase . The replication sample was of Vietnamese ethnicity and encompassed 253 T1R-affected and 563 T1R-free leprosy patients . The validation sample comprised 471 T1R-affected subjects and 446 T1R-free leprosy patients as controls from the Central-west and South-east regions of Brazil as described previously [16 , 37 , 38] . In both replication and validation samples , cases and controls were matched for leprosy subtype . Genotypes of all subjects of the discovery phase were determined using the Illumina Human 660w Quad v1 bead chip . SNPs with call rate < 0 . 98 , more than two Mendelian errors in T1R-affected or T1R-free sets , minor allele frequency ( MAF ) < 0 . 01 or presented Hardy-Weinberg equilibrium ( p < 1 . 0 x 10−3 ) in 763 leprosy unaffected parents were removed from the analyses . Genotypes for the replication and validation phase samples were obtained through high-throughput SEQUENOM platform . The same quality control thresholds from the discovery phase were applied for SNP call rates and MAF exclusion to the replication and validation phase , with the exception of the HWE p value cut off which was restricted to p < 0 . 05 due to the lower number of tested SNPs compared to the discovery phase . A total of 38 , 753 genotyped A/T and C/G SNPs were removed prior to the phasing and imputation . The remaining 495 , 973 SNPs that passed the quality control filtering in the discovery phase were used to impute additional 11 . 5 million variants ( SNPs and INDELs ) in both T1R-affected and T1R-free family sets with SHAPEIT2 [39] and IMPUTE2 [40] software and the 1000 genomes Phase I v3 dataset containing 1092 individuals as the reference panel . Given the exploratory nature of the discovery phase , the threshold of imputation information measure ( Info ) > 0 . 5 was applied to capture most of the common variants ( MAF > 5% ) with reasonable confidence ( S5 Fig ) [41] , MAF > 0 . 001 and more than 10 informative families in both T1R-affected and T1R-free sets were used as a post-imputation quality control filtering for the association analyses . Imputed variants that were evaluated in the replication and validation phase had their genotypes confirmed in 440 subject of the discovery sample using the high-throughput SEQUENOM platform . In the discovery phase , a TDT was used to estimate non-random transmission of alleles from heterozygote parents to leprosy-affected offspring in both T1R-affected and T1R-free sets ( p Discovery ) . The analysis was carried out under a log-additive model using FBATdosage v2 . 6 for genotyped and imputed variants [42] . To contrast the TDT tests from the discovery phase a FBATHet test in T1R-affected and T1R-free sets was used ( p Heterogeneity ) . Briefly , heterogeneity of the allelic transmission rates in an endophenotype can be done in the FBATdosage framework by pooling the two subsets ( T1R-affected and T1R-free ) and contrasting the presence of the endophenotype T1R ( T1 = 1/V1 ) with the absence of T1R ( T2 = −1/V2 ) , where V1 and V2 denote the variance of the FBATdosage statistic for the each sample set , respectively [36] . Population-based association analyses were performed using logistic regression under a log-additive model and adjusting by the co-variables gender and age at leprosy diagnosis using PLINK v1 . 0 . 7 . The one-sided test was used with the alternative hypothesis that the T1R-risk alleles were also risk factors in the replication and validation samples . Multivariable analysis were performed with stepwise conditional logistic regression in SAS 9 . 3 . The haplotype analysis in the Brazilian sample was performed with THESIS v3 . 1 [43] . The linkage disequilibrium structure was evaluated with Haploview 4 . 1 [44] . To summarize the different steps of the study we used an inverse variance–weighted meta-analysis with a fixed-effect model and an alternative random-effect model proposed by Han and Eskin as implemented in the software METAL [45] and METASOFT [46] , respectively . To estimate the risk effect for the family-based design the un-transmitted allele from parents to T1R-affected offspring in the TDT was used as a pseudo-sib control . Briefly , up to three unaffected pseudo-sibs were created per family , one for each possible un-transmitted genotype . Subsequently , the original T1R-affected offspring were compared to the T1R-free pseudo-sibs in a matched case-control [47] . Under a log-additive model , TDT and pseudo-sibs analyses are equivalent [47] . Of note , METAL and METASOFT use standard errors and β coefficients to combine the statistics of each studied phase . In contrast to the replication and validation steps , a two-sided test was used in the combined analysis for the Vietnamese and Brazilian samples . To investigate if there was an enrichment of shared risk alleles between T1R and IBD , we used a hypergeometric test to systematically compare evidence of association with T1R in the Vietnamese . For instance , out of the 6 , 333 , 954 variants tested for association in our study 319 , 671 had p < 0 . 05 in the T1R-affected subset . Using the observed prior information of the number of variants with p < 0 . 05 , the hypergeometric test calculates the statistical significance of randomly selecting 22 variants with p < 0 . 05 when 208 variants ( number of variants from the IBD GWAS meta-analysis present in the T1R dataset ) were randomly drawn from a total of ~6 . 3 million . Here , the hypergeometric test corresponds to the one-tailed Fisher’s exact test . The same analytical approach was applied for the T1R specific variant in IBD , but in this analysis we used the number of variants with p < 0 . 05 and p heterogeneity < 0 . 05 out of a total of ~6 . 3 million variants of the GWAS . Since we tested for sharing of the same risk allele between T1R , and IBD , CD or Ulcerative Colitis ( UC ) one-tailed p values are reported . The same strategy was used in the three control phenotypes ( schizophrenia , height and blood metabolites . Since we tested for sharing of the same risk allele between T1R , and IBD , CD or one-tailed p values are reported . IBD meta-analysis data was freely available at the IBDgenetics website ( https://www . ibdgenetics . org/ ) [24 , 48] . Briefly , seven CD and eight UC collections with genome-wide data were combined with additional replication samples resulting in a total of 42 , 950 IBD cases and 53 , 536 health controls for the IBD meta-analysis [24 , 48] . Variants that surpassed p < 5 . 0 x 10−8 for association with IBD were reported as significant . Functional data for annotated SNPs were extracted from the GTeX ( http://www . gtexportal . org/home/ ) and Haploreg v4 http://www . broadinstitute . org/mammals/haploreg/haploreg . php databases . [19 , 20] The FBAT dosage is available at https://www . hgid . org/index . php ? menu=download
Leprosy still affects approximately 200 , 000 new victims each year . A major challenge of leprosy control is the prevention of permanent disability due to nerve damage . Nerve damage occurs if leprosy remains undiagnosed for extended periods or when patients undergo pathological inflammatory responses termed Type-1 Reactions ( T1R ) . T1R is a rare example where beneficial inflammatory responses are temporal separated from host pathological responses . There is strong experimental evidence that supports a role of host genetic factors in T1R susceptibility . Here , we employed a genome-wide association study ( GWAS ) to investigate susceptibility factors for T1R in Vietnamese families . We followed up the initial GWAS findings in independent population samples from Vietnam and Brazil and identified a set of cis-eQTL genetic variants for the ENSG00000235140 lncRNA as global risk factors for T1R . To test our proposal that T1R is a strong model for pathological inflammatory responses we evaluated if inflammatory bowel disease ( IBD ) genetic risk-factors were enriched among T1R risk factors . We observed that more than 10% of IBD-risk loci were nominally associated with risk for T1R suggesting a shared mechanism of excessive inflammatory response in the both disease etiologies .
You are an expert at summarizing long articles. Proceed to summarize the following text: Beginning January 2014 , Psychological Science gave authors the opportunity to signal open data and materials if they qualified for badges that accompanied published articles . Before badges , less than 3% of Psychological Science articles reported open data . After badges , 23% reported open data , with an accelerating trend; 39% reported open data in the first half of 2015 , an increase of more than an order of magnitude from baseline . There was no change over time in the low rates of data sharing among comparison journals . Moreover , reporting openness does not guarantee openness . When badges were earned , reportedly available data were more likely to be actually available , correct , usable , and complete than when badges were not earned . Open materials also increased to a weaker degree , and there was more variability among comparison journals . Badges are simple , effective signals to promote open practices and improve preservation of data and materials by using independent repositories . Signals rapidly communicate information such as values , beliefs , and identities to others [17–19] . Male peacocks signal fitness with elaborate feather displays , automobile drivers signal political identities with bumper stickers , and Chicagoans signal acceptance of yearly disappointment by wearing Cubs apparel . Badges are an easy means of signaling and incentivizing desirable behaviors . Journals can offer badges acknowledging open practices to authors who are willing and able to meet criteria to earn the badge ( https://osf . io/tvyxz/ ) . Badges acknowledging open practices signal that the journal values transparency , lets authors signal that they have met transparency standards for their research , and provides an immediate signal of accessible data , materials , or preregistration to readers . Badges allow adopting journals to take a low-risk policy change toward increased transparency . Compared , for example , to measures that require data deposition as a condition of publication , badge implementation is relatively resource-lite , badges are an incremental change in journal policy , and if badges are not valued by authors , they are ignored and business continues as usual . In January 2014 , Psychological Science ( PSCI ) adopted badges to acknowledge open data , open materials , and preregistration of research if published . Following the specifications maintained by the Center for Open Science ( http://cos . io/ ) Badges Committee for what it means to be “open data” or “open materials , ” the PSCI editorial team awarded one or more badges to authors who applied for them upon article acceptance and provided evidence to the editors that they met the specified criteria . To meet the criteria to earn an open data badge , authors must make all digitally shareable data relevant to the publication available on an open access repository . Similarly , to earn an open materials badge , authors must make all digitally shareable materials , such as survey items , stimulus materials , and experiment programs , available on an open access repository . Materials that cannot be shared digitally must be described in sufficient detail for an informed reader to know how to reproduce the protocol . Those who apply for a badge and meet open data or open materials specifications receive the corresponding badge symbol at the top of their paper and provide an explicit statement in the paper including a URL to the data or materials at an open repository . We did not include preregistration , the act of confirming an unalterable version of one’s research plan prior to collecting data , in this analysis . Preregistration requires initiating behaviors prior to starting the research and thus will require more time to see any impact in the published literature than in our assessment of impact within the first 1 . 5 years . We examined the impact of adopting badges by comparing data and material sharing rates before ( 2012–2013; PSCI before badges ) and after adoption ( 2014–May 2015; PSCI with badges ) in Psychological Science , and across the same time period in comparison journals from the same discipline ( journals without badges ) . Articles already in the publication process on January 1 , 2014 may not have had an opportunity to apply for badges even though their article appeared in 2014 . This suggests that the results reported in this article underestimate the overall impact of badges . The design and analysis of this study was preregistered at https://osf . io/ipkea/ , and all data and materials are available at https://osf . io/rfgdw/ . We preregistered an additional investigation involving reaching out to authors to evaluate accessibility of data or materials that were not shared upon publication . However , we postponed that part of data collection due to feasibility constraints . We used the population of empirical articles with studies based on experiment or observation ( N = 2 , 478 ) published in 2012 , 2013 , 2014 , and January through May 2015 issues of one journal that started awarding badges , Psychological Science ( PSCI; N = 838 ) , and four journals in the same discipline that did not: Journal of Personality and Social Psychology ( JPSP; N = 419 ) , Journal of Experimental Psychology: Learning , Memory , and Cognition ( JEPLMC; N = 483 ) , Developmental Psychology ( DP; N = 634 ) , and Clinical Psychological Science ( CPS; N = 104 ) . Psychological Science ( 2014 impact factor [IF] = 4 . 94 ) is a respected journal that publishes empirical research from any area of psychology . The four comparison journals ( 2014 IF’s 2 . 86 to 5 . 03 ) are respected journals that publish empirical research from a particular area of psychology represented by their titles . Clinical Psychological Science , which has only been publishing since January 2013 , does not yet have an estimate of impact . It was selected to represent clinical psychology and as the other empirical journal published by the Association for Psychological Science . A total of 220 additional articles published in these journals between 2012 and May 2015 are not part of this corpus because they were not reports of empirical research ( i . e . , editorials , theoretical reviews , commentaries ) . Coders were trained to reliably apply the coding scheme for assessing accessibility of data and research materials ( https://osf . io/4rf3v/ ) . Five trial articles from four journals included in this study , with content representative of a range of possible outcomes , were given to each coder . The first author’s coded responses to these articles were defined as the gold standard . Coders had to achieve 95% reliability with the gold standard before actual coding began . All questions , including free text answers , were included in this evaluation of reliability . If any response did not provide the same conclusion as the gold standard , it was marked incorrect . If 95% reliability was not met with the initial five , coders received three new trial articles and repeated the process . Once their responses were reliable , coders received additional coding instructions ( https://osf . io/er9xk/ ) and access to the population of articles . All articles had a unique , standardized identification number based on their order , month , year , and journal of publication . Coders selected articles solely by this identification number . Individual articles can be matched to their corresponding bibliographic metadata , available at https://osf . io/rmune/ . Availability of data and materials were coded using an identical coding structure . The full coding scheme illustrated in Fig 1 is available at https://osf . io/4rf3v/ . All of the variables used in analysis for this article are as follows: Badge awarded . Whether or not the article was awarded a badge for open data or open materials . Availability statement . Whether or not the article makes a statement regarding the location , available or unavailable , of its data or materials . Reported available . Whether or not the article specifically states the data or materials were available for use , including noting that the data or materials are not available . Reported location . If data or materials were available , what means were provided for accessing them: an independent archive/repository , personal website , independent website , journal supplement , appendix or table , or an indication that data or materials were available upon request . Actually available . Whether the data or materials reported available at a publicly accessible location were found at the expected location , excluding articles with data or materials in the article text , appendix , or journal-hosted supplement . Correct data/materials . If the data or materials could be retrieved , whether the data or materials corresponded to what was reported as being available . To determine correctness of open data , coders evaluated whether the type of data , the variables in the dataset , the number of participants , and the data contents matched the description provided in the manuscript . To determine correctness of open materials , coders evaluated whether the contents of the materials , such as survey items and stimuli , matched the description provided . Usable data/materials . If data or materials could be retrieved , whether the data or materials were understandable and usable after brief review . To determine the usability of data/materials , coders evaluated whether they felt the format of and the context provided with the data/materials would allow them to easily be used for their own purposes . Complete data/materials . If data or materials could be retrieved , whether all of the data or materials for reproducing the reported findings appeared to be available . To determine the completeness of the data/materials , coders evaluated whether all data/materials described for all studies in the article were accessible . We first examined whether articles’ reporting availability of data and materials increased over time , particularly in Psychological Science ( PSCI ) after badges were introduced on January 1 , 2014 . We examined the entire population of empirical articles from the target and comparison journals ( N = 2 , 478 ) ; as such , we used descriptive statistics to evaluate our research questions . In PSCI , for the half years prior to 2014 , an average of 2 . 5% of articles reported open data ( range = 1 . 5% to 4 . 0% per half year ) , and after January 1 , 2014 , increased monotonically with an average of 22 . 8% of articles reporting open data ( range = 12 . 8% to 39 . 4%; Fig 2 ) . Across the four comparison journals , for the half years prior to 2014 , an average of 3 . 3% of articles reported open data ( range = 1 . 6% to 4 . 9% ) , and after January 1 , 2014 , the average was 2 . 1% ( range = 1 . 8% to 2 . 3% ) . All four comparison journals had very low rates of articles reporting data availability ( JPSP = 4 . 5% , JEPLMC = 2 . 3% , DP = 2 . 4% , CPS = 1 . 0% ) . In PSCI , for the half years prior to 2014 , an average of 12 . 7% of articles reported open materials ( range = 6 . 1% to 17 . 7% ) , and after January 1 , 2014 , increased monotonically with an average of 30 . 3% reporting open materials ( range = 27 . 5% to 41 . 0%; Fig 3 ) . Across the four comparison journals , for the half years prior to 2014 , an average of 19 . 3% of articles reported open materials ( range = 16 . 2% to 23 . 4% ) , and after January 1 , 2014 , 20 . 6% ( range = 17 . 4% to 26 . 1% ) . The four comparison journals varied widely in rates of reporting materials availability ( JPSP = 32 . 2% , JEPLMC = 28 . 8% , DP = 6 . 6% , CPS = 9 . 6% ) . The social- and cognitive-psychology-oriented journals tended to report sharing materials more frequently . Usually , those were descriptions of surveys or stimulus items in an appendix for the article , rather than a complete description of the protocol . In summary , reported sharing of materials and especially data increased dramatically in Psychological Science after introducing badges , but did not change systematically in the comparison journals over the same time period . We next examined whether the introduction of badges was associated with an increase in the rate of using independent repositories . Repositories may provide greater quality assurance and guarantees of preservation than other storage locations such as personal web pages . For this analysis , we considered only those articles that reported sharing . Among PSCI articles reporting available data , 7 . 7% ( N = 1 ) before January 1 , 2014 , and 71 . 2% ( N = 52 ) after , reported that the data were available in an independent repository . Among comparison journal articles reporting available data , 9 . 7% ( N = 3 ) before January 1 , 2014 , and 26 . 7% ( N = 4 ) after , reported that the data were available in an independent repository . This suggests that , when data is shared , it is increasingly likely over time to be shared in an independent repository , and that availability of badges dramatically accelerates this trend . In fact , 46 of the 73 PSCI articles reporting data availability in 2014 and 2015 also earned a badge , and 100% of those 46 reported being in an independent repository . Similarly , among PSCI articles reporting available materials , 0% ( N = 0 ) before January 1 , 2014 , and 45 . 4% ( N = 44 ) after reported that the materials were available in an independent repository . Among comparison journal articles reporting available materials , 0% ( N = 0 ) of materials before January 1 , 2014 , and 2 . 0% ( N = 3 ) after reported that the materials were available in an independent repository . Again , 38 of the 97 PSCI articles reporting materials availability in 2014 and 2015 also earned a badge , and 100% of those 38 reported being in an independent repository . The first results showed nearly a 10-fold increase in reported availability of data for PSCI with badges ( 22 . 8% ) , compared with PSCI before badges ( 2 . 5% ) and the four journals without badges combined ( 2 . 8% ) . Effects were similar but weaker for materials ( 30 . 3% , 12 . 7% , and 19 . 9% , respectively ) . However , reporting availability of data and materials does not guarantee that they are available , or that they are correct , usable , and complete . Do badges increase the likelihood that reported available data and materials are actually available , correct , usable , and complete ? It is possible that badging is sufficient to increase motivation to claim the behavior , but not sufficient to increase performing the behavior . However , the specified criteria for earning the badge , the simple editorial checks on meeting those criteria , and the visibility of the badge may all stimulate sharing behavior . The present investigation leverages a naturalistic intervention that occurred at Psychological Science and not at similar journals in the same discipline . However , opportunities to earn badges were not randomly assigned to journals or authors . This necessarily weakens the certainty of causal inference . Nonetheless , we assert a causal interpretation that badges promote data sharing because of the implausibility of alternative explanations . The most obvious alternative explanation is that the adoption of badges changed the population of authors submitting or earning acceptance at Psychological Science dramatically toward that very small minority ( <3% ) that shares data and materials even when badges are not offered . Given that Psychological Science has extremely high rejection rates ( ~93% ) , such a scenario would require a rapid and sizable shift in population submitting to the journal [21] . In comparison , rejection rates at Clinical Psychological Science , Developmental Psychology , Journal of Experimental Psychology: Learning , Memory , and Cognition , and Journal of Personality and Social Psychology are ~77% , 80% , 78% , and 89% , respectively ( personal communication , [22] ) . Also , given Psychological Science’s status , concerns about optional sharing would need to exceed the perceived value of publishing in the field’s premiere empirical outlet . Moreover , many of the manuscript submissions of articles published in 2014 would have occurred in 2013—prior to the announcement of the new policy . Another weakness in the present research is that the evaluations of data and material accessibility , correctness , usability , and completeness were the result of coder assessments and did not include reanalysis of the data or reuse of the materials . Such an effort would provide complementary insight on the extent to which this increase in transparency translates to an increase in reproducibility . Another research opportunity with the existing data is to code the research domains of the PSCI articles with badges to see if data and materials sharing rates accelerated more quickly in some subfields compared to others . All data reported in this paper are available at https://osf . io/u6g7t/ to facilitate follow-up investigation . Finally , badges are not a panacea . Sharing rates increased dramatically , but not all data or materials that could be shared were shared . Moreover , even with badges , the accessibility , correctness , usability , and completeness of the shared data and materials was not 100% . Some incompleteness could be attributable to gaps in the specifications for earning badges . For example , in late 2015 , the Center for Open Science Badges Committee ( http://osf . io/tvyxz ) considered provisions for situations in which the data or materials for which a badge was issued somehow disappear from public view . Adherence to badge specifications can also be improved by providing easy procedures for editors or journal staff to validate data and material availability before issuing a badge , and by providing community guidelines for validation and enforcement . Broader adoption of badges across journals will accelerate the accumulation of evidence about their effectiveness and will facilitate the refinement of specifications for badge awards and the process of badge administration . For example , the Center for Open Science is collaborating with publishers and others to create a badge “bakery” that inserts metadata about the issuer , recipient , and location of resources into the badge itself . As digital objects , the badges could then be searched , indexed , and maintained programmatically , which would increase their value for monitoring transparent practices . Badges will not be sufficient to make transparent all data and materials that could be made publicly accessible . Additional interventions will include government , funder , or publisher mandates , such as some of the more stringent standards offered in the Transparency and Openness Promotion ( TOP ) Guidelines ( http://cos . io/top/ ) [14] . Evaluation of different mechanisms for promoting or requiring transparency will help reveal the most efficient and effective methods . And , of course , 100% availability is unlikely because of ethics , privacy , and intellectual property exceptions . However , badges and additional interventions can shift the culture to make sharing the default . Badges may seem more appropriate for scouts than scientists , and some have suggested that badges are not needed [23] . However , actual evidence suggests that this very simple intervention is sufficient to overcome some barriers to sharing data and materials . Badges signal a valued behavior , and the specifications for earning the badges offer simple guides for enacting that behavior . Moreover , the mere fact that the journal engages authors with the possibility of promoting transparency by earning a badge may spur authors to act on their scientific values . Whatever the mechanism , the present results suggest that offering badges can increase sharing by up to an order of magnitude or more . With high return coupled with comparatively little cost , risk , or bureaucratic requirements , what’s not to like ?
Openness is a core value of scientific practice . The sharing of research materials and data facilitates critique , extension , and application within the scientific community , yet current norms provide few incentives for researchers to share evidence underlying scientific claims . In January 2014 , the journal Psychological Science adopted such an incentive by offering “badges” to acknowledge and signal open practices in publications . In this study , we evaluated the effect that two types of badges—Open Data badges and Open Materials badges—have had on reported data and material sharing , as well as on the actual availability , correctness , usability , and completeness of those data and materials both in Psychological Science and in four comparison journals . We report an increase in reported data sharing of more than an order of magnitude from baseline in Psychological Science , as well as an increase in reported materials sharing , although to a weaker degree . Moreover , we show that reportedly available data and materials were more accessible , correct , usable , and complete when badges were earned . We demonstrate that badges are effective incentives that improve the openness , accessibility , and persistence of data and materials that underlie scientific research .
You are an expert at summarizing long articles. Proceed to summarize the following text: Heme metabolism is central to malaria parasite biology . The parasite acquires heme from host hemoglobin in the intraerythrocytic stages and stores it as hemozoin to prevent free heme toxicity . The parasite can also synthesize heme de novo , and all the enzymes in the pathway are characterized . To study the role of the dual heme sources in malaria parasite growth and development , we knocked out the first enzyme , δ-aminolevulinate synthase ( ALAS ) , and the last enzyme , ferrochelatase ( FC ) , in the heme-biosynthetic pathway of Plasmodium berghei ( Pb ) . The wild-type and knockout ( KO ) parasites had similar intraerythrocytic growth patterns in mice . We carried out in vitro radiolabeling of heme in Pb-infected mouse reticulocytes and Plasmodium falciparum-infected human RBCs using [4-14C] aminolevulinic acid ( ALA ) . We found that the parasites incorporated both host hemoglobin-heme and parasite-synthesized heme into hemozoin and mitochondrial cytochromes . The similar fates of the two heme sources suggest that they may serve as backup mechanisms to provide heme in the intraerythrocytic stages . Nevertheless , the de novo pathway is absolutely essential for parasite development in the mosquito and liver stages . PbKO parasites formed drastically reduced oocysts and did not form sporozoites in the salivary glands . Oocyst production in PbALASKO parasites recovered when mosquitoes received an ALA supplement . PbALASKO sporozoites could infect mice only when the mice received an ALA supplement . Our results indicate the potential for new therapeutic interventions targeting the heme-biosynthetic pathway in the parasite during the mosquito and liver stages . Plasmodium falciparum ( Pf ) and Plasmodium vivax account for more than 95% of human malaria . P . falciparum is widely resistant to the antimalarial drugs chloroquine ( CQ ) and antifolates . Sporadic resistance is also seen in P . vivax [1] . Emerging resistance to the artemisinin-based combination therapies [2] and the absence of an effective vaccine highlight an urgent need to develop new drug targets and vaccine candidates [3] , [4] . The de novo heme-biosynthetic pathway of the malaria parasite offers potential drug targets and new vaccine candidates . The malaria parasite is capable of de novo heme biosynthesis despite its ability to acquire heme from red blood cell ( RBC ) hemoglobin . During the intraerythrocytic stages , the parasite detoxifies hemoglobin-heme by converting it into hemozoin [5] , [6] . The source of the heme used in the parasite mitochondrial cytochromes and the parasite heme requirements during the mosquito and liver stages are yet unknown . Hence , the role of the de novo heme-biosynthetic pathway throughout the entire parasite life cycle is a subject of considerable interest [7] . Detailed studies in our laboratory and elsewhere have completely characterized all the enzymes in P . falciparum heme-biosynthetic pathway . The parasite enzymes are unique in terms of their localization and catalytic efficiencies . The first enzyme , δ-aminolevulinate synthase ( PfALAS ) [8] , [9] , and the last two enzymes , Protoporphyrinogen IX oxidase ( PfPPO ) and Ferrochelatase ( PfFC ) [10] , [11] localize to the mitochondrion . The enzymes that catalyze the intermediate steps: ALA dehydratase ( PfALAD ) [12] , [13] , Porphobilinogen deaminase ( PfPBGD ) [9] , [14] , and Uroporphyrinogen III decarboxylase ( PfUROD ) [15] localize to the apicoplast ( a chloroplast relic ) , whereas , the next enzyme Coproporphyrinogen III oxidase ( PfCPO ) is cytosolic [16] . Figure 1 depicts the pathway . The enzymes that localize to the apicoplast have very low catalytic efficiency compared with RBC counterparts [17] , [18] . Earlier studies showed that host ALAD and FC are imported into the parasites in the intraerythrocytic stages , suggesting that the host machinery may augment parasite heme synthesis [6] , [19] . The apicoplast is involved in the synthesis of heme , fatty acids , iron-sulfur proteins , and isoprenoids [20] . Yeh and Risi [21] showed that a chemical knockout of apicoplast function could be rescued by isopentenyl pyrophosphate supplement to P . falciparum cultures in vitro . This suggests that during the intraerythrocytic stages , the parasite requires apicoplast function for isoprenoid synthesis but not for heme or fatty acid synthesis . However , heme as such is essential for parasite survival in the intraerythrocytic stages , minimally constituting the cytochrome component of the Electron Transport Chain ( ETC ) . The ETC is used as a sink for electrons generated in the pyrimidine pathway [22] . Atovaquone inhibits parasite growth by inhibiting cytochrome bc1 activity of the ETC , most likely by competitively inhibiting the cytochrome b quinone oxidation site [23] , [24] . Previously , we showed that PfPPO requires the ETC and is likewise inhibited by atovaquone [10] . Heme can also serve as a source of iron for the iron-sulfur proteins involved in isopentenyl pyrophosphate synthesis [20] . The question arises whether the parasite depends on de novo heme biosynthesis or heme from hemoglobin or a combination of both to make mitochondrial cytochromes . The steps involved in the acquisition of heme from RBC hemoglobin and the storage of heme as hemozoin in the food vacuole of the parasite are reasonably well understood [7] , [25] . In addition to the possibility of acquiring heme from hemoglobin to make cytochromes in the blood stages , there is also a suggestion that Plasmodium may be able to scavenge heme in the liver stages as well , as is the case with organisms infecting nucleated cells such as T . cruzi , Leishmania and M . tuberculosis [7] . A direct approach to examine the role of the heme-biosynthetic pathway throughout the Plasmodium life cycle , including the sexual stages in the mosquitoes and liver stages in the animal host , is to knockout genes in the pathway and determine the effect of the knockouts ( KOs ) using the P . berghei ( Pb ) -infected mouse model . We used an in vivo animal model of parasite infection to determine the role of heme biosynthesis during all the stages of parasite development . Figure 2A depicts the double crossover recombination strategy followed to obtain PbALAS and PbFC KOs . Table S1 shows the primers used to amplify the 5′ upstream and 3′ downstream regions of PbALAS and PbFC . Figure 2B–M shows the detailed characterization of the KOs based on RT-PCR , Southern , Northern , and Western analyses . We bypassed the liver stage of the infection cycle by injecting 105 intraerythrocytic-stage parasites intraperitoneally into mice . There was no significant difference in the growth of the PbKO parasites compared with the PbWT parasites ( Figure 3 ) . These results indicate that the parasite may be acquiring host heme during the intraerythrocytic stages . The potential of human RBCs and mouse reticulocytes to synthesize heme was explored in this study . We detected the ALAS and FC proteins by Western analysis in mouse reticulocytes but not in human RBCs ( Figure S1A and B ) . Unlike in human RBCs , it was possible to radiolabel the total heme and hemoglobin-heme in short-term mouse reticulocyte cultures incubated with [4-14C]ALA ( Figure S1C–G ) . Because P . berghei prefers reticulocytes , the experimental system made it feasible to study the availability of hemoglobin-heme not only for hemozoin formation but also for parasite cytochromes . Furthermore , we were able to block heme labeling in the mouse reticulocyte cultures using succinyl acetone ( SA ) , a specific inhibitor of ALAD ( Figure S2A–C ) . Since P . berghei can only grow but poorly infect fresh reticulocytes in vitro , reticulocytes infected in vivo with PbWT and PbKO parasites were used to perform short-term radiolabeling experiments in the presence of [4-14C]ALA . We found [4-14C]ALA incorporation into total heme and hemozoin-heme of PbWT parasites and both of the PbKO parasites . SA inhibited the radiolabeling ( Figure 4A and B ) . Radiolabeled heme appearing in the PbWT and PbALASKO parasites could come from host hemoglobin as well as from parasite heme biosynthesis . But , we would not expect to find [4-14C]ALA incorporated into the heme synthesized by the PbFCKO parasites . The ethyl acetate∶acetic acid mixture used to extract heme did not extract hemozoin . Therefore , we extracted hemozoin using acid-acetone solvent . We analyzed the labeling of mitochondrial proteins by non-denaturing PAGE and observed a sharp band at the top of the gel after silver staining . The band was radiolabeled in PbWT parasites and in both of the PbKO parasites . The radiolabeling was almost completely inhibited by SA ( Figure 4C–E ) . SDS-PAGE analysis of the band excised and eluted from non-denaturing PAGE showed five separate protein bands and MALDI analysis revealed the presence of two cytochrome oxidase subunits . The sharp silver-stained band in non-denaturing PAGE thus appeared to represent a complex of proteins and needs to be further characterized in detail ( Figure S3 ) . For now , it is clear that the PbWT parasites and both of the PbKO parasites incorporated hemoglobin-heme into mitochondrial hemoproteins and into hemozoin . Next , we examined whether the parasite could use hemozoin-heme to make mitochondrial cytochromes . We tested the effect of CQ , which is known to block hemozoin formation [26] , on P . berghei-infected short-term reticulocyte cultures . PbFCKO parasite was used to avoid any contribution from parasite-synthesized heme . CQ was injected into PbFCKO-infected mice as described in the Materials and Methods . After 7 h , the infected reticulocytes were incubated in short-term cultures and the incorporation of [4-14C]ALA into hemozoin and mitochondrial cytochromes over a period of 9 h was measured . We resorted to in vivo treatment of the animals with the drug , since we found that direct addition of the drug to reticulocyte culture failed to inhibit hemozoin formation under the conditions used , even at high concentrations . Figures 4F and G show that the CQ treatment inhibited hemozoin labeling by 70% but did not affect the labeling of mitochondrial cytochromes . These results suggest that host hemoglobin may provide heme to mitochondrial cytochromes and hemozoin through independent pathways . The radiolabeling of hemoglobin-heme made it impossible to assess the contribution of parasite-synthesized heme using [4-14C]ALA in P . berghei-infected reticulocytes . We could , however , assess the contribution of parasite-synthesized heme in P . falciparum cultures . In those cultures , all of the radiolabeled heme was synthesized by the parasite . The hemoglobin-heme was not radiolabeled in the P . falciparum cultures because the human RBCs used in the in vitro cultures lacked the mitochondrial enzymes required to synthesize heme ( Figure S1 ) . Although not radiolabeled , the preformed hemoglobin in the RBCs could act as a heme source for the parasite . We found [4-14C]ALA incorporation into the total heme , hemozoin-heme , and mitochondrial hemoproteins in the P . falciparum cultures . SA ( 50 µM ) inhibited the radiolabeling ( Figure 4H–J ) . Earlier studies used SA at a fixed concentration ranging from 1 to 2 mM to inhibit heme synthesis and parasite growth [5] . The present study showed that while the 50% growth inhibitory concentration was around 1 to 2 mM ( Figure S4A ) , concentrations as low as 50 µM inhibited heme synthesis ( Figure 4H–J ) . We observed similar results in short-term P . berghei cultures ( Figure S4B ) . The P . falciparum mitochondrial cytochromes also formed a complex in non-denaturing PAGE and need to be further characterized in detail . Thus , we showed that both hemoglobin-heme and parasite-synthesized heme could be incorporated into hemozoin in the food vacuole and into mitochondrial cytochromes . Hemozoin formation from host hemoglobin in P . falciparum is well characterized [25] . Hemozoin formation from heme synthesized in the parasite mitochondrion , however , needs to be studied further . The relative contributions of hemoglobin-heme and parasite-synthesized heme to parasite cytochrome biosynthesis during the intraerythrocytic stages need to be assessed under different environmental conditions . To examine the role of parasite-synthesized heme in the mosquito stages , we allowed Anopheles mosquitoes to feed on mice infected with PbWT and PbKO parasites . Figure 5 shows that both PbWT and PbKO parasites formed ookinetes . We found no difference between the WT and KO ookinetes in vitro using gametocyte cultures or in vivo using midgut preparations . In contrast , Figure 6 shows a drastic decrease in PbKO oocysts formation in the midgut and absence of PbKO sporozoites in the salivary glands . We examined whether ALA supplement could overcome the block in PbALASKO parasites for which 0 . 1% ALA was supplemented in feeding solution ( PbALASKO ( Mq+ALA ) ) . The results obtained indicate that the formation of oocysts and sporozoites were restored ( Figure 6 ) . Our results reveal that parasite heme synthesis was required for oocyst and sporozoite development in the mosquitoes . In the case of PbFCKO parasites , we attempted to supplement heme through blood feeding on mice , but we were not able to rescue the defect . This suggests that the parasite could not acquire heme from the mouse hemoglobin in the mosquito blood meal or from any other mosquito source during the sexual stages of its development . We examined the ability of PbALASKO ( Mq+ALA ) sporozoites to reinfect mice by measuring the parasitemia in the mice on subsequent days with and without ALA supplement ( 0 . 1% in drinking water ) . We did not detect any parasites in the mice infected with PbALASKO ( Mq+ALA ) sporozoites that did not receive ALA supplement ( PbALASKO ( Mq+ALAMi−ALA ) ) . We did , however , detect parasites in the mice infected with PbALASKO ( Mq+ALA ) sporozoites that received ALA supplement ( PbALASKO ( Mq+ALAMi+ALA ) ) . The infected animals died after 14–16 days , when the parasitemia levels reached around 60% ( Figure 7 ) . Mosquitoes infected with PbALASKO parasites ( without ALA supplement ) failed to give rise to blood-stage parasites in mice when we allowed them to feed . This is an additional proof to suggest that the PbALASKO parasites did not form sporozoites in the mosquito salivary glands . We reproduced all the mosquito transmission experiments by intravenously injecting the sporozoites obtained from mosquito salivary gland extracts into mice . Thus , our results suggest that parasite heme synthesis is absolutely essential for liver-stage development . Our results discount the suggestion [7] that the parasite may import host-synthesized heme during the liver stage . In this study , we assessed the role of parasite-synthesized heme in all stages of malaria parasite growth . We generated ALAS and FC KOs in P . berghei . We used the KOs to track parasite-synthesized heme and host hemoglobin-heme during the intraerythrocytic stages of the parasite . The KOs did not affect parasite growth in mice when the parasites were injected intraperitoneally . All infected animals died within 10 to 12 days , when parasitemia reached around 60% . The synthesis of mitochondrial cytochromes is essential for parasite survival , so our results mean that the PbKO parasites used hemoglobin-heme to synthesize cytochromes during the intraerythrocytic stages . We demonstrated this by radiolabeling hemoglobin-heme with [4-14C]ALA in short-term mouse reticulocyte cultures . In the short-term in vitro P . berghei cultures , we found radiolabeled hemozoin and mitochondrial cytochromes in reticulocytes infected with PbWT , PbALASKO , and PbFCKO parasites . We could not , however , distinguish between the contributions of hemoglobin-heme and parasite-synthesized heme in those cultures , because the use of [4-14C]ALA to radiolabel heme would bypass the potential ALASKO block . At the same time , the PbFCKO parasites would not be able to incorporate [4-14C]ALA into heme . We showed in a prior study that P . berghei imports host ALAD as well as host FC [6] . Therefore , we cannot rule out the possibility that the parasite used FC imported from the host to synthesize heme . We addressed this possibility using P . falciparum in human RBC culture . Western analysis indicated that the human RBCs used to culture P . falciparum did not contain detectable levels of ALAS and FC . Again , the RBCs did not incorporate [4-14C]ALA into heme ( Figure S1 ) . Thus , all of the radiolabeled heme in P . falciparum was synthesized de novo by the parasite . We found that 50 µM SA completely inhibited heme synthesis in P . falciparum ( Figure 4H–J ) but did not affect parasite growth ( Figure S4A ) . This means that P . falciparum can use hemoglobin-heme to sustain growth under these conditions . Earlier studies used a fixed , high concentration of SA ( 1–2 mM ) [5] , which inhibited both heme synthesis and parasite growth . In this study , SA was found to inhibit heme synthesis at a much lower concentration than that required to inhibit parasite growth , indicating that de novo heme synthesis is not essential for P . falciparum growth in culture . This is likely to be true of P . berghei as well , because 50 µM SA completely inhibited heme synthesis in P . berghei-infected reticulocytes ( Figure 4A–E ) but did not affect P . berghei growth in short-term cultures ( Figure S4B ) . The earlier studies correlating the growth of the parasite with inhibition of heme synthesis or host enzyme import [5] , [6] , [17] have now been re-evaluated with the use of specific gene KOs in the pathway . Because the parasite can survive in the absence of de novo heme synthesis , it may appear that the parasite heme-biosynthetic pathway has no role in the intraerythrocytic stages . However , our results show for the first time that P . falciparum growing in human RBCs incorporated parasite-synthesized heme radiolabeled with [4-14C]ALA into hemozoin as well as into mitochondrial cytochromes . Hemoglobin-heme in the RBCs was not radiolabeled; so the heme in the parasite hemozoin and mitochondrial cytochromes was synthesized de novo by the parasite . It has long been assumed that only hemoglobin-heme is converted into hemozoin in the parasite food vacuole . We found that parasite-synthesized heme can also give rise to hemozoin in the food vacuole . Since hemoglobin transport into the food vacuole involves cytostomes and other vesicle-mediated transformations [25] , it is not clear at this stage how the parasite-synthesized heme made in the mitochondrion finds its way to the food vacuole . Our results also emphasize the fact that hemozoin is , perhaps , the only mechanism for heme detoxification in the parasite . A recent study showed that the malaria parasite lacks the canonical heme oxygenase pathway for heme degradation and relies on hemozoin formation to detoxify heme [27] , although an earlier study suggested the possible presence of heme oxygenase in the apicoplast [7] , [28] . It appears that the parasite mitochondrion would need a two-way transporter for heme: one to incorporate hemoglobin-heme into the mitochondrion and another to transport mitochondrial heme into the pathway leading to hemozoin formation in the food vacuole . Free heme was also detected in the erythrocyte at a concentration around 1 µM [29] and the parasite may be able to scavenge this heme directly [7] . It was also suggested that ferriprotoporphyrin could leach from the food vacuole into the parasite cytosol [30] . We found that SA inhibited the radiolabeling of hemozoin and of mitochondrial cytochromes in PbFCKO parasites . But , CQ inhibited the radiolabeling of hemozoin but not of mitochondrial cytochromes ( Figure 4F and G ) . These results suggest that hemoglobin-heme may be incorporated into mitochondrial cytochromes and into hemozoin through independent processes . Figure 8 gives some of the pathways that may be involved . It needs to be established whether hemoglobin-heme and parasite-synthesized heme are functionally equivalent . The parasite-synthesized heme may be a backup mechanism that could be of significance only if hemoglobin-heme is not available , as may be the case with sickle cell and other hematological disorders . It has been proposed that low levels of free heme in the plasma induce heme oxygenase-1 to generate carbon monoxide that binds with sickle hemoglobin-heme . This could prevent the release of the heme , and thus suppress the heme-mediated pathogenesis of cerebral malaria , without affecting the parasite load [31] . In another scenario , it was suggested that human hemoglobin variants offer protection by interfering with host actin remodeling in P . falciparum-infected erythrocytes . These variant hemoglobins are unstable and undergo oxidation , leading to the denaturation and release of heme and oxidized forms of iron that can affect host actin dynamics and thus affect parasite virulence . However , malaria parasites develop normally in such erythrocytes , both in culture and in vivo [32] . Therefore , parasite-synthesized heme may sustain parasite survival when hemoglobin-heme is unavailable , although pathogenesis is ameliorated . It is also possible that the parasite-synthesized heme has a function that is presently unknown . The growth pattern of the KO parasites in the mosquito stages was striking . While ookinetes formed , oocysts formation decreased substantially , and no sporozoites appeared in the salivary glands . Furthermore , when these mosquitoes fed on mice , we found no intraerythrocytic-stage parasites in the blood of the mice . ALA supplement to the mosquitoes enabled PbALASKO to form oocysts and sporozoites . This is clear proof that de novo parasite heme synthesis is required for parasite development in mosquitoes . Hence , inhibitors of heme and porphyrin synthesis , such as diphenyl ether herbicides , can be explored to prevent parasite development in mosquitoes [33] . Equally striking was the growth pattern of PbALASKO parasites in the liver stage . The sporozoites formed in the mosquitoes with ALA supplement could infect mice only when the mice received ALA supplement . This again shows that parasite de novo heme synthesis is required for development in the liver stage . The liver stage is a major focus of malaria interventions and the role of parasite heme synthesis in liver-stage development needs to be investigated in more detail . Inhibitors of parasite heme synthesis offer newer drug candidates for blocking infection and transmission , since the parasite enzymes involved have unique properties [10] , [11] , [14]–[18] . Irradiated sporozoites serve as a malaria vaccine candidate [4] . There are several current efforts to design and stabilize irradiated sporozoites for large-scale clinical trials [34]–[36] . Our results with PbALASKO ( Mq+ALA ) sporozoite infections in mice offer some additional options for a genetically attenuated sporozoite vaccine that can be tested in the animal model . The biology of parasite heme synthesis may change drastically between the intraerythrocytic stages and the mosquito and liver stages . The malaria parasite essentially depends on glycolysis to generate ATP in the intraerythrocytic stages . Hemoglobin is available as a heme source in addition to parasite-synthesized heme . In the mosquito and liver stages , the parasite depends entirely on its own biosynthetic machinery to provide heme . It is possible that the de novo heme-biosynthetic pathway of the parasite is augmented during the mosquito and liver stages . The ATP synthesized by the ETC may be necessary to provide the energy needed for ookinetes in the mosquito midgut to develop into sporozoites in the mosquito salivary glands . The energy provided by the ETC may also be necessary for the sporozoites to explore the mammalian host from the skin to liver and give rise to merozoites in the hepatocytes . Animal experiments were carried out as per the guidelines of the Committee for the Purpose of Control and Supervision of Experiments on Animals ( CPCSEA ) , Government of India ( Registration No: 48/1999/CPCSEA ) . The guidelines of National Institute of Malaria Research , New Delhi , were followed for all the mosquito infection studies . All the experiments were carried out as approved by the Institutional Animal Ethics Committee of the Indian Institute of Science , Bangalore ( CAF/Ethics/102/2007-435 and CAF/Ethics/192/2010 ) . In vitro cultures for P . falciparum 3D7 isolate were maintained continuously on human O+ red cells of 5% hematocrit supplemented with 10% O+ serum or 0 . 5% Albumax II in RPMI 1640 medium containing L-glutamine ( GIBCO ) by the candle jar method [37] or in a CO2 incubator . Synchronization was carried out by sorbitol treatment [38] and parasites at the late trophozoite and schizont stages were freed from infected erythrocytes by treatment with an equal volume of 0 . 15% ( w/v ) saponin in PBS [39] . The released parasites were centrifuged at 10 , 000× g for 10 minutes and the pellet obtained was washed four times with ice cold PBS to remove any detectable hemoglobin . The routine propagation of P . berghei ANKA strain ( MRA-311 , MR4 , ATCC Manassas Virginia ) was carried out in 6–8 weeks old Swiss mice . In brief , mice were injected intraperitoneally with 105 P . berghei infected-RBCs/reticulocytes and the parasite growth was routinely monitored by assessing the percentage of parasitemia in Giemsa stained thin smears prepared from tail vein blood . On day 8–10 post-infection , mice were anesthetized with ketamine/xylazine and the infected blood was collected through cardiac puncture . The blood obtained was diluted with PBS to initiate fresh infections in mice [40] , [41] . Parasite isolation was carried out as described earlier [39] . To generate the knockout parasites , primers were designed and PCR was carried out with P . berghei genomic DNA to amplify the 670–740 bp fragments that correspond to the 5′- UTR and 3′-UTR regions of PbALAS/FC genes . The resultant fragments were cloned into the appropriate restriction sites flanking the human DHFR selection cassette of pL0006 replacement plasmid ( MRA-755 , MR4 , ATCC Manassas Virginia ) . The plasmid constructs were then digested with ApaI and NotI , and transfected into P . berghei schizonts that were purified from intraerythrocytic stage infections initiated by sporozoite injections [42] . In brief , P . berghei schizonts were purified and subjected to nucleofection with the appropriate constructs , followed by pyrimethamine selection . Limiting dilution was carried out for pyrimethamine-resistant parasites [43] and the targeted deletion of PbALAS and PbFC genes in the respective knockout parasites were confirmed by PCR , Southern , Northern and Western analyses . The details of the primers and restriction sites are provided in Table S1 . A . stephensi mosquitoes were reared under standard insectary conditions maintained at 27°C and 75–80% humidity with a 12 h light and dark photo-period as described [44] , [45] . Larvae were reared on yeast tablets at a fixed density of one larva per ml . Upon maturation , the pupae were segregated for adult emergence . The emerged adult mosquitoes were fed on filter-sterilized 10% glucose solution containing 0 . 05% paraminobenzoic acid . For egg production , adult female mosquitoes were allowed to take blood feeding on mice anesthezied with ketamine/xylazine . P . berghei infection studies in A . stephensi mosquitoes were carried out as described elsewhere [46]–[48] . In brief , antibiotic-treated adult female mosquitoes of 5–7 days old , starved for 12 h , were allowed to feed on anesthetized-P . berghei infected mice with 8–12% parasitemia showing 2–4 exflagellation centres per field . The fully engorged mosquitoes were then separated and maintained at 19°C . At 20 h post feeding , the mosquito midguts were dissected to remove the blood bolus and ookinete numbers were quantified as described [49] . On day 10 post feeding , mercurochrome staining was carried out for the dissected midguts to determine the number of oocysts formed [50] , followed by the dissection of salivary glands on day 19 to examine and count the number of sporozoites present [51] . To supplement the PbALASKO-infected mosquitoes , routine feeding was carried out with sugar solution containing 0 . 1% ALA from 20 h post feeding until the dissection of salivary glands on day 19 . To supplement PbFCKO-infected mosquitoes , blood feeding was given to the mosquitoes in six days interval from the day of infection till the sporozoite analysis , besides the routine feeding with sugar solution . The ability of the sporozoites to develop asexual stage infections was studied by allowing the mosquitoes infected with P . berghei wild-type and knockout parasites to feed for 15–20 min on 6–8 weeks old Swiss mice ( 30 mosquitoes/mouse ) anesthetized with ketamine/xylazine . The development of asexual stage parasites was monitored by examining the Giemsa stained blood smears from day 5 post infection . To inject 104 sporozoites intravenously in mice , salivary gland extracts of the infected mosquitoes were prepared and sporozoites were counted as described [51] . ALA supplement in mice was carried out immediately after sporozoite infection and continued for 7 days by including 0 . 1% ALA in drinking water . In vitro radiolabeling of heme in mouse reticulocytes was carried out at 37°C in a CO2 incubator , for a period of 9 h in RPMI-1640 medium containing 10% FBS , by adding 1 µCi of [4-14C]ALA to a total volume of 5 ml containing 109 reticulocytes . In brief , reticulocytosis was induced in mice by injecting a single dose of phenylhydrazine ( 2 . 5 mg in saline/mouse ) intraperitoneally . Two days later , reticulocytes from the mice blood were separated by performing the density-gradient centrifugation on isotonic percoll [52] , washed thrice with the medium and used for labeling . Labeling studies in human RBCs were also carried out in a similar fashion with 109 human RBCs in RPMI-medium containing 10% human serum . To perform in vitro labeling for the intraerythrocytic stages of P . berghei wild-type and knockout parasites , the respective blood-stage infections were initiated in phenylhydrazine-treated mice by intraperitoneal injection of 105 infected erythrocytes . The blood was collected when the parasitemia reached around 5–8% with parasites predominantly in early trophozoites . After washing thrice with RPMI-1640 containing FBS , the cells were resuspended in 10 ml of the medium to a final hematocrit of 5% and labeling was carried out for 9 h as described for reticulocytes by adding 3 µCi of [4-14C]ALA . To study the in vitro effect of SA on heme labeling , cultures were treated for 3 h with 50 µM SA prior to the addition of [4-14C]ALA , and the labeling was carried out for 9 h in the presence of SA . For CQ treatment , PbFCKO-infected mice were injected intraperitoneally with two doses of 0 . 5 mg CQ dissolved in water at 6 h time interval , when the blood stage parasites were predominantly in early rings . The blood was collected 1 h after the second dosage and the cells were washed with medium , followed by in vitro labeling for 9 h with 3 µCi of [4-14C]ALA . In vitro labeling for P . falciparum in the presence and absence of SA was carried out with synchronized cultures harbouring 5–8% early trophozoites maintained in RPMI-1640 containing 10% O+ serum or 0 . 5% Albumax II . Mitochondria isolation was carried out as described [53] by homogenizing the parasite pellet in 10 volumes of buffer pH 7 . 4 containing 5 mM Hepes-KOH , 75 mM sucrose , 225 mM mannitol , 5 mM MgCl2 , 5 mM KH2PO4 and 1 mM EGTA with protease inhibitors . The homogenate was then centrifuged at 4500×g for 5 min at 4°C and the supernatant obtained was subjected to 44 , 700×g for 7 min at 4°C to pellet mitochondria . Labeling of hemoproteins in the parasite mitochondria was examined by solubilizing the pellet in 20 mM Tris buffer pH 7 . 5 containing 5% Triton X-100 and protease inhibitors , and centrifuging at 20 , 000×g to remove membrane debris , followed by loading the supernatant on to a 5% Native-PAGE . The radiolabeled sharp band seen at the top of the gel in silver staining was subjected to MALDI analysis . To measure the intensity of radiolabeling , the gel was dried and exposed to phosphorimager screen for 24 h . For food vacuole preparation , 4500×g pellet was processed as described [54] , [55] . After lysis in ice cold water pH 4 . 2 and DNaseI treatment in uptake buffer ( 25 mM HEPES , 25 mM NaHCO3 , 100 mM KCl , 10 mM NaCl , 2 mM MgSO4 , and 5 mM sodium phosphate , pH 7 . 4 ) , food vacuoles were purified by titurating the pellet in 42% percoll containing 0 . 25 M sucrose and 1 . 5 mM MgSO4 , and centrifuging at 16 , 000 g for 10 min at 4°C . The food vacuole pellet obtained was washed with 1 ml of uptake buffer to remove percoll . Extraction of free and protein-bound heme ( total heme ) was carried out as described earlier [10] . Briefly , the parasite pellet was extracted with 10 volumes of ethyl acetate∶glacial acetic acid ( 4∶1 ) for 30 min at 4°Cand centrifuged at 16 , 000×g for 10 min . The organic phase containing heme and porphyrins was separated and washed thrice with 1 . 5 N HCl of one-third total volume , and twice with water to remove porphyrins and any ALA present . The extracted organic phase containing heme was dried under a stream of nitrogen and dissolved in methanol , followed by thin-layer chromatography ( TLC ) on silica gel using the mobile phase 2 , 6-lutidine and water ( 5∶3 ) in ammonia atmosphere [56] . The intensity of radiolabeling was quantified by exposing the TLC sheets to phosphorimager screen for 8 h . To extract heme from hemozoin , food vacuole pellet was resuspended in 10 volumes of cold acetone containing 0 . 1 N HCl , vortexed for 30 min at 4°C and centrifuged at 16 , 000×g for 10 min . The supernatant obtained was dried , dissolved in methanol and analyzed by TLC as described for total heme . The complete extraction of heme from hemozoin can be easily visualized by the color change of the pellet from dark brown to pale and if necessary , the extraction was carried out twice . Parasite genomic DNA was isolated by SDS/proteinase K method [57] . Total RNA from the parasite was prepared using Trizol reagent ( Invitrogen ) according to the manufacturer's protocol . PCR , Western , Southern and Northern analyses were carried out using standard procedures . Polyclonal antibodies for ALAS and FC , cross-reacting with the proteins of both human and mouse origin , were procured from Santa Cruz Biotechnology , Inc . To detect P . berghei ALAS and FC , polyclonal antibodies raised against P . falciparum ALAS and FC , cross-reacting with P . berghei proteins were used . All these antibodies were used in 1∶1000 dilution for Western blotting . In vitro ookinete formation in P . berghei wild-type and knockout parasites was analyzed by injecting 2×107 parasites in phenylhydrazine-treated mice , followed by sulfadiazine treatment for two days to remove asexual stages . After removing the leukocytes using CF-11 cellulose columns , the gametocyte-infected blood was diluted with nine volumes of ookinete culture medium and incubated at 19°C for 19–21 h [58] . Hemoglobin was purified from mouse reticulocytes and human RBCs by resuspending the cells in hypotonic lysis buffer containing 20 mM Tris pH 7 . 5 and protease inhibitors . The lysate was incubated in ice for 30 min , followed by centrifugation at 20 , 000×g for 20 min and the supernatant obtained was loaded on to a UNOsphereQ column ( Bio-Rad ) . After washing the column with 10 mM NaCl , haemoglobin was eluted with lysis buffer containing 50 mM NaCl . To perform MALDI analysis , the protein complex was eluted from 5% Native-PAGE and resolved in 12% SDS-PAGE , followed by in-gel trypsin digestion . Proteins were identified by searching the National Center for Biotechnology Information ( NCBI ) nr protein database using MASCOT peptide mass fingerprint with cysteine carbamidomethylation and methionine oxidation as fixed and variable modifications , respectively , and taking into account of one missed cleavage and 0 . 5 Da peptide mass tolerance . MALDI analysis was carried out at Proteomics Facility , Molecular Biophysics Unit , Indian Institute of Science . Statistical analysis was performed using unpaired t-test of Excel software with two-tailed distribution and unequal sample variance . P values of <0 . 05 were considered as significant . Graphs were prepared using Sigmaplot 10 . 0 . Error bars given in the figures represent the standard deviations . The band intensities were quantified using Fujifilm Multi guage V3 . 0 software .
We demonstrated about two decades ago that the malaria parasite could make heme on its own , although it imports heme from red blood cell hemoglobin during the blood stages of infection . We investigated the role of parasite-synthesized heme in all stages of parasite growth by knocking out two genes in the heme-biosynthetic pathway of Plasmodium berghei that infects mice . We found that the parasite-synthesized heme complements the function of hemoglobin-heme during the blood stages . The parasite-synthesized heme appears to be a backup mechanism . The parasite incorporates both sources of heme into hemozoin , a detoxification product , and into mitochondrial cytochromes . The parasite-synthesized heme is , however , absolutely essential for parasite growth during the mosquito and liver stages . We restored the sporozoite formation and liver-stage development of the knockout parasites by providing the missing metabolite . Thus , the heme-biosynthetic pathway could be a target for antimalarial therapies in the mosquito and liver stages of infection . The knockout parasite could also be tested for its potential as a genetically attenuated sporozoite vaccine .
You are an expert at summarizing long articles. Proceed to summarize the following text: Extensive departures from balanced gene dose in aneuploids are highly deleterious . However , we know very little about the relationship between gene copy number and expression in aneuploid cells . We determined copy number and transcript abundance ( expression ) genome-wide in Drosophila S2 cells by DNA-Seq and RNA-Seq . We found that S2 cells are aneuploid for >43 Mb of the genome , primarily in the range of one to five copies , and show a male genotype ( ∼ two X chromosomes and four sets of autosomes , or 2X;4A ) . Both X chromosomes and autosomes showed expression dosage compensation . X chromosome expression was elevated in a fixed-fold manner regardless of actual gene dose . In engineering terms , the system “anticipates” the perturbation caused by X dose , rather than responding to an error caused by the perturbation . This feed-forward regulation resulted in precise dosage compensation only when X dose was half of the autosome dose . Insufficient compensation occurred at lower X chromosome dose and excessive expression occurred at higher doses . RNAi knockdown of the Male Specific Lethal complex abolished feed-forward regulation . Both autosome and X chromosome genes show Male Specific Lethal–independent compensation that fits a first order dose-response curve . Our data indicate that expression dosage compensation dampens the effect of altered DNA copy number genome-wide . For the X chromosome , compensation includes fixed and dose-dependent components . The somatic cells of multicellular animals are almost exclusively diploid , with haploidy restricted to post-meiotic germ cells . Having two copies of every gene has an obvious advantage . Mutations arise de novo within cells of an organism and within organisms in populations , such that deleterious mutation-free haploid genomes are extremely rare . The wild type alleles of genes tend to be dominant to the recessive loss-of-function alleles , providing a degree of redundancy allowing diploid organisms to survive even with a substantial genetic load of deleterious mutations in each haplotype . While the dose of most individual genes is of little consequence to the organism , larger scale genomic imbalance , or aneuploidy , is detrimental [1]–[4] . Chromosomal aneuploidy occurs when whole chromosomes are lost or duplicated and segmental aneuploidy results from deletions , duplications , and unbalanced translocations . In Drosophila , a systematic genome-wide segmental aneuploidy study [5] demonstrated that of all genes ( now known to be about 15 , 000 [6] ) , only about 50 are haploinsufficient and just one gene is triplo-lethal . However , these same experiments showed that large deletions and duplications result in reduced viability and fertility that depends on the extent of aneuploidy , and not on any particular region or gene [5] . This indicates that the detrimental effect of aneuploidy is a collective function of multiple small effects , not a function of particular genes . Interestingly , while aneuploidy results in inviability at the organism level , aneuploid cells can out-compete diploid cells for growth in vivo or in vitro . Human cancer cells are a good example of proliferating cells characterized by aneuploidy [7] . Most tumors are nearly diploid or tetraploid with extra or lost chromosomes . Even tumors with a normal number of chromosomes contain other rearrangements that result in segmental aneuploidy . It is likely that aneuploidy results in a systems or gene interaction defect . Given that a deleterious effect of aneuploidy is likely to occur at the level of genome balance , understanding the response to aneuploidy requires the exploration of general control mechanisms that operate at the network level . We have turned to widely used Drosophila S2 tissue culture cells as an aneuploid model [8] , [9] . These cells are generally tetraploid [9] and studies of gene expression and X chromosome dosage compensation indicate that they are male [10] . As a natural consequence of chromosomal sex determination in Drosophila , females have two X chromosomes and two pairs of autosomes ( 2X;2A ) and males have a single X chromosome ( 1X;2A ) [11] . Therefore , male cells can be thought of as naturally occurring chromosomal aneuploids . The response to altered gene dose probably occurs at multiple levels , but transcription is an early step in the flow of information from the genome and is a likely site for control . For example , X chromosome dosage compensation clearly occurs at the transcriptional level [12] and is exquisitely precise [13] . The Male Specific Lethal ( MSL ) complex regulates the balanced expression of X chromosomes in wild type 1X;2A male flies . MSL is composed of at least four major proteins ( Msl1 , Msl2 , Msl3 , and Mof ) and two non-coding RNAs ( RoX1 and RoX2 ) [11] . Mof is an acetyltransferase responsible for acetylating H4K16 [11] , [14] , [15] . Mof is highly enriched on the male X chromosome as a component of the MSL complex . However , Mof also associates with a more limited repertoire of autosomal genes independently of MSL [16] . H4K16ac is associated with increased transcription in many systems [17] . Therefore , it is widely believed that this acetylation results in increased expression of the X chromosome [11] , although an alternative hypothesis suggests that MSL sequesters Mof from the autosomes to drive down autosome expression [18] . Determining which of these mechanisms occurs is complicated by the very nature of sampling experiments when much of the transcriptome is altered . The number of X chromosome transcripts sampled from the transcriptome depends on the relative abundance of the X chromosome and autosome transcripts . The salient feature of both models is balanced X chromosome and autosome expression . While the term dosage compensation is used to describe X chromosome expression , dosage compensation is not restricted to X chromosomes in Drosophila . Autosomes also show significant , but much less precise , dosage compensation at the expression level [13] , [19]–[21] , suggesting that there is a general dose response genome-wide . Despite the clear role of MSL in X chromosome dosage compensation , the control system rules for MSL function and the contribution of global compensation mechanisms to the specific case of the X chromosome are poorly understood . There are three basic transcript control mechanisms that could modify the effect of gene dose: buffering , feedback , and feed-forward [22] . Here we define buffering as the passive absorption of gene dose perturbations by inherent system properties . For example , if transcription obeys mass-action kinetics and the gene/transcription complex is considered an enzyme [23] , then one would not expect a one-to-one relationship between mRNA and gene copy because of the small effect of a change in enzyme concentration at steady-state [24] . In addition to the enzymatic properties of transcription , more than a generation of molecular biologists has elegantly described extensive transcriptional regulation networks controlling key phenotypes [25] . These regulatory motifs are sensitive to changes in gene dose [26] . Feedback is an outstanding error-controlled regulator that detects deviations from the norm and implements corrective action . Feed-forward regulation differs in that it anticipates the possible effect of perturbations on the system rather than correcting the perturbation after the deviation occurs . This could operate if cells detect copy number and correct transcription levels before a quantitative error in transcript abundance is evident . In male embryos , the sex determination hierarchy detects X chromosome number and leads to association of the MSL complex with the X chromosome before zygotic transcription is activated [27] , as expected for a feed-forward regulator . However , MSL is selectively bound to transcribed genes [28] , which is also consistent with feedback regulation . By examining the response of X chromosome genes to dose in the presence and absence of MSL , we show that X chromosome dosage compensation results from a combination of MSL-dependent feed-forward regulation based on anticipated effects from unbalanced gene dose and a more general and dynamic response to perceived gene dose . The latter could be due to negative feedback , buffering , or both . To determine the extent of aneuploidy in S2 cells , we performed next generation sequencing ( DNA-Seq ) and comparative genome hybridization ( CGH ) . These data confirmed the predicted male genotype of S2 cells , as the average sequence depth of the X chromosome ( reads per kb per million reads , RPKM ) was 54% of the autosome RPKM ( Figures 1 and 2A ) . We also found that S2 cells exhibit numerous large regions of segmental aneuploidy ( Figure 1 , Figure S1 , Table S1 ) . Stepwise deviations from expected dose covered ∼42% ( ∼40 . 0 Mb ) of the autosomes and ∼17% ( ∼3 . 8 Mb ) of the X chromosome ( Figure S1 ) . The vast majority of the aneuploid segments showed an extra or lost copy . There was high congruence between DNA-Seq and CGH methods . For example , we determined that >93% of calls for copy numbers between one and five made by DNA-Seq analysis were confirmed by CGH , even when comparing different lots of cells grown under slightly different conditions ( Figure S2 , Table S2 ) . These data suggest that S2 cells are highly aneuploid but show a reasonably stable genotype . There was much more variability seen when copy number was greater than five ( 30% agreement between methods and cultures ) . This could be due to failure to call short segmental duplications or to repeat expansion/retraction in different cultures . Regardless of cause , we decided to focus our subsequent expression analyses on the high-confidence one to five copy genes ( Table S3 ) . We observed striking differences in DNA-Seq read density among chromosome arms due to segmental aneuploidy ( Figure 2A , p<10−15 , KS test ) . To determine if these DNA differences are also associated with similar changes at the transcript level , we profiled transcript expression by next generation sequencing ( RNA-Seq ) . We validated RNA-Seq data by microarray profiling and found outstanding agreement ( ρs = 0 . 87 , p = 0 ) . Expression analysis revealed striking dosage compensation . Even though copy number values significantly differed at the chromosome level ( Figure 2A ) , we found that expression from autosome arms and the X chromosome were similar inter se ( Figure 2B ) . In no case was the expression of a chromosome arm significantly different from all other arms ( p>10−2 , KS test ) , indicating that dosage compensation occurs genome-wide , not just on the X chromosome . To examine the precision of dosage compensation , we determined the relationship between expression and copy number . Compensation was not perfect , as expression increased with copy number ( Figure 2C , p<10−4 , KS test ) . This imperfect compensation resulted in a sublinear relationship between copy number and gene expression , such that per copy expression values decreased with increased copy number on the autosomes and especially on the X chromosome ( Figure 2D ) . This inverse relationship between copy number and expression per copy indicates that partial dosage compensation occurs genome-wide . X chromosome dosage compensation was of particular interest . In wild type males , X chromosome dose ( 1X ) is 50% of autosomal dose ( 2A ) . In S2 cells this relationship occurred at 2X;4A due to tetraploidy . The precision of X chromosome dosage compensation in S2 cells was revealed by the indistinguishable expression of two copy X chromosome genes and four copy autosome genes ( Figure 2C , p = 0 . 15 , KS test ) . Thus X chromosome dosage compensation shows similar efficacy in diploid 1X;2A flies and in aneuploid 2X;4A tissue culture cells . The aneuploid S2 cells also allowed us to examine the effect of X chromosome dosage compensation when the X chromosome dose was greater or less than 50% . Precise X chromosome dosage compensation did not occur at these other gene doses ( Figure 2C , p<10−9 , KS test ) . For example , when we compared expression from three copy genes on the X chromosome and autosomes , X chromosome gene expression per copy was higher despite identical copy number ( Figure 2D ) . Thus , we suggest that X chromosome dosage compensation is error generating when the underlying X chromosome gene dose is equivalent to the autosomal gene dose . Similarly , we found under-compensated X chromosome expression when there was a single copy of an X chromosome segment . These data indicate that the anticipated or predicted X chromosome copy number that implements the sex and dosage compensation pathway determines X chromosome expression . The actual X chromosome dose is not a factor . This error generation following perturbation is a property of feed-forward regulation [22] . To evaluate the effect of the MSL complex on appropriate and error generating X chromosome dosage compensation in S2 cells , we performed RNA interference ( RNAi ) experiments to knockdown expression of two genes encoding key MSL components , msl2 and mof . If MSL operates via feedback regulation , then knockdown should differentially alter expression depending on dose , whereas if MSL is a feed-forward regulator , the effect of MSL on expression should be X chromosome specific but dose independent . We selected double stranded RNAs ( dsRNA ) targeting msl2 and mof that resulted in greater than 90% knockdown at the mRNA ( not shown ) and protein levels ( Figure 3A ) . MSL is a chromatin-modifying machine . We therefore also determined if RNAi altered X chromatin . The X chromosome showed high levels of acetylation at expressed genes ( Figure 3B and 3C ) , and both msl2 and mof RNAi resulted in markedly reduced H4K16ac levels on the X chromosome as determined by chromatin immunoprecipitation on microarray ( ChIP-chip , Figure 3B , 3D , and 3E ) . RNAi against mof also resulted in decreased autosomal H4K16ac ( Figure 3B and 3E ) . All these data suggest that the RNAi treatments were effective . We then measured the effect of msl2 and mof RNAi on expression by RNA-Seq . As in the previous experiments , we validated expression by microarray expression profiling and found outstanding agreement ( rs = 0 . 87–0 . 89 , p = 0 , Figure S3 ) . We observed decreased expression of X chromosome genes following either RNAi treatment ( Figure 4 , p<10−2 , KS test ) , consistent with the role of MSL in promoting expression of X chromosome genes relative to autosomes . For example , in mof RNAi cells we observed a median expression of 26 . 4 RPKM for autosomal genes present at four copies and only 18 . 6 RPKM for X chromosome genes present at two copies ( p<10−15 , KS test ) . The msl2 or mof RNAi treatments broke the precise equilibration of 2X with 4A expression . We observed 1 . 35-fold greater X chromosome expression attributable to wild type Msl2 or Mof ( average RNAi/Mock expression ratio = 0 . 74 , p<10−15 , KS test ) , with little to no effect on autosomal expression ( Figure 5A and 5B ) . If MSL acts as a strict feed-forward regulator , then MSL would have the same fold effect on all populations of X chromosome genes at a given copy number , irrespective of the actual copy number . Indeed , we observed a similar fold effect on the expression of X chromosome genes with different copy numbers ( Figure 5C and 5D , 0 . 58<p<0 . 89 in msl2 RNAi , 0 . 21<p<0 . 91 in mof RNAi , KS test ) . These data clearly indicate that MSL acts on expression based on X chromosome gene nature , rather than monitoring actual copy number . Drosophila X chromosomes are dosage compensated over the full range of gene expression values . Given that MSL is bound selectively to expressed genes , we also asked if there is a relationship between expression levels and dosage compensation . We determined that the RNAi treatments had the same effect on X chromosome gene expression regardless of expression levels ( Figure 5E and 5F ) . Interestingly , these experiments also showed only a modest effect of mof on autosomal expression , suggesting that the proposed autosomal function of Mof [16] is subtle . The effect of Mof on autosomes was expression level dependent , as we observed a greater fold effect at low expression levels . However , the most overt effect of wild type Msl2 or Mof was a 1 . 35-fold increase in X chromosome expression at all expression values . These data indicate that MSL acts as a feed-forward multiplier causing a fixed-fold effect on X chromosome expression regardless of gene copy number and basal gene expression value . X chromosome dosage compensation is 2-fold , but we observed only a 1 . 35-fold effect of MSL . If MSL is the only contributor to X chromosome dosage compensation and if knockdown was complete , we would expect X chromosome and autosome genes with the same copy number to show the same expression levels following msl2 or mof RNAi treatment . However , following either msl2 or mof RNAi , three copy genes on the X chromosome were still 1 . 19-fold over-expressed relative to three copy genes on autosomes ( Figure 6A , p<0 . 01 , KS test ) . This difference between expected and observed expression could be due to residual MSL activity exclusively , or due to a combination of residual MSL activity and an MSL-independent component of X chromosome dosage compensation . The MSL-independent compensation could be the same as observed on the autosomes . Given that the fixed-fold properties of MSL also apply to residual activity , then the over-expression of X chromosome genes following RNAi treatment should also have a fixed fold effect if there is residual MSL activity . We observed significantly increased variance in the expression ratios between the X chromosome and autosomes following RNAi ( p<10−2 , F test , Figure 6B ) . This supports the idea that much of the unexplained X chromosome dosage compensation is not due to a fixed-fold effect on expression . It is possible that there are MSL-dose dependent effects on X chromosome expression due to variable affinity , although the fixed-fold effect of MSL knockdown on the population of genes makes this less likely . These data suggest that there is an MSL-independent component of X chromosome dosage compensation . To determine if the MSL-independent component is the same dosage compensation system that operates on autosomes , we characterized the sublinear expression response to gene dose for the X chromosome and autosomes with or without RNAi treatment . There were three distinct trend lines for the relationship between copy number and expression: one for the autosomes and one each for the X chromosome with and without RNAi treatment ( Figure 6A ) . There are an infinite number of possible sublinear curves . If the nature of the dose response on the X chromosome differed from the autosomes , or the presence or absence of MSL , then scaling should not result in a common fit . However , if the three dose response curves are the result of a common dosage compensation mechanism , then they should scale to yield a single curve that fits all three of the absolute dose-response curves . We set median expression fold change at 2X and 4A to 1 . 0 for both copy number and expression ( Figure 6C ) . We found that X chromosome and autosomes show remarkably similar fold changes in expression relative to fold changes in copy number . Additionally , the relationship between X chromosome expression and copy number is MSL independent following scaling . These data suggest that like the autosomes , the X chromosome is subject to dosage compensation based on actual gene dose . The gene dose to expression response fits a one parameter model y = x ( EC50 +1 ) / ( EC50 + x ) , where y is transcript abundance , x is DNA copy number expressed as a ratio relative to wild type , and EC50 is the copy number required for half maximal expression ( r2>0 . 99 ) . This indicates that gene expression is a saturating function of gene dose regardless of chromosome location or the presence of MSL . Our data indicate that the MSL complex and general compensation mechanisms independently contribute to male X chromosome dosage compensation . The MSL complex recognizes active X chromosome genes [28]–[31] . We have shown that MSL then acts as a simple unidirectional multiplier of expression regardless of the actual gene dose and gene expression level . In contrast , buffering and feed-back are dose sensitive and absorb the expression perturbations caused by unbalanced dose . We suggest that all these mechanisms are critical for proper X chromosome dosage compensation . Some rough accounting illustrates the composite nature of X chromosome dosage compensation . In the Drosophila genus , dosage compensation results in a 2 . 0- to 2 . 2-fold increase in X chromosome expression in males relative to autosomes [13] , [32] . Similarly , in S2 cells we observed a 2 . 08-fold increase in X chromosome expression . The fixed-fold effect of MSL resulted in at least a 1 . 35-fold increase in X-chromosome expression . Dose-responsive compensation also acted to increase X chromosome expression and was independent of MSL function . We can estimate the contribution of dose-responsive compensation from work performed on whole flies and on S2 cells . Autosomal dosage compensation increases per copy expression by 1 . 4- to 1 . 6-fold in diploid flies with a single copy of tens of genes [13] , [19] . In agreement with those reported values , we can project that a 2-fold change in scaled DNA dose in S2 cells results in about a 1 . 5-fold increase in scaled gene expression . Thus , at face value , the layered effect of dose-responsive compensation and feed-forward dosage compensation may explain all of the final increase in S2 cell X chromosome expression ( 1 . 50-fold×1 . 35-fold = 2 . 03-fold ) . While most work on dosage compensation focuses on the X chromosome [2] , [11] , other organisms also show dosage compensation on autosomes [33] . For example , mammalian trisomies show only about a 1 . 3-fold increase in gene expression as a result of a 1 . 5-fold change in gene dose [34] , [35] . Compensation is likely to be a universal property of biological systems that enables cells to avoid deleterious effects of genetic load and other perturbations . Drosophila S2 cells [9] ( a . k . a . SL2 ) were obtained from Drosophila RNAi Screening Center ( DRSC , Harvard Medical School , Boston , MA ) and were grown at 25°C in Schneider's Drosophila Medium ( Invitrogen , Carlsbad , CA ) supplemented with 10% Fetal Bovine serum ( SAFC Biosciences , Lenexa , KS ) and Penicillin-Streptomycin ( Invitrogen , Carlsbad , CA ) . These cells were used for all experiments , except CGH , where S2-DRSC cells were obtained from the Drosophila Genomics Resource Center ( #181 , Bloomington , IN ) . We extracted S2 cell genomic DNA using a genomic DNA kit ( Qiagen , Valencia , CA ) . Approximately 2 µg of purified genomic DNA was randomly fragmented to less than 1 , 000 bp by 30 min sonication at 4°C with cycles of 30 s pulses with 30 s intervals using the Bioruptor UCD 200 and a refrigerated circulation bath RTE-7 ( Diagenode , Sparta , NJ ) . Sonicated chromatin ( see ChIP protocol ) was purified by phenol/chloroform extraction . We extracted S2 cell total RNA with Trizol ( Invitrogen , Carlsbad , CA ) and isolated mRNA using Oligotex poly ( A ) ( Qiagen , Valencia , CA ) . The number of cells used for each extraction was counted using a haemocytometer . The quality of mRNA was examined by RNA 6000 Nano chip on a Bioanalyzer 2100 ( Agilent , Santa Clara , CA ) according to the manufacture's protocol . One hundred ng of the extracted mRNA was then fragmented in fragmentation buffer ( Ambion , Austin , TX ) at 70°C for exactly 5 min . The first strand cDNA was then synthesized by reverse transcriptase using the cleaved mRNA fragments as template and high concentration ( 3 µg ) random hexamer Primers ( Invitrogen , Carlsbad , CA ) . After the first strand was synthesized , second strand cDNA synthesis was performed using 50U DNA polymerase I and 2U RNaseH ( Invitrogen , Carlsbad , CA ) at 16°C for 2 . 5 h . Deep sequencing of both DNA and short cDNA fragments were performed [36] , [37] . Libraries were prepared according to instructions for genomic DNA sample preparation kit ( Illumina , San Diego , CA ) . The library concentration was measured on a Nanodrop spectrophotometer ( NanoDrop products , Wilmington , DE ) , and 4 pM of adaptor-ligated DNA was hybridized to the flow cell . DNA clusters were generated using the Illumina cluster station , followed by 36 cycles of sequencing on the Illumina Genome Analyzer , in accordance with the manufacturer's protocols . Two technical replicate libraries were constructed for each DNA-Seq sample . Two libraries were prepared from two biological replicates of each RNA material ( RNAi or mock treated ) . dsRNA for RNAi treatment [38] was produced by in vitro transcription of a PCR generated DNA template from Drosophila genomic DNA containing the T7 promoter sequence on both ends . Target sequences were scanned to exclude any complete 19 mer homology to other genes [39] . The dsRNAs were generated using the MEGAscript T7 kit ( Ambion , Austin , TX ) and purified using RNAeasy kit ( Qiagen , Valencia , CA ) . Two different primer sets were used for each target gene , and the one with better RNAi efficiency was used for downstream experiments . The selected primer sequences for generation of msl2 dsRNA template by PCR were as follows: forward , 5′-taatacgactcactatagggTTGCTCCGACTTCAAGACCT-3′ , and reverse , 5′-taatacgactcactatagggGCATCACGTAGGAGACAGCA-3′ and the selected primer sequences for generation of mof dsRNA template were as follows: forward , 5′-taatacgactcactatagggGACGGTCATCACAACAGGTG-3′ , and reverse , 5′-taatacgactcactatagggTGCGGTCGCTGTAGTCATAG-3′ . For RNAi treatment , S2 cells were resuspended in serum free media at 2×106 cells/ml . Twenty µg dsRNA was added to 1 ml of cell suspension and incubated for 45 min at room temperature . Cells with the same serum free media treatment but without added dsRNA were used as mock treated controls . After the incubation , 3 ml complete medium was added and the cells were cultured for another 4 d . Cells were collected and split into three aliquots for mRNA extraction , chromatin immunoprecipitation , and western analysis . For ChIP [40] , 5–10×106 S2 cells were fixed with 1% formaldehyde in tissue culture media for 10 min at room temperature . Glycine was added to a final concentration of 0 . 125 M to stop cross-linking . After 5 min of additional incubation and two washes with ice-cold PBS , cells were collected and resuspended in cell lysis buffer ( 5 mM PH 8 . 0 PIPES buffer , 85 mM KCl , 0 . 5% Nonidet P40 , and protease inhibitors cocktail from Roche , Basel , Switzerland ) for 10 min and then resuspended in nuclei lysis buffer ( 50 mM PH 8 . 1 Tris . HCl , 10 mM EDTA , 1% SDS and protease inhibitors ) for 20 min at 4°C . The nuclear extract was sheared to 200–1 , 000 bp by sonication on ice for 8 min ( pulsed 8 times for 30 s with 30 s intervals using a Misonix Sonicator 3000; Misonix , Inc . Farmingdale , NY ) . The chromatin solution was then clarified by centrifugation at 14 , 000 rpm for 10 min at 4°C . Five ul anti-H4AcK16 ( Millipore , Billerica , MA ) was incubated with the chromatin for 2 h and then was bound to protein A agarose beads at 4°C overnight . The beads were washed three times with 0 . 1% SDS , 1% Trition , 2 mM EDTA , 20 mM PH 8 . 0 Tris , and 150 mM NaCl; three times with 0 . 1% SDS , 1% Trition , 2 mM EDTA , 20 mM PH 8 . 0 Tris , and 500 mM NaCl; and twice with 10 mM PH 8 . 1 Tris , 1 mM EDTA , 0 . 25 M LiCl , 1% NP40 , and 1% sodium deoxycholate . The immunoprecipitated DNA was eluted from the beads in 0 . 1 M NaHCO3 and 1% SDS and incubated at 65°C overnight to reverse cross-linking . DNA was purified by phenol-chloroform extraction and ethanol precipitation . The precipitated DNA for Chromatin immunoprecipitation was amplified using a Ligation-mediated PCR ( LM-PCR ) protocol from FlyChip [41] . ChIP was performed on triplicate biological samples . Six hundred ng of amplified DNA ( ChIP enriched DNA or input DNA ) were labeled using 6ug Cy3- or Cy5-labeled random nonamers ( Trilink Biosciences , San Diego , CA ) with 50U Klenow ( New England Biolabs , Ipswich , MA ) and 2 mM dNTPs . The labeled DNA was purified and hybridized to FlyGEM microarrays [42] . Arrays were scanned on an Axon 4000B scanner ( Molecular Devices Corporation , Sunnyvale , CA ) and signal was extracted with GenePix v . 5 . 1 image acquisition software ( Molecular Devices Corporation ) . Two hundred ng aliquots of the same extracted mRNA used for RNA-Seq were labeled as described [42] and were hybridized to NimbleGen custom 12 plex microarrays at 42°C using a MAUI hybridization station ( BioMicro Systems , Salt Lake City , UT ) according to manufacturer instructions ( NimbleGen Systems , Madison , WI ) . Arrays were scanned on an Axon 4200AL scanner ( Molecular Devices Corporation , Sunnyvale , CA ) and data were captured using NimbleScan 2 . 1 ( NimbleGen Systems , Madison , WI ) . Cell lysates were prepared from cells 4 d after dsRNA or mock treatment by boiling for 5 min in NuPAGE LDS sample buffer ( Invitrogen , Carlsbad , CA ) . Samples were run by SDS-PAGE using a 4%–12% Bis-Tris gel ( Invitrogen , Carlsbad , CA ) and transferred to PVDF membrane . Blots were incubated with anti-MSL antibody ( 1∶500 ) , anti-MOF antibody ( 1∶3 , 000 , gifts of M . Kuroda ) , or anti-α tubulin antibody ( 1∶10 , 000 , Sigma , St . Louis , MO ) and then with HRP-secondary antibodies in PBS buffer with 0 . 1% Tween 20 . Protein signals were detected by Pierce SuperSignal West Dura extended Duration Substrate ( Thermo Fisher Scientific , Rockford , IL ) . Images were captured using a Fuji LAS-3000 Imager and quantified using the Image Gauge software ( Fuji Film , Tokyo , Japan ) . Both DNA-Seq and RNA-Seq sequence reads were compiled using a manufacturer-provided computational pipeline ( Version 0 . 3 ) including the Firecrest and Bustard applications [36] . Sequence reads were then aligned with the Drosophila melanogaster assembly ( BDGP Release 5 , dm3 ) [6] , [43] using Eland . Only uniquely mapped reads with less than two mismatches were used . For DNA-Seq data , we counted the number of reads in the non-overlapped 1 kb region along each chromosome using all sequenced reads from two technical DNA-Seq libraries and calculated the read density by the number of unique mapped reads per kb per million mapped reads ( RPKM ) [37] . The breakpoint positions of aneuploid segments were identified using the Bayesian analysis of change point ( bcp ) package from R [44] . Because some reads mapped to multiple positions in the genome and thus inappropriately lower the deduced copy number in regions with low sequence complexity , we removed all the 1 kb windows with RPKM lower than 2 ( RPKM value of one copy = 2 . 29 ) prior to change point analysis . Breakpoints with posterior possibility >0 . 95 were used . Copy number was assigned to segments based on the fold between average segments RPKM value between breakpoints ( 2 . 29±1 . 15 RPKM = 1 copy , 4 . 58±1 . 15 RPKM = 2 copy , etc . ) . Genes spanning two segments were not used in gene expression analysis . For RNA-Seq data , we counted the number of unique mapped reads within all unique exons of Drosophila Flybase [45] Release 5 . 12 annotation ( Oct . 2008 ) and calculated the total number of reads of all unique exons per kb of total length of unique exons per million mapped reads ( RPKM ) for each annotated gene . The RPKM calculation was done for individual RNA-Seq libraries separately , and then RPKM values were averaged for biological replicates ( r2 = 0 . 98 between replicates ) . Non-expressed genes are not useful for ratiometric analysis and these were therefore excluded . We used RPKM values for intergenic regions to determine expression thresholds . For intergenic regions , the RPKM values were calculated for total number of reads between adjacent gene model pairs . Only 5% of intergenic regions in S2 cells have a RPKM value greater than or equal to 4 . Therefore , we called genes with RPKM values no less than 4 in S2 cells as expressed with an estimated type I error rate of 5% . All microarray data ( except CGH ) and statistical tests were processed and analyzed in R/Bioconductor [46] . For the ChIP-chip experiments , we used quantile normalization based on the input channel . The distributions of raw and normalized intensities were checked to make sure that normalization was appropriate ( i . e . , that the skew was maintained ) . We used the average ChIP/input ratio from biological replicates ( r2 = 0 . 40–0 . 54 between replicates ) . The ChIP/input ratios in RNAi and mock treated cells were used for K-means clustering analysis with 3 nodes using Euclidean similarity metric and genes on X chromosome and autosomes were clustered separately using Cluster3 . 0 and then visualized using Tree-View [47] . For expression profiling , we normalized using loess within each 12-plex and quantile between 12-plexes . Average probeset log2 intensities were calculated in both channels for each gene . Correlations between array intensities and RPKM values were estimated by Spearman's rank correlation coefficient . The comparisons for the distributions of DNA densities or expression values among different chromosomes and different copy numbers were performed using two sample Kolmogorov-Smirnov tests ( KS tests ) . Normalization is inherently problematic when a large fraction of the genome changes expression , as in the RNAi experiments . Given that 20% of the genome is encoded on the X chromosome ( X ) and 80% is encoded on autosomes ( A ) , and that one samples transcripts from a total mRNA pool to generate an expression profile , and that X chromosome expression is reduced by half and autosome expression does not change , then autosomal transcripts must be over-sampled in the experiment . Conversely , if the autosome expression is doubled , then X chromosome transcripts must be under-sampled . While it is imprudent to formally state the precise contribution of X chromosome expression changes and autosomal expression changes due to MSL-mediated dosage compensation , we can determine which makes the larger contribution based on the RPKM , total mRNA , and cell count measurements . Using this information , we calculated the log-likelihood value for two hypotheses: Here hypothesis H0 states that the expression of autosomes ( A ) remains the same and the expression of the X chromosome ( X ) decreases by half after RNAi treatment . Hypothesis H1 states that the expression of autosomes ( A ) is increased by 2-fold after the RNAi treatment and the expression of X chromosome ( X ) remains the same . The expected sum of expression in the RNAi treated cells is 90% of wild type for H0 and 180% for H1 . E is the measured mRNA per cell . In the duplicate RNA-Seq experiments , we obtained mRNA yields of 0 . 16 pg and 0 . 17 pg/cell from mock treated , 0 . 15 pg and 0 . 19 pg/cell from Msl2 knockdown , and 0 . 14 pg and 0 . 20 pg/cell from Mof knockdown S2 cells . The log-likelihood of H0 – the log-likelihood of H1 = 26 . 4 suggests that X chromosome expression change contributes more than autosomal expression change to the observed measurements of expression in wide type cells relative to RNAi treated cells . DNA was isolated from Drosophila S2-DRSC cells obtained from the Drosophila Genomics Resource Center ( #181 , Bloomington , IN ) and from w1118 0–2 h embryos as described previously [48] . The isolated cell line and embryonic DNA were labeled with either Cy5 or Cy3 conjugated dUTP and subsequently hybridized to a custom Agilent genomic tiling array ( GEO; GPL7787 ) . Changes in copy number along each of the Drosophila chromosome arms were detected by a dynamic programming algorithm which divided each arm into the optimal number of copy number segments [49] . All Seq and array data sets are available at GEO under accession number GSE16344 . The CGH data set is available at modENCODE submission ID 596 .
While it is widely recognized that mutations in protein coding genes can have harmful consequences , one can also have too much or too little of a good thing . Except for the sex chromosomes , genes come in sets of two in diploid organisms . Extra or missing copies of genes or chromosomes result in an imbalance that can lead to cancers , miscarriages , and disease susceptibility . We have examined what happens to gene expression in Drosophila cells with the types of gross copy number changes that are typical of cancers . We have compared the response of autosomes and sex chromosomes and show that there is some compensation for copy number change in both cases . One response is universal and acts to correct copy number changes by changing transcript abundance . The other is specific to the X chromosome and acts to increase expression regardless of gene dose . Our data highlight how important gene expression balance is for cell function .
You are an expert at summarizing long articles. Proceed to summarize the following text: Innate immunity is the first line of defense against microbial insult . The transcription factor , IRF3 , is needed by mammalian cells to mount innate immune responses against many microbes , especially viruses . IRF3 remains inactive in the cytoplasm of uninfected cells; upon virus infection , it gets phosphorylated and then translocates to the nucleus , where it binds to the promoters of antiviral genes and induces their expression . Such genes include type I interferons ( IFNs ) as well as Interferon Stimulated Genes ( ISGs ) . IRF3-/- cells support enhanced replication of many viruses and therefore , the corresponding mice are highly susceptible to viral pathogenesis . Here , we provide evidence for an unexpected pro-microbial role of IRF3: the replication of the protozoan parasite , Toxoplasma gondii , was significantly impaired in IRF3-/- cells . In exploring whether the transcriptional activity of IRF3 was important for its pro-parasitic function , we found that ISGs induced by parasite-activated IRF3 were indeed essential , whereas type I interferons were not important . To delineate the signaling pathway that activates IRF3 in response to parasite infection , we used genetically modified human and mouse cells . The pro-parasitic signaling pathway , which we termed PISA ( Parasite-IRF3 Signaling Activation ) , activated IRF3 without any involvement of the Toll-like receptor or RIG-I-like receptor pathways , thereby ruling out a role of parasite-derived RNA species in activating PISA . Instead , PISA needed the presence of cGAS , STING , TBK1 and IRF3 , indicating the necessity of DNA-triggered signaling . To evaluate the physiological significance of our in vitro findings , IRF3-/- mice were challenged with parasite infection and their morbidity and mortality were measured . Unlike WT mice , the IRF3-/- mice did not support replication of the parasite and were resistant to pathogenesis caused by it . Our results revealed a new paradigm in which the antiviral host factor , IRF3 , plays a cell-intrinsic pro-parasitic role . Toxoplasma gondii , an obligate intracellular protozoan , is responsible for severe Toxoplasmosis [1] . Roughly one-third of the world's population is infected with T . gondii , which may lead to a mononucleosis-like syndrome with fever , lymph node enlargement , asthenia and headache . T . gondii is a major cause of blindness [1] , and infection in pregnant women , transmitted transplacentally , can cause congenital fetal toxoplasmosis , leading to miscarriage , microcephaly , hydrocephalus , and seizures . To date , there is no vaccine against T . gondii for human use and no permanent cure for chronic toxoplasmosis; moreover , therapies such as pyrimethamine and clindamycin have significant side effects , including bone marrow suppression , rashes , and male infertility [2 , 3] . T . gondii evades adaptive immunity by transforming into dormant cysts that cause an asymptomatic chronic infection [4] . For this reason , the innate immune response of the host against T . gondii has received considerable attention , focused almost exclusively on cells of the immune system , such as macrophages and dendritic cells ( DCs ) , and several key cytokines produced by these cells in response to T . gondii infection [5–10] . In contrast , little is known about the innate immune response that T . gondii elicits in non-immune cells , such as the epithelia , fibroblasts , the central nervous system ( CNS ) and ocular cells , which together represent important host organs for the parasite . In the current study , we investigated the role of the type I interferon ( IFN ) system , the most prominent antiviral innate immune response , in T . gondii infection of cells of immune and non-immune origins . Microbial infection of mammalian hosts elicits a variety of immune responses that are temporally regulated . An early response is the activation of the innate immune signaling pathways that lead to the transcriptional induction of many cellular genes , including those encoding cytokines; the cytokines are then secreted and act upon as yet uninfected cells to forearm them against oncoming microbial infection . The IFN system is a good example of such a circuitry [11] , whereby virus infection induces the synthesis of type I IFN that is secreted and activates immune cells to eliminate the infected cells . In addition , IFN can directly induce an antiviral state in a cell by inducing hundreds of genes , called IFN-stimulated genes ( ISG ) , which encode intracellular proteins , some with the ability to interfere with different stages of virus replication . Surprisingly , ISGs can also be induced by many other signaling pathways activated by microbial infection , without any involvement of IFN , indicating a much broader physiological role of these genes [12] . Much is known about how ISGs are induced by microbes . Microbial pathogen-associated molecular patterns ( PAMP ) are recognized by cellular pattern recognition receptors ( PRR ) , such as membrane-bound Toll-like receptors ( TLR ) , cytoplasmic RIG-I-like receptors ( RLRs ) , and various cytoplasmic DNA receptors [13 , 14] . One such receptor , STING , can be activated either by direct DNA-binding or by cyclic dinucleotides produced by the cyclic GMP-AMP synthase ( cGAS ) , which is also activated by cytoplasmic DNA [15–19] . The PRRs use adaptor proteins , such as MyD88 , TRIF or MAVS , to assemble different multi-protein signaling complexes , including specific protein kinases . One such protein kinase is TBK1 , used by TLR3 , TLR4 , RLRs and STING to directly phosphorylate the latent transcription factor , IRF3 , and activate it [20] . Activation causes nuclear translocation of IRF3 , where it induces transcription of ISGs by binding to a specific promoter sequence , called ISRE . Thus , any signaling pathway that can activate TBK1 and IRF3 has the ability to induce ISGs . Other genes , such as that of IFN-β itself , need in addition to IRF3 , other transcription factors for induction . Because the ISRE is recognized by all nine members of the IRF family , some signaling pathways use other IRFs to induce ISGs . For example , type I IFN-signaling uses a transcription complex containing IRF9 for this purpose . Thus , in an infected cell , ISGs are directly induced by IRF3 , but concomitant synthesis of type I IFN can reinforce the ISG induction . Here , we report that ISG induction by IRF3 is not only not detrimental to the parasite but it actually promotes efficient T . gondii replication , in cell cultures or in mice , although type I IFN itself has no role in it . We present evidence that this novel interaction between the parasite and IRF3 , which we have termed "parasite-IRF3 signaling activation" ( PISA ) , is achieved by parasite-mediated activation of TBK1 through the cGAS/STING pathway . To investigate whether the type I IFN system regulates the replication of unicellular parasites , we used the virulent T . gondii RH strain as a model , and measured parasite replication by immunoblot of parasitic SAG1 protein as well as quantitative PCR ( qPCR ) of the parasitic genomic DNA . Two types of mouse cells , mouse embryonic fibroblasts ( MEFs ) and bone marrow-derived DCs ( BMDCs ) , and two human cell lines , HT1080 ( fibrosarcoma ) and M17 ( neuroblastoma ) , were used , and in all cell types TgSAG1 expression increased over time after parasite infection . Using this assay , we observed that T . gondii RH replicated poorly in cells deficient in IRF3 ( Fig . 1A , 1B , 1C; S1 Fig . ) . Specifically , in MEFs ( Fig . 1A ) and BMDCs ( Fig . 1B ) from IRF3 -/- mice , or in human cells in which IRF3 was knocked down by shRNA ( KD ) ( Fig . 1C ) , T . gondii replicated poorly . Expression of recombinant IRF3 in the HT1080 KD cells improved their ability to support parasite replication ( Fig . 1C ) . To gain an understanding of the kinetics of IRF3 action , we used an engineered cell line , Clone 10 , expressing recombinant V5-tagged IRF3 under Doxycycline ( Dox ) control [21] . To perform this experiment , we first determined the inhibitory concentration of Dox against T . gondii . Both the Western blot and the quantification of parasitic DNA showed an IC50 of 10–12 μg/ml in HT1080 host cells under our growth conditions ( S2 Fig . ) , and essentially no inhibition at 1 μg/ml , the concentration of Dox we used to shut-down IRF3 expression in Clone 10 cells . When grown in the presence of Dox ( 1 μg/ml ) , these cells did not express IRF3 and did not support efficient T . gondii replication ( Fig . 1D , left ) . Removal of Dox resulted in the appearance of IRF3 protein around 18 h and promoted robust T . gondii growth that closely followed on the heels of IRF3 appearance ( Fig . 1D , right ) , consistent with parasitic growth stimulation by IRF3 . We further confirmed the stimulatory role IRF3 on T . gondii by two independent techniques: microscopy and flow cytometry , both of which clearly revealed stunted parasite replication in the IRF3-/- cells . In confocal microscopy , more parasites are seen the PVs in the wild type MEFs than in the IRF3-/- MEF at any time point of infection ( Fig . 2A ) . For flow cytometry ( Fig . 2B ) , we set the baseline to detect cells containing two or more parasites . With this gating , nearly all initially infected WT BMDCs scored as positive ( 30 . 91% of total , matching the m . o . i . of 0 . 3 ) , whereas the number was significantly lower ( 6 . 28% ) in the IRF3-/- BMDCs ( Fig . 2B ) . When gated for 1 parasite per cell , KO and WT cells showed the same percentage of parasite-positive cells ( 30–33% of the total population ) , which was equal to the m . o . i ( as shown for WT ) ( Fig . 2B ) . These results also indicated that IRF3 deficiency affected the intracellular replication of the parasite , but not the entry or the formation of PV . The experiments described thus far used T . gondii RH , a highly laboratory-passaged non-cyst-forming strain . To inquire if the need for IRF3 extends to other types of T . gondii , we tested the growth of three more strains by qRT-PCR , which showed that IRF3 is needed not only for RH , but also for another type I strain ( GT1 ) as well as type II ( ME49 ) and type III ( VEG ) strains , although the extent of IRF3-dependence varied ( Fig . 2C ) . Together , using four independent parasite growth analyses , e . g . immunoblot , qRT-PCR , flow cytometry and confocal microscopy , we demonstrated a specific role of IRF3 in augmenting T . gondii replication that is independent of host cell and parasite types . Although IRF3 is well known as a transcription factor , we have shown that it can trigger apoptosis by a transcription-independent mechanism [22] . To inquire which function of IRF3 is critical for supporting parasite growth , we capitalized on the fact that the transcriptional function but not the pro-apoptotic function of IRF3 absolutely requires the presence of HDAC6 , which deacetylates β-catenin , an obligatory co-activator of IRF3-driven transcription [23] . T . gondii in fact replicated very poorly in HDAC6-/- MEF cells ( Fig . 3A ) , reinforcing our conclusion that in order to support parasite replication IRF3 was acting as a transcription factor of its target genes . Indeed , in infected cells , there was strong induction of ISGs , as manifested by the presence of the ISG56 protein ( Fig . 3B , upper half ) . ISG56 induction did not require induced IFN as an intermediate because in IFNAR-/- cells , which cannot respond to type I IFN , infection caused similar induction of ISG56; moreover , T . gondii replicated well in these cells ( Fig . 3B , lower half ) . As stated above , IFN can also induce ISGs , but not its own gene . Hence , we treated IRF3 KO MEF cells , which supported T . gondii growth poorly , with exogenous IFN-β to induce the ISGs and then challenged them with the parasite . As shown , T . gondii could replicate efficiently in IFN-β-treated cells , even when the cells did not express any IRF3 ( Fig . 3C ) . These results strongly suggest that one or more ISG-encoded proteins , induced by activated IRF3 , facilitate parasite replication; however , type I IFN is not required for this action of IRF3 , although it can substitute for IRF3 by virtue of the fact that IFN and IRF3 can induce the expression of an overlapping set of genes , the ISGs . Activation of IRF3 , as a transcription factor , requires phosphorylation of at least two of its serine residues ( Ser396 , Ser398 in human IRF3 and Ser388 , Ser390 in mouse IRF3 ) [24] . The known signaling pathways activate IRF3 through its phosphorylation by the protein kinase , TBK1 , which in turn is activated by signal-dependent auto-phosphorylation [25] . The same scenario held true for IRF3 activation in T . gondii-infected cells . IRF3 was phosphorylated upon infection in all cell lines tested and the phosphorylation was sustained , indicating the continuous presence of active IRF3 in infected cells ( Fig . 4A , left ) . In all cases , endogenous TBK1 was also phosphorylated with similar kinetics ( Fig . 4A , right ) . The need for the two target Ser residues , 396 and 398 , was confirmed by using an IRF3 mutant in which they were mutated; in IRF3 KD cells , expression of wild type IRF3 promoted efficient replication of T . gondii but the mutant IRF3 was ineffective ( Fig . 4B ) . Finally , the essential role of TBK1 was confirmed by using TBK1 KO cells , in which IRF3 was not phosphorylated and the parasite replicated poorly; however , expression of recombinant TBK1 in these cells caused IRF3 phosphorylation as well as efficient parasite replication ( Fig . 4C ) . To identify the specific signaling pathway used by PISA , we used various knockout cell lines devoid of strategic signaling molecules ( Fig . 5 ) . Without the knowledge of the nature of the PAMP used by T . gondii to activate PISA , we tested the requirements of the major PRRs and their adaptor proteins ( Fig . 5A ) . RLRs were ruled out , because PISA was activated in RIG-I KO cells , as manifested by TgSAG1 synthesis and IRF3 phosphorylation ( Fig . 5A , panel 2 ) ; the same was true for all TLRs that use MyD88 as the obligatory adaptor protein , since loss of MyD88 also had no effect on PISA ( Fig . 5A , panel 3 ) . TLR3 and TLR4 can signal via TRIF instead of MyD88; however , neither of these TLRs were required for PISA ( Fig . 5A , panels 4 , 5 ) , thus indicating a potentially novel signaling branch in PISA-related cellular signaling . Since neither RLRs nor TLRs were needed for PISA , we tested the DNA-sensing STING pathway , and found that PISA is indeed defective in STING KO MEF cells and that parasite growth as well as phosphorylation of TBK1 and IRF3 in these cells could be enhanced by recombinant expression of STING ( Fig . 5B ) . STING can directly respond to cytoplasmic DNA; alternatively , the enzyme cGAS is activated by this PAMP and produces cyclic dinucleotides ( CDNs ) , which in turn activate STING . To distinguish between these possibilities , we resorted to 293T cells , which are known to express very little cGAS and STING [18] . As expected , these cells did not support T . gondii replication ( Fig . 5C ) . Ectopic expression of either cGAS or STING alone could not promote parasite growth; however , co-expression of both proteins fully triggered PISA , supporting efficient T . gondii replication as well as TBK1 and IRF3 phosphorylation . In contrast to wild type cGAS , an enzymatically inactive mutant of cGAS failed to trigger PISA . These results suggest that cGAS recognizes a PAMP , possibly parasitic DNA , and produces a CDN that activates STING and consequently TBK1 and IRF3 . Since the naturally low level of STING in 293T cells appeared to correlate with poor T . gondii growth , we inquired whether this may be true for some other commonly used cell lines . We first quantified the STING mRNA levels in the following cell lines , which were either immortalized or cancerous: A549 , H196 , H1048 ( all lung carcinoma cells ) , HeLa ( cervical cancer ) , and three kinds of 293 cells ( also known as HEK293 , human embryonic kidney cells ) , viz . 293T ( expressing the T-antigen ) and two 293 cell lines of unknown origin , obtained from different laboratories ( ours and Dr . George Stark's ) . Quantitative RT-PCR results ( Fig . 6A ) revealed that the STING levels in these cells varied widely; A549 and H196 contained the highest amount , the two 293 cell lines contained slightly lower levels , and H1048 , HeLa , HME , and 293T contained very small amounts . While the mechanism of the natural variation in STING expression is unknown , these results reveal the diversity that may exist within established cell lines , even those bearing the same name . When tested for PISA in terms of T . gondii growth ( Fig . 6B , 6C ) , there was a general correlation with STING expression . For example , A549 and H196 cells supported robust parasite growth , the two 293 cells supported moderate growth , and H1048 , HME and 293T supported poor parasite growth . As seen by others [26–28] , our HeLa cell line supported decent parasite growth ( Fig . 6B ) , even though it had a low level of STING , lending further support to the variability of cell lines and the contribution of multiple host determinants besides STING in parasite replication . Upon establishing the need of IRF3 for promoting parasite growth in cell culture , we wanted to determine whether the same is true in vivo as well . For this purpose , mice of different genetic backgrounds were infected with T . gondii by intraperitoneal injections , and their rates of survival were monitored . Infection by the parasite killed WT mice in a dose-dependent fashion; in contrast , IRF3-/- mice were quite resistant to death at every dose of infection tested ( Fig . 7A ) . For example , at the lowest dose of 10 parasites per mouse , all WT mice either died or suffered severe weight loss by 15 days post infection , whereas none of the IRF3-/- mice died even after 25 days post infection . The pathogenicity in the WT mice correlated with weight loss ( Fig . 7B ) , high parasite loads in the key organs tested , including spleen , liver and brain , whereas parasites were nearly undetectable in the same organs of IRF3-/- mice ( Fig . 7C ) . Since IL-12 has been shown to be a protective cytokine in the mouse model of Toxoplasma infection , we tested its level in IRF3-/- and WT dendritic cells ( DCs ) , following parasite infection in culture , as well as in the serum of the mice upon infection . Whereas WT DCs and wild type mice produced IL-12 in response to T . gondii infection , the levels were much higher when IRF3 was absent ( Fig . 7D ) . When we tested IL-12 induction in nonimmune cell types , no post-infection induction of IL-12 could be detected in the non-immune cells , such as A549 , MEF and M17 cells ( S3 Fig . ) . Thus , IRF3 is needed for parasitic replication in a cell-intrinsic manner in many cell-types , although it is possible that the higher systemic IL-12 levels in the IRF3 KO animal , likely produced by infected immune cells , plays an important or even dominant role in the lower parasitic lethality . The higher level of IL-12 could be due to the non-transcriptional IL-12-suppressive role of IRF3 , as shown in elegant recent studies [29 , 30] . Recent studies have also revealed that three members of the IFN-γ-inducible p47 GTPase family are induced upon T . gondii infection in mouse; they are IGTP , IRG-47 and LRG-47 [29] . Of these , IGTP and LRG-47 play a role in resistance to acute T . gondii infection [29 , 30] . However , all three were expressed in similar amounts in T . gondii-infected WT and IRF3 -/- mice , as measured by qRT-PCR ( S4 Fig . ) , suggesting that the parasite resistance of IRF3 -/- mice is unlikely due to overexpression of these IFN-γ-inducible GTPases . We have uncovered a new signaling pathway , PISA , which is activated in mammalian cells upon infection with T . gondii and follows the scheme: Parasitic PAMP → cGAS → STING → TBK1 → P-IRF3 → ISG ( s ) → Parasite replication ( Fig . 8 ) . All the cellular proteins of the PISA pathway have been previously identified as signaling components of the host’s innate immune defense responses that protect it from viral or bacterial infection . In contrast , PISA is pro-microbial , not anti-microbial , and hence , should not be viewed as the host’s defense response; rather , it is the first example of a parasite co-opting an innate antiviral pathway for its replication . It remains to be seen whether this new paradigm is true for other intracellular protozoa as well . It is interesting to note that type I IFN was not needed for efficient parasite growth indicating that intracellular proteins , induced by IRF3 in the infected cells , promote this process . A recent study [31] concluded that intracellular death of T . gondii results in the release of its nucleic acids , leading to the activation of RNA or DNA receptors , thereby activating IRF3 and inducing IFN-β . Pioneering studies by Beiting et al [32] recently showed that in macrophages , heat-killed T . gondii induce ISGs more efficiently than live parasites . The loss of this induction in TLR3 -/- macrophages suggested that a T . gondii RNA , perhaps released by phagocytosis of the dead parasites , is the PAMP . We do not know if a fraction of the parasites in our preparation was dead , but it appears that UV-killed and heat-killed parasites interact differently with the host . We cannot rule out a contribution of RNA in PISA , but the lack a role of TLR3 and the need for cGAS suggest that DNA is likely the major PAMP . Although the exact structural features of the DNA recognized by the cytoplasmic DNA sensors remain to be defined , unmethylated DNA of prokaryotic pathogens is a major PAMP . Interestingly , genomes of three Apicomplexa parasites , namely Plasmodium falciparum , T . gondii and Cryptosporidium parvum , contain very low or little methylation [33 , 34] . Purified genomic DNA of P . falciparum and P . berghei in fact activates the STING pathway [35] and type I IFN in an IRF3-dependent manner [36] . A new concept of PAMP , named 'viability-associated PAMP' or vita-PAMP , has been defined as an entity generated by viable pathogens and not by dead ones . Although the vita-PAMP of cytoplasmic bacteria was identified as mRNA generated from bacterial transcription [37] , any de novo synthesized PAMP could potentially be a vita-PAMP . This concept may also hold true for T . gondii since we have observed that UV-inactivated , non-replicative parasite is unable to activate PISA . Unfortunately , our knowledge of the molecular exchange between the parasite and its host is highly limited . Nonetheless , an increasing number of T . gondii macromolecules are found to access the host cytoplasm , such as kinases and pseudokinases , injected into the host cytoplasm promptly after parasitic invasion , whereby they regulate specific host signaling pathways [38–49] . The parasitic rhoptry kinase ( ROP16 ) and the rhoptry protein phosphatase 2C ( PP2C ) actually travel all the way to the host nucleus and likely regulate host gene expression [40 , 45] . As mentioned , it will be important to know if infecting parasites actually release DNA or RNA at any stage of growth and whether they reach the host cytoplasm to activate the cGAS-STING pathway . We provide evidence that PISA promotes parasite replication but do not know whether additional pathways are also stimulatory . It remains possible that genes induced by other transcription factors , such as NF-κB and AP-1 , that are known to be activated by STING signaling [50] , co-operate with IRF3-induced genes in promoting parasite replication . A distinct feature of PISA is that it requires the adaptor protein , STING , and it appears that STING and TBK1 form a signal-dependent complex that activates IRF3; indeed , STING has been reported to bind and activate TBK1 in vitro [51] . Recent studies have shown that under certain conditions T . gondii is highly vulnerable to autophagy [52] and , in the absence of a documented apoptotic cascade in T . gondii , autophagy has been suggested to be the primary mechanism of programmed cell death in T . gondii and potentially other related parasites . It is tempting to speculate that parasite-induced ISGs suppress an autophagic response that might otherwise be triggered by a host defense mechanism . PISA appears to be stimulatory to T . gondii replication in all human and mouse cell lines and primary cells that we tested . More importantly , without IRF3 and PISA , the parasite replicated poorly and was poorly pathogenic in mice . Pathogenesis in T . gondii-infected mice has been shown to be indirectly inhibited by IRF3 through its action on specific cytokine synthesis . Thus , in this context , IRF3 inhibits pathogenesis by T . gondii , in contrast to the cell-intrinsic pro-parasitic effect of IRF3 reported here . Not surprisingly , in IRF3-/- mice , the latter effect was overriding and little pathogenesis was observed because the parasite could not replicate in infected cells of these mice . We established the pivotal role of IRF3 and PISA in inducing ISGs to promote parasite replication but they can also be induced by many other pathways , activated by other stimulants that do not use IRF3 . For example , the IFN-activated Jak-STAT pathway uses IRF9 whereas TLR7 and TLR9 use IRF7 to induce the same genes . Thus , even in the absence of IRF3 , if IFN synthesis is induced by any viral or bacterial infection , circulating IFN will induce ISGs systemically and facilitate efficient T . gondii replication . Even for WT mouse , IFN produced by prior viral infection will make it a better host for subsequent T . gondii infection because the as-yet uninfected cells will already be loaded with ISG products . Therefore , in the natural context , viral or bacterial infection of an organism may set it up to be a better host for T . gondii . The ISGs have so far been studied in the context of their specific antiviral activities; this report reveals an unexpected new facet of their physiological significance in promoting microbial growth . The new knowledge can , in principle , be exploited to inhibit toxoplasmosis; inhibitors of any components of PISA , including IRF3 and ISGs , will be attractive candidates for this purpose . For good reasons , much of the related research so far has focused on formulating potent agonists of TLR and RLR signaling to boost the innate immune response and host defense [53] . However , inhibitors for these pathways are also known; for example , we discovered several inhibitors of ISG induction by TLR3 through the screening of a chemical library [54] . A similar approach can be taken to identify small molecule inhibitors of PISA , which can target multiple ISGs that may be involved in promoting various steps of parasitic cell division [55] . H196 , H1048 , HME , and HEK293 cell lines were kindly provided by Dr . George Stark , Lerner Research Institute ( LRI ) . All cells , with the exception of the primary macrophages and DCs , were grown in Dulbecco's minimum essential media ( DMEM ) supplemented with 10% FBS ( fetal bovine serum ) , 20 mM L-glutamine , 100 U/mL of penicillin and 100 μg/mL of streptomycin ( Life Technologies ) . Primary bone-marrow-derived DCs were obtained as follows . Bone marrow from the femurs and hind legs of mice of the appropriate genotype was collected in 1 ml of RPMI1640 medium ( without serum ) and single cell suspensions were made using 26G needle and syringe . Cells were washed twice with RPMI by centrifugation at 5 , 000 x g for 10 min in at 4°C , resuspended in RPMI containing 10% FBS , recombinant GM-CSF ( PeproTech; Cat# 315–03 ) ( 10 ng/ml ) and recombinant IL-4 ( eBioscience; Cat# 14-8041-62 ) ( 10 ng/ml ) , and then grown for 8 days . The mature DCs were resuspended in RPMI plus 10% FBS and transferred to appropriate multi-well plates for infection with T . gondii . The details of the Clone 10 cells were described before [21] . Briefly , these cells were generated from the parental HT1080/shIRF3 cells by expressing N-terminally V5-tagged human IRF3 , subcloned into pTRE2hyg vector ( Clontech ) and co-transfected with pTet-Off ( Clontech ) . The resultant cells were selected against G418 ( 400 μg/ml ) , puromycin ( 1 μg/ml ) , and hygromycin ( 100 μg/ml ) . Where mentioned , MEF cells were treated with murine IFN-β ( R&D Systems ) . Primary antibodies were obtained against the following: FLAG , HA and V5 epitope tags ( Millipore ) , SAG1 and actin ( Santa Cruz Biotechnology sc-52255 , sc-8432 , respectively ) , P-IRF3 ( Ser 396 ) , IRF3 , P-TBK1 ( Ser 172 ) , TBK1 ( Cell Signaling mAb #4947 , mAb #4302 , mAb #5483 , #3013 , respectively ) , and murine ISG56 ( LRI Hybridoma Core ) . cGAMP ( 3'3' cyclic GAMP ) was from Invivogen . Lipofectamine 2000 and Lipofectamine LTX with Plus reagent were from Invitrogen/Life Technologies . N-terminally V5-tagged human IRF3 and the deletion mutants were described before [22] . N-terminally HA-tagged STING and N-terminally Flag-tagged TBK1 were used for transfection experiments . Flag-tagged wild type ( WT ) cGAS and its enzymatically defective mutant were kindly provided by Robert Silverman ( LRI ) . Human cell lines were transfected using Lipofectamine 2000 , and mouse cells ( MEFs ) , with Lipofectamine LTX with Plus reagent . Unless otherwise stated , 0 . 8 μg plasmid was used for transfection per 5 x 105 cells ( each well of a 12-well plate ) using the manufacturers' instructions . After 8 h post transfection , cells were infected with T . gondii at an MOI of 2 . IRF3 knockdown HT1080 cells were described previously [22 , 56] . Briefly , these cells were generated by lentivirally expressing the shRNAs against the 5’ and 3’ UTRs of human IRF3 and selecting the resultant transduced cells under G418 . These cells were used to express IRF3 by transfecting a cDNA of human IRF3 without the UTR . Clone 10 cells were described before and were derived from these parental cells . All T . gondii strains were grown in hTERT-immortalized human foreskin cells as described before [57 , 58] and purified by differential centrifugation ( 3 , 000 x g , 10 min ) , followed by filtration through 3 μm Whatman filter . Parasites were resuspended in phosphate-buffered saline , counted in a hemocytometer under microscope , and used for infection of cells at an MOI of 2 , as described in Fig . legends . The infected cells were processed for immunoblot as described later . All experiments in this paper , except those indicated in Fig . 2C , used the T . gondii RH strain , which is a highly virulent , type I strain . RT-PCR primers for cGAS and STING ( Fig . 6A ) were kind gifts of Dr . George Stark . Real-time qRT-PCR was performed as described with the following primer pair , against the ITS-1 region conserved in all T . gondii strains [59]: AATATTGGAAGCCAGTGCAGG ( forward ) , CAATCTTTCACTCTCTCTCAA ( reverse ) . Results were normalized against GAPDH gene , amplified with the following primers: CTGGAAAACCCTGCCAAATA ( forward ) , TGCTCAGTTTAGCCCAGGAT ( reverse ) . The primers for IGTP and LRG-47 have been described [60 , 61]; those for IRG-47 ( GenBank M63630 . 1 ) , designed by us , were: GCCAAACCCATAGCTTTCAAG ( forward ) and GAAATCAAACGCACCCAGATC ( reverse ) . PCRs were setup in a final volume of 20 μl using 5 ng of template DNA , 200 ng of each primer and 1x of the iQ SYBR Green Supermix ( BioRad ) . Quantitative RT-PCR analysis was performed on a DNA Engine Opticon 2 Real-Time Cycler ( MJ Research ) . Primers directed against the T . gondii ITS-1 gene did not generate a product when the template genomic DNA was derived from organs of uninfected mice . Immunoblotting procedures were performed as described preciously [62 , 63] . Briefly , at the indicated times post infection ( p . i . ) , cells were washed twice with PBS . For IB , cells were lysed by the addition of 1 . 5 x Laemmli sample buffer containing protease inhibitor ( Roche , Product # 04693116001 ) and phosphatase inhibitor cocktails ( Cell Signaling Technology , Cat# 5870 ) . Cells were fully lysed by pipetting followed by sonication . Samples were heated at 95°C and equal amounts of proteins were analyzed on denaturing SDS-polyacrylamide gels . The proteins were transferred to PVDF Immobilon-P membrane ( Millipore , Cat# IPVH00010 ) and probed with specific primary antibody followed by secondary antibody conjugated to horseradish peroxidase . Bands were visualized by chemiluminescence-based detection system ( LI-COR Biosciences ) . Cells ( 2 . 0 × 105 /well ) were plated onto cover glasses in 6-well plates , grown overnight ( to ∼5 x 105 cells ) , then infected with T . gondii at an m . o . i . of 0 . 2 . At indicated times p . i . , cells were fixed in ice-cold methanol for 5 min and permeabilized with PBS containing 0 . 1% Triton X-100 . Fixed cells were blocked in PBS containing 1% BSA for 1 hr and labeled with anti-SAG1 as primary antibody ( 1:50 ) for 3 h , and Alexa Fluor 488-conjugated secondary antibody ( 1:200 ) . Cytosol was stained with anti-actin antibody and Alexa Fluor 647-conjugated secondary . Nuclei were stained with DAPI . Cells were visualized at a 60× magnification in a Nikon A1RSI confocal microscope . DCs were prepared from wild type and IRF3-/- mice as described earlier and infected with freshly egressed RFP-expressing T . gondii RH strain at an m . o . i . of 0 . 3 . At 18 h post-infection , the DCs were extensively washed to remove any free parasites , and 5 × 105 cells were collected in 300 μl cold PBS and stained with anti-CD11c-FITC antibody ( BD Pharmingen ) for 1 hr at 4° . DCs were then washed with PBS , pelleted at 300 x g for 6 min at 4° C , fixed with 1% formalin , and analyzed for RFP-positive cells by flow cytometry in a FacsCanto II cell analyzer ( BD Biosciences ) using FlowJo software ( Ashland , OR ) . For in vivo infection , we used the IRF3-/- mice , as described before [23] . The stated number of RH parasites ( e . g . , 10 , 50 , or 100 as indicated in Fig . 7 ) in phosphate-buffered saline was intraperitoneally injected in mice [64 , 65] . For survival analyses , mice were monitored for the indicated time post-infection . For various tissue analyses , animals were sacrificed on the indicated days; organs were homogenized and total DNA isolated by the DNeasy Blood and Tissue kit ( Qiagen , Valencia , CA ) . All animal procedures were approved by the Institutional Animal Care and Use Committee . Changes between treatment groups were analyzed by one-way ANOVA and by Student's t-test with Bonferroni correction . Numerical data were derived from three experiments , and results expressed as mean ± SEM or SD as stated ( presented as error bars in graphs ) . P < 0 . 05 was considered significant .
Interferon Regulatory Factor 3 ( IRF3 ) is an essential transcription factor for the expression of antiviral genes , including type I IFNs and ISGs . The coordinated action of the ISGs leads to the inhibition of one or multiple steps of viral life cycle . In contrast to the well-known antiviral function of IRF3 , we report here an unexpected pro-parasitic role of IRF3 in supporting the replication of the protozoan parasite , Toxoplasma gondii , in both cells and mice . The IRF3-deficient mice did not support T . gondii replication and , therefore , were protected from T . gondii-induced pathogenesis . The novel pro-Toxoplasma role of IRF3 was type I IFN-independent , but required its transcriptional function that induced the effector ISGs . Using cells deficient in known components of the IRF3 activation pathways , we have delineated the nature of the pro-parasitic signaling pathway , which we named ‘PISA’ . Our detailed genetic and biochemical analyses revealed that PISA is activated by a T . gondii-triggered cytoplasmic cGAS/STING/TBK1-dependent pathway that activates IRF3 for the induction of the pro-parasitic ISGs .
You are an expert at summarizing long articles. Proceed to summarize the following text: The differentiation of cells into distinct cell types , each of which is heritable for many generations , underlies many biological phenomena . White and opaque cells of the fungal pathogen Candida albicans are two such heritable cell types , each thought to be adapted to unique niches within their human host . To systematically investigate their differences , we performed strand-specific , massively-parallel sequencing of RNA from C . albicans white and opaque cells . With these data we first annotated the C . albicans transcriptome , finding hundreds of novel differentially-expressed transcripts . Using the new annotation , we compared differences in transcript abundance between the two cell types with the genomic regions bound by a master regulator of the white-opaque switch ( Wor1 ) . We found that the revised transcriptional landscape considerably alters our understanding of the circuit governing differentiation . In particular , we can now resolve the poor concordance between binding of a master regulator and the differential expression of adjacent genes , a discrepancy observed in several other studies of cell differentiation . More than one third of the Wor1-bound differentially-expressed transcripts were previously unannotated , which explains the formerly puzzling presence of Wor1 at these positions along the genome . Many of these newly identified Wor1-regulated genes are non-coding and transcribed antisense to coding transcripts . We also find that 5′ and 3′ UTRs of mRNAs in the circuit are unusually long and that 5′ UTRs often differ in length between cell-types , suggesting UTRs encode important regulatory information and that use of alternative promoters is widespread . Further analysis revealed that the revised Wor1 circuit bears several striking similarities to the Oct4 circuit that specifies the pluripotency of mammalian embryonic stem cells . Additional characteristics shared with the Oct4 circuit suggest a set of general hallmarks characteristic of heritable differentiation states in eukaryotes . How differentiated cell types are epigenetically maintained through repeated cell division is a topic of intensive study [1] , [2] , both for its role in basic developmental processes [3] and its relevance to the advancement of human stem cell therapeutics [4] . However , as a basic model of differentiation , stem cell systems have several drawbacks , such as the vast number of distinct cell types , the difficulty of isolating large homogeneous cell populations , and the challenge of genetic manipulation . A much simpler example of epigenetic inheritance of differentiated cell states is found in Candida albicans , the most prevalent human fungal pathogen . This eukaryote forms two distinctive types of cells , white and opaque , that differ strikingly in their appearance [5] ( Figure 1A and 1B ) , competency to mate [6] , and the human tissues to which they are likely best suited [7]–[11] . Each cell type is heritably maintained through many cell divisions , with switching back and forth between the two cell types occurring stochastically , only once every 104 generations . The low rate of switching makes it easy to obtain large populations of homogeneous cells of each type . Furthermore , it is relatively straightforward to manipulate the genes of C . albicans , which has allowed dissection of both the regulation underlying the switch and the functions of downstream genes that are ultimately responsible for conferring the specific attributes of each cell type [12]–[16] ( for reviews , see [17] , [18] ) . A master regulator of the white-opaque switch , White Opaque Regulator 1 ( Wor1 ) , forms interlocking feedback loops with two other transcription regulators ( Czf1 and Wor2 ) . The three regulators are up-regulated in opaque cells compared to white cells and together are responsible for the establishment and maintenance of the opaque cell type [13] . The white state is maintained by the transcription regulator Efg1 , which is down-regulated in opaque cells [13] , [19] . The expression of more than 400 genes was previously found to differ between the two cell types [20] , [21] , but subsequent genome-wide chromatin immunoprecipitation ( ChIP-Chip ) experiments indicated that Wor1 directly bound only 58 of these genes [13] . Much of this discordance may be due to indirect regulation; indeed , Wor1 itself controls a large number of transcriptional regulators that may direct the differential expression of additional genes . However , it was much more difficult to explain the observation that only 30% of all Wor1-bound regions flank at least one differentially expressed transcript . Are the other Wor1 binding sites simply non-functional ? Do they act only on more distal transcripts and/or only in response to certain environmental cues ? Does Wor1 also play a non-regulatory role , helping to maintain chromosome structure via these binding sites ? Although we investigate this issue specifically in C . albicans , we note that discordance between binding ( determined by ChIP ) and regulation ( based on RNA analysis ) has frequently been observed in the circuits of a broad range of organisms [22]–[26] . To better resolve the relationship between the binding of a master regulator of differentiation and differential expression of its direct targets between cell types , we performed massively-parallel strand-specific sequencing of RNA from white and opaque cells . Applying several novel algorithms to the resulting dataset and merging these results with the existing ORF-based gene annotation , we first annotated the C . albicans transcriptome . This revealed that thousands of transcripts overlap another transcript on the opposite strand , demonstrating widespread presence of anti-sense transcription in this yeast , as in the model yeast Saccharomyces cerevisiae [27] , [28] . With the new annotation we found that the abundance of 1 , 306 transcripts differed between white and opaque cell types , a 3-fold increase over the number identified previously by microarray . We next revisited the poor correspondence between Wor1 binding and differential expression and found a remarkable improvement in concordance . Thus , a large fraction of the Wor1 bound regions previously lacking proximity to a differentially expressed gene , and therefore also lacking obvious function , can now be assigned the function of regulating previously invisible or inaccurately-measured transcripts . Our analysis of the Wor1 circuit revealed several unusual properties . For example , the targets of Wor1 have abnormally long upstream intergenic regions and un-translated regions ( UTRs ) . We show here that many of these long UTRs are cell-type-specific ( that is , the transcript length is differentially regulated ) and thus may function to bring additional layers of regulation to the differentiation circuit . A meta-analysis of the Oct4 circuit [29]–[31] , which governs the pluripotency and differentiation of mouse embryonic stem cells , reveals many of these same “unusual” properties . These surprising similarities across vast evolutionary distances , combined with many other shared features , suggest that several hallmarks of cell differentiation circuits exist broadly across eukaryotes . To characterize the transcriptomes of white and opaque cells , we sequenced the poly ( A ) fraction of RNA extracted from replicate white and opaque cell cultures ( Materials and Methods and Figure 1B ) , expecting to find messenger RNAs , polyadenylated non-coding RNAs , and abundant non-polyadenylated transcripts that persist through the purification steps . Importantly , the sequencing libraries were prepared using an approach that preserves the genomic strand from which the sequenced RNA fragments were originally transcribed ( see Materials and Methods and Figure S1 ) [32] . Our sequencing runs yielded 29–136 million 50-base sequence reads per sample , which were subsequently aligned to a filter database ( containing , e . g . , rDNA sequences ) and then to the Candida albicans genome ( build Ca21 ) and a database of previously annotated splice junctions ( Materials and Methods and Figure S2 ) . An overview of the results is depicted in Figure 1C . The majority of reads from each sample ( 60–68% ) was successfully aligned , allowing detection of 93–95% of previously annotated exons with mean 50–200x sequence coverage ( i . e . , the number of reads aligned across a genomic position ) . 37–47% of positions were covered by an alignment in the strand-specific genome , and 423–904 deletions , which represent both splice junctions and deletion polymorphisms relative to the haploid reference genome , were detected ( Mitrovich et al . [33] , in preparation ) . On the whole , we have obtained more than sufficient sequence depth from these samples to build the first transcript annotation for C . albicans . Our RNA-Seq dataset allows us the first opportunity to define a true transcript annotation for C . albicans , which until now has had a gene annotation based primarily on computationally-predicted open reading frame ( ORF ) sequence boundaries and generally not informed by experimental data . We first developed a general computational approach ( Figure 2A ) that can define a new transcript annotation by combining an existing annotation ( in this case the ORF-based annotation ) with evidence found in RNA sequence data for un-translated regions ( UTRs ) and entirely novel transcripts . This effort included the development of new methods for the de novo identification of splice junctions and transcriptionally active regions ( TARs ) , which are based on gapped read alignments and clusters of sequence coverage , respectively ( Materials and Methods , Figure S3 , and Mitrovich et al . [33] , in preparation ) . We applied these methods to a single dataset produced by combining the reads from all four RNA sequence libraries , reasoning that ( 1 ) combining the datasets at this stage would be more powerful and straightforward than combining four separate annotations further downstream , and ( 2 ) the different datasets were sufficiently similar to one another . This is supported by the high reproducibility of biological replicates ( r = 0 . 95−0 . 99; Figure S5 ) and the observation that most exons , when expressed in both cell types , appear to extend to roughly the same boundaries . Rather than providing a completely de novo gene annotation ( as for S . cerevisiae in Yassour et al . [34] , for example ) , we sought to leverage the existing ORF-based annotation to provide an updated annotation in which existing transcripts , if expressed , were augmented with 5′ and 3′ UTRs , and new , isolated clusters of expression ( i . e . , those not overlapping an annotated exon on the same strand ) were added to the annotation as novel TARs ( nTARs ) . Thus , we devised a method to merge the splice junction and TAR-finding output with the existing ORF-based annotation ( Materials and Methods and Figure S4 ) and applied it to our datasets , resulting in the new C . albicans transcript annotation ( Tables S1 , S2 , S5; summarized in Figure 2B ) . The new transcript annotation contains 23% more transcripts ( N = 7 , 823 ) covering 13% more of the genome ( 76 . 1% versus 63 . 6% ) than the old annotation . We estimate that roughly 1 , 048 of these transcripts are non-coding because they do not contain a canonical ORF that is at least 120 nucleotides long ( i . e . , encoding a peptide at least 40 amino acids long ) , which increases the number of non-coding RNAs ( ncRNAs ) annotated in C . albicans by nearly 500% . However , there are also a large number of new coding transcripts ( i . e . , transcripts that contain putative ORFs encoding peptides 40 or more amino acids long ) , leading to an estimated 9% increase in the number of coding transcripts . Many of these ORFs may have been missed in previous annotations due to their short length ( 91% are shorter than 100 amino acids ) and , in some cases , due to lack of conservation in other species . It is likely that some of the ORFs defined here by our arbitrary length cutoff are not translated into protein . On the whole though , the number of putative ORFs at least 40 amino acids long found in novel transcripts ( N = 561 ) is significantly higher than expected by chance ( median N = 453; P-value <0 . 0001 by simulation; Materials and Methods ) , suggesting that many are translated into protein . As detailed in the next section , at least 18 of these short , novel ORFs are likely to serve an important function in opaque cells . In the new transcript annotation 5′ and 3′ UTRs of median length 99 and 136 bases were defined for 5 , 465 and 5 , 768 transcripts , respectively . These estimates are longer than estimates of 5′ and 3′ UTR length based on tiling arrays ( 68 and 91 in David et al . [35] ) , but closely resemble those based on RNA-Seq data ( 111 and 142 in Yassour et al . [34] ) for the related model yeast , Saccharomyces cerevisiae . Finally , 50% of transcripts in the new annotation overlapped transcripts from the opposite strand by at least 1 bp and 31% did so across more than 10% of their length , indicating that , as in other eukaryotes [27] , [28] , [36] , there is widespread antisense transcription in C . albicans . This observation underscores the importance of sequencing RNA in a strand-specific manner . Overall , the new transcript annotation described here represents a dramatic revision from previous annotations that microarrays were designed to assess . Using this new annotation we revisited the differences in gene expression between white and opaque cells . We determined which of the 7 , 823 newly defined transcripts were differentially expressed between white and opaque cell types by employing a likelihood ratio test [37] . We required a 2-fold or greater change in expression and false discovery rate ( FDR ) of 10−4 or less , which resulted in a set of 1 , 306 differentially-expressed transcripts ( Table S3 ) . As expected , we find strong ( 50-fold ) up-regulated expression of WOR1 , the gene that encodes a master regulator of white-opaque switching ( Figure 3A ) . As predicted by a previous study [14] , WOR1 has an unusually long 5′ UTR ( 1 , 978 bp , compared to the genome-wide median length of 99 bp ) . Unexpectedly , the lower WOR1 expression in white cells is associated with increased expression on the strand opposite this long UTR , suggesting an alternative internal antisense promoter is active and may be repressing WOR1 expression in white cells . To confirm the quality of these data we compared them directly to data generated using microarrays that are commonly used to study gene expression in C . albicans . We hybridized the same samples used for RNA sequencing ( Materials and Methods ) and examined the fold-change measurements produced by each technology for all previously annotated transcripts ( Figure 3B ) . We found a strong overall correlation ( r = 0 . 79 ) , which , as noted in other comparisons of RNA-Seq and microarray data , is stronger for high abundance transcripts ( r = 0 . 89 ) than it is for low abundance transcripts ( r = 0 . 71 ) , which are generally more accurately measured by RNA-Seq [32] , [37] , [38] . The 1 , 306 differentially expressed transcripts found here represent a 3-fold increase in the number observed by microarray [21] , which is partly attributable to the fact that 37% of these transcripts are novel ( N = 488 ) and thus were not probed on previous microarrays . Novel transcripts are unexpectedly frequent amongst the set of white-opaque differentially-expressed transcripts ( N = 488 versus 218 expected; χ2 P-value = 10−89 ) , a provocative observation we can not yet entirely explain , but which suggests an important role for non-coding transcripts and short proteins in the white-opaque circuit . In any case , this observation emphasizes the importance of “hypothesis-free” approaches to measuring gene expression . The remaining differentially-expressed transcripts , not recognized as such by microarray ( N = 376 ) , may be explained by the documented , improved sensitivity and dynamic range of RNA-Seq [38] , [39]; indeed , these transcripts not discovered by microarray have 2-fold lower average abundance than those that were , as estimated by RPKM ( reads per kb of transcript per million uniquely aligned reads ) . We were especially interested in the 488 novel differentially expressed transcripts , which fall into three major classes: ( 1 ) antisense transcripts , ( 2 ) isolated transcripts that encode proteins , and ( 3 ) isolated non-coding transcripts . We discuss these three classes in turn . We found 213 novel transcripts that overlap another transcript on the opposite strand across at least one third of their length . NTAR_364 is a particularly informative example of a differentially expressed novel transcript that overlaps another transcript on the opposite strand ( Figure 3C ) . The gene opposite NTAR_364 is STE4 , which encodes the β subunit of the heterotrimeric G protein complex required for mating [40] , [41] . Mating is a process specific to opaque cells [6] , and accordingly , NTAR_364's 14-fold down-regulation is inverse to STE4's 8-fold up-regulation in opaque cells . The anti-correlated expression of these two overlapping transcripts strongly suggests a mechanism in which NTAR_364's expression acts to repress expression of STE4 . There is ample precedent for this type of regulation in eukaryotes and bacteria [42]–[45] . To determine the prevalence of such mechanisms in C . albicans , we examined the expression profiles of all 759 such sense-antisense transcript pairs , filtering down to the subset of 44 pairs in which both transcripts are significantly changed and at least one transcript is coding ( Figure 3D ) . Our expectation was that we would observe strong anti-correlated differential expression across all such pairs if these mechanisms are prevalent and a lack of correlation if they are not . Instead , we found a modest and significant anti-correlation ( r = −0 . 25; P-value = 0 . 05; Figure 3D ) . Sense–antisense pairs in which one member is differentially-expressed are 2-fold more likely , than expected by chance , to have the second member differentially-expressed in the opposite direction ( 17% versus 8%; χ2 P-value = 10−4 ) . These results suggest that some , but not all , anti-sense transcripts act to repress the steady-state abundance of their sense counterpart . Despite the lack of perfect anti-correlation , there are several transcript pairs that , like the STE4-NTAR_364 pair mentioned , are considerably differentially-expressed in opposite directions ( Figure 4 ) , which strongly suggests a regulatory function for the novel antisense transcripts involved . The second major class of novel , differentially-expressed transcripts contains those that are isolated in the genome and code for protein . In total , we identified 224 novel differentially expressed transcripts that do not overlap a transcript on the opposite strand . Sixty-nine of these transcripts encode a putative protein at least 40 amino acids long . Amongst these is a group that clusters into three genomic locations and encodes a large family of novel , short ORFs ( Figure 5A , Figure S6A and S6B ) . Eighteen of the 24 ORFs in this family are encoded by transcripts that are opaque-specific , including NTAR_1179 . 2 , which with 287-fold higher abundance in opaque cells is the third most differentially-expressed transcript genome-wide . Using a combination of BLAST and PSI-BLAST against fungal genomes and eukaryotic protein sequence databases , we identified 46 members of this family ( see sequence alignments in Figure 5B and Figure S6C ) , 24 from C . albicans and 22 from its closest known relative , Candida dubliniensis . Homologs could not be identified in any other species , further underscoring the potential importance of these genes to opaque-cell differentiation , since these two yeast species are the only two known to switch between distinct white and opaque forms [46] . The neighbor-joining phylogeny inferred for these ORFs ( Figure 5C and Figure S6D ) indicates that most were present and similarly clustered in the common ancestor of C . albicans and C . dubliniensis . Computational predictions of secondary structure [47] indicated there are likely three β sheets followed by two α helices in these proteins ( Figure 5B ) and the structure prediction server I-TASSER [48] found a putative bacterial hemolysin ( PDB ID: 3HP7 ) to be the closest structural analog . Finally , 155 of the isolated , differentially-expressed transcripts do not appear to code for protein . At this time it is difficult to assess their functions in a purely computational manner; thus , their roles in the white-opaque switch await experimental characterization . In all three classes of novel transcripts we observe examples in which the master regulator Wor1 is bound adjacent to or overlapping the differentially expressed transcripts ( Figure 3C and Figure 5A ) , suggesting that these novel antisense and isolated transcripts are directly regulated by Wor1 binding . Thus , they may form a key , but heretofore unknown , part of the circuit . To assess the concordance between Wor1 binding and differential expression of nearby transcripts more globally we compared the previous ORF-based and our new RNA-Seq-based gene annotations to regions identified as Wor1-bound in chromatin immunoprecipitation-on-tiling microarray ( ChIP-Chip ) experiments [13] . We first associated Wor1-bound regions with adjacent genes using both the new and the old annotations ( Figure S7 ) , and then evaluated both the frequency with which Wor1 binding flanked at least one differentially expressed gene and the frequency with which Wor1-bound genes were differentially expressed ( Figure 6 ) . We also compared measurements of differential expression from three different platforms: ( a ) hybridization to spotted PCR-product microarrays ( reported previously by Tsong et al . [21] ) , ( b ) hybridization to custom-designed Agilent 8x15k microarrays ( reported here ) , and ( c ) strand-specific RNA-Seq ( also reported here ) . The pairing of the new transcript annotation with the RNA-Seq measurements of differential expression ( Figure 6 , first row ) clearly yields the strongest concordance between Wor1 binding and differential expression: 65% of Wor1-bound regions are associated with at least one differentially expressed transcript . This represents a greater than 2-fold improvement in concordance over a previously published association [13] , in which only 30% of bound regions were observed to flank at least one differentially expressed transcript ( Figure 6 , last row ) . In this previous association , differential expression of transcripts was measured by spotted PCR-product arrays designed to assay only transcripts in the old annotation . The concordance between binding and differential expression improves incrementally with the use of better microarray platforms ( 38–40%; Figure 6 , rows 5–6 ) and with RNA-Seq-based expression measurements computed using the old transcript annotation ( 48–51%; Figure 6 , rows 3–4 ) . However , by far the best concordance is found when RNA-Seq-based expression measurements are computed using the new transcript annotation . Thus , the dramatically improved association of master regulator binding and cell type-specific expression observed here is attributable to both the novel transcripts and the improved expression measurements provided by RNA-Seq . The fact that the WOR1 gene has a 2 kb long 5′ UTR and about 6 kb of Wor1-bound intergenic DNA upstream of it ( Figure 3A ) suggests that this master regulator of white-opaque switching is under complex regulation . We next examined whether other transcripts in the circuit have similar properties . It was previously noted that Wor1-bound intergenic regions are , on average , 5-fold longer than typical intergenic regions ( median 3 , 390 bp for Wor1-bound genes versus 623 bp genome-wide ) [13] . However , given the substantial changes we have made to the gene annotation , it was unclear whether this length bias would remain; in particular , it seemed plausible that some of the unusually long “intergenic” regions may actually contain , and thus be due to , previously unannotated long UTRs . We find that while genome-wide intergenic length is , on average , more than 2-fold shorter in the new annotation ( new median length = 262 bp ) , the intergenic regions bound by Wor1 are still , on average , 5-fold longer than expected by chance ( new median length = 1346 bp; Mann-Whitney P-value = 10−80; Figure 7A ) . Unexpectedly , we also found that 5′ UTRs of Wor1-bound genes are 58% longer than expected ( median 157 bp in the circuit versus 99 bp genome-wide; Mann-Whitney P-value = 10−20; Figure 7B ) and 3′ UTRs in the circuit are 22% longer than expected ( median 166 bp in the circuit versus 136 bp genome-wide; Mann-Whitney P-value = 10−6; Figure 7C ) . The unusually long UTRs found in the Wor1 circuit and the apparent change in UTR length at WOR1 ( Figure 3A ) motivated us to look more generally into changes in promoter usage and transcriptional termination between cell types , as reflected in changes in 5′ and 3′ UTR length , respectively . We devised a simple method to isolate putative cases of UTR length change , reasoning that a change in UTR length for a given transcript could be detected as a change in the apparent expression of the UTR that is significantly less than or greater than what was measured for the transcript's coding region . We required a minimum 2-fold difference in fold-change between UTR and coding region and a χ2 P-value less than 10−5 ( Materials and Methods ) . Using these criteria , we identified 145 transcripts with at least one UTR apparently changing length between white and opaque cells ( Table S4 ) . Visual inspection revealed that not all these cases are straightforward to interpret; however , many are , and these provide several examples for further study ( Figure 7D–7F ) . Most of the cases identified here are changes in 5′ UTRs ( N = 111; 77% ) , which likely reflects an emphasis on the usage of alternative promoters as a means of differentiating the two cell types . One of the transcripts , EFG1 , is a regulator of white-opaque switching and was previously shown to exhibit different 5′ UTR lengths in white and opaque cells [49] . EFG1 and 26 other transcripts with significant 5′ UTR changes are also associated with Wor1 binding nearby their genomic loci ( observed frequency = 24%; expected = 10%; χ2 P-value = 10−8 ) . For several of these transcripts , such as ORF19 . 2049 ( Figure 7D ) and EFG1 ( Figure 7E ) , the UTR is shorter in opaque cells and Wor1 is bound in opaque cells between the apparent white- and opaque-preferred transcription start sites , suggesting a direct regulatory mechanism . Other examples , such as PPS1 ( not shown ) and ORF19 . 7060 ( Figure 7F ) , are probably not directly related to Wor1 binding , but may instead involve mechanisms related to the transcription of antisense genes . Comparing Wor1 binding to gene expression revealed an additional feature of Wor1-controlled transcripts: direct binding of Wor1 within a transcribed region ( rather than upstream of it ) is associated with strong down-regulation of the bound transcript in opaque cells . The non-coding transcript NTAR_913 provides a clear example of this phenomenon ( Figure 7G ) . Genome-wide , we found 89 cases in which a transcript overlaps a Wor1-bound region by more than 50% , and the expression of such transcripts is frequently white-specific ( Figure 7H ) . This observation suggests the prominence of an underappreciated mode of gene regulation in which a transcription regulator may repress transcription via direct binding to the transcribed region . Given the unusual characteristics of the WOR1 locus and Wor1's target genes , we next examined whether other examples of heritable cell differentiation circuits exhibited similar features . One of the most studied transcription circuits is that of Oct4 , which governs the differentiation and pluripotency of mammalian embryonic stem ( ES ) cells [1] , [50] . Oct4 is a master regulator of mammalian cell types in the same sense that Wor1 is a master regulator of Candida cell types: Oct4 expression is required to maintain the pluripotent ES cell type [51] , and Oct4's over-expression in other cell types , along with additional factors , returns them to the ES cell state [2] , [52] . Although much is known about this circuit , we could not find any previous reports on the general properties of the circuit ( e . g . , relative UTR length of Oct4-bound genes ) . To determine if the unusual properties of the Wor1 circuit in Candida are shared with the Oct4 circuit , we performed a meta-analysis of publicly-available data , including ChIP-Seq-based Oct4 binding data [30] , [31] and microarray-based profiles of gene expression during stem cell differentiation [29] ( Materials and Methods ) . We discovered that the Oct4 circuit of mice does indeed share “unusual” characteristics with the Wor1 circuit of Candida . Intergenic regions bound by Oct4 are 33% longer than expected by chance ( median 23 kb in the circuit versus 17 kb genome-wide; Mann-Whitney P-value = 10−3 ) and are 2-fold longer than expected if they also flank a transcript that is differentially expressed during differentiation ( median 34 kb in the differentially-expressed circuit; Mann-Whitney P-value = 10−4; Figure 8A ) . 5′ UTRs and 3′ UTRs are also longer than expected ( 161 and 1048 bp in the circuit versus 137 and 727 bp genome-wide; Mann-Whitney P-values = 10−5 and 10−12 , respectively; Figure 8B and 8C ) , but the relative magnitude of length bias for 5′ versus 3′ UTRs ( +18% and +44% , respectively ) is flipped relative to that observed in the Wor1 circuit ( +58% and +22% , respectively ) . Unfortunately , the appropriate data are not yet available to determine whether UTR lengths are frequently changing between cell types in the Oct4 circuit of mice as they are in the Wor1 circuit of Candida . By sequencing the transcriptomes of white and opaque cells ( Figure 1 ) and applying a novel computational approach ( Figure 2A ) , we have provided the first transcript annotation for C . albicans ( Figure 2B ) , the most prevalent human fungal pathogen . This new view of the C . albicans transcriptional landscape includes over a thousand newly discovered transcripts , some of which are transcribed antisense to previously annotated genes , but many of which are entirely isolated from other genes . A subset of these transcripts codes for proteins , some of which are specific to Candida species and may function in host-pathogen interactions . Overall , the new view of gene expression in C . albicans is reminiscent of that provided by recent sequencing of the transcriptome of another yeast species , S . cerevisiae [28] , [34] , [38] , but with two important differences . First , we have captured a more faithful depiction of the transcriptome by using a method that measures expression across entire genes in a strand-specific fashion . Second , relative to the model organism S . cerevisiae , the transcriptome of C . albicans was poorly characterized prior to RNA sequencing . Our analysis dramatically expands the view of transcription in this yeast , resulting in annotations for hundreds of new coding and non-coding transcripts and thousands of UTRs . The revised annotation and expression data allowed us to examine , at unprecedented resolution , the differences between two cell types . White and opaque cells are specified by one of the largest known transcriptional circuits in C . albicans; as discussed in the introduction , each cell type is heritable for many generations and switching between them is epigenetic . Our principle findings are summarized as follows: In addition to the conclusions listed above , a comparison of the RNA-seq data from C . albicans to those determined in other species reveals some important differences and similarities . With the new strand-specific data presented here we were able to systematically examine changes in the expression of sense and antisense transcripts . The high frequency of antisense transcripts combined with the weak anti-correlated expression of transcripts in sense-antisense pairs ( Figure 3D ) suggests that while transcriptional interference mechanisms likely control transcription rates in some cases , antisense transcription may also play a different role in this yeast , perhaps acting post-transcriptionally via RNAi mechanisms Genome-wide anti-correlated expression of sense-antisense pairs was previously observed in S . cerevisiae [27] , but in that study the anti-correlation across all sense-antisense pairs was stronger than what we observed here . It is possible that the difference between species is related to the loss of mechanisms for post-transcriptional control by antisense transcripts in S . cerevisiae , but not in C . albicans [54] . Thus , whereas C . albicans may use antisense transcripts for a mix of transcriptional and post-transcriptional regulation , antisense transcription in S . cerevisiae may function primarily to regulate sense transcripts through transcriptional interference . Finally , we note several striking mechanistic similarities between the Wor1 circuit that governs white-opaque switching in yeast and the Oct4 circuit that controls the pluripotency and differentiation of mammalian embryonic stem cells . In both systems , differentiation is controlled by a series of master transcription regulators arranged in interlocking feedback loops , the differentiation process requires long periods of time relative to the cell division time , and the differentiated states are “remembered” through many cell generations [1] , [17] , [18] , [50] . In each system , hundreds of binding sites for the master regulator were thought to be “non-functional” [25] , though , as we have shown here for the yeast system , many of these instead are likely to impart cell-type specific expression to previously unannotated transcripts . In addition , amongst the direct targets of the master regulators is an abundance of genes that encode transcription regulators themselves [13] , [29] , [55] and genes with unusually long upstream intergenic regions ( compare Figure 7A to Figure 8A ) and abnormally long UTRs ( compare Figure 7B and 7C to Figure 8B and 8C ) . It seems likely that the latter two characteristics reflect a large number of regulatory inputs to genes of these circuits . The expanded upstream regions may also allow the formation of more complex tertiary chromatin structures involved in gene regulation [56] , [57] . Regardless of their function , they are clearly identifiable landmarks of both circuits . We have also shown here that many of the long UTRs are regulated , in the sense that they are longer in one cell type and shorter in the other . Finally , it appears as though non-coding RNAs are an important component of both circuits [31] . Taken together , these findings suggest an unexpected level of sophistication is required to maintain distinct cell types through many cell divisions—whether in a relatively simple fungal system with only two cell types , or in a complex mammalian developmental system involving numerous differentiated tissues . White cells of mating type a/a were selected by growth of C . albicans strain QMY23 [58] , a derivative of the sequenced strain SC5314 , on sorbose medium [59] . Opaque cell lines were then isolated following spontaneous cell-type switching . Liquid cultures of white or opaque cells ( two samples of each , referred to throughout the manuscript as white and opaque replicate #1 and white and opaque replicate #2 ) were grown at 23°C in SC medium [60] supplemented with 100 mg/l uridine to an OD600 of 1 ( log phase growth ) . Samples ( 5 ml ) were collected by centrifugation ( 5 min , 2000 g , 4°C ) , and pellets frozen in liquid nitrogen . Total RNA was extracted from frozen pellets as described [61] . For each sample , poly ( A ) RNA was isolated from 50 µg of total RNA by two rounds of purification using a Poly ( A ) Purist MAG kit ( Ambion ) . To construct libraries suitable for SOLiD System sequencing ( Figure S1 ) , each poly ( A ) -selected RNA sample ( 150–300 ng ) was fragmented in a 10 µl volume by incubation with 1 unit of RNase III and 1X reaction buffer ( Ambion ) for 10 minutes at 37°C . Fragmented RNA was then immediately diluted to 100 µl and purified using a RiboMinus Concentration Module ( Invitrogen ) following manufacturer's protocol , with the following modifications: sample was initially mixed with 100 µl Binding Buffer and 250 µl ethanol , column was washed only once with 500 µl Wash Buffer , and purified sample was eluted in 20 µl water . RNA fragmentation was confirmed and sample quantified using an Agilent 2100 Bioanalyzer , with an RNA 6000 Pico Chip , following manufacturer's protocol . 50 ng fragmented RNA was dried by vacuum centrifugation at low heat , then suspended in 3 µl water . An amplified cDNA library was constructed using components from the SOLiD Small RNA Expression Kit ( Ambion ) . Hybridization and ligation of Adaptor Mix A to the fragmented RNA and reverse transcription were carried out according to manufacturer's protocol , but with 18 h ligations and no RNase H treatment . cDNA was brought up to 100 µl and purified using a Qiagen MiniElute PCR Purification Kit , following manufacturer's protocol . Half of the eluted cDNA was mixed with an equal volume of loading dye ( 95% formamide , 0 . 5 mM EDTA , 0 . 025% each bromophenol blue and xylene cyanol FF ) , heated to 95°C for 3 min , then cooled immediately on ice . Sample was run on a 7 cm denaturing 7M urea/1X TBE/6% polyacrylamide gel at 180V for 17 min , then stained with SYBR Gold Nucleic Acid Gel Stain ( Invitrogen ) . DNA was visualized by UV-illumination , and material between 100–200 nt excised by scalpel . The excised region was further cut into 4 vertical strips ( such that each represented the same DNA size distribution ) . Amplification was performed directly on gel strips again using components from the SOLiD Small RNA Expression Kit ( Ambion ) . Two 100 µl PCR reactions were performed , each with one gel strip , 1X PCR Buffer , 0 . 2 mM dNTP mix , 2 µl AmpliTaq DNA Polymerase , and 2 µl SOLiD PCR Primer Sets 1 , 2 , 3 or 4 ( for white and opaque sample replicates #1 and white and opaque sample replicates #2 , respectively ) . Reactions conditions were 95°C ( 5 min ) ; 16 cycles of 95°C ( 30 sec ) , 62°C ( 30 sec ) , and 72°C ( 30 sec ) ; 72°C ( 7 min ) . The two amplification reactions were pooled and purified using a PureLink PCR Micro Kit ( Invitrogen ) following manufacturer's protocol , but combining two sequential elutions . To ensure appropriate size distributions ( >75% of product >140 bp ) , products were assayed using a Bioanalyzer DNA 1000 chip; yields ranged from 360–1140 ng . Templated beads were generated for sequencing using standard manufacturers' protocols . Beads from the first pair of white and opaque libraries ( “Replicate #1” ) were deposited onto a full slide with 8 other barcoded libraries not presented here . Beads from the second pair of white and opaque libraries ( “Replicate #2” ) were deposited onto two quadrants of a slide each . Massively parallel ligation sequencing was carried out to 50 bases using Life Technologies SOLiD System V3 and following the manufacturer's instructions . For microarray analysis , we used aliquots of the same total RNA samples used to generate the WT libraries ( replicate #2; discussed above ) . Aminoallyl-labeled cDNAs were synthesized using 5 µg of total RNA in 50 µl reverse transcription reactions with 250U SuperScript III Reverse Transcriptase ( Invitrogen ) , as described previously [58] . The cDNA samples were dried in a speed-vac to ≤9 µl total . Samples were then brought to 9 µl with water and supplemented with 1 µl of fresh 1M Na Bicarbonate , pH 9 . 0 . Cy3 and Cy5 dyes were prepared by re-suspending Amersham mono-reactive dye packs ( Cat . #PA23001 and PA25001 ) in 10 µl DMSO , and 1 . 25 µl of either Cy3 or Cy5 were added to each sample . Labeling reactions were incubated for one hour at room temperature in darkness . Dye-coupled cDNA samples were purified by adding 800 µl of Zymo DNA binding buffer ( Zymo Research ) to each sample and loading onto Zymo-25 columns . The remainder of the purification was performed as per the manufacturer's directions , and the samples were eluted with 40 µl of water . For each competitive hybridization , 0 . 2 µg each of Cy3 and Cy5 labeled cDNA were combined in 25 µl final volume of water , incubated at 95°C for 3 min , cooled to room temperature , mixed with 25 µl of Agilent 2x GE hybridization buffer ( HI -RPM ) , and loaded onto individual “blocks” ( 40 µl each ) on Agilent 8x15k custom gene expression microarrays . Hybridization was carried out at 65°C for 16 hours and the arrays were washed with Agilent wash buffers as per the manufacturer's recommendations . Whole transcriptome reads were aligned to a modified version of the Assembly 21 release of the Candida albicans genome [62] . As this is a haploid assembly , known single nucleotide variation between alleles from the most recent diploid assembly ( Assembly 19 , [63] ) was mapped to Assembly 21 , and the genome sequence was modified to reflect these ambiguous positions , allowing expressed sequences from either allele to be aligned equivalently . Alignment was performed with Life Technologies' SOLiD Whole Transcriptome Pipeline [32] , [64] . This software is open-source and freely available ( http://solidsoftwaretools . com/gf/project/transcriptome/ ) . An overview of the alignment strategy is presented in Figure S2 . In all the analyses of gene expression presented here , only reads that were both uniquely and fully aligned were considered . A “uniquely and fully” aligned read is defined as a read with a max-scoring alignment to the genome ( 1 ) scoring at least 31 ( alignment score is calculated with a match score of +1 and a mismatch score of −2 ) , ( 2 ) scoring at least 9 higher than any of the other alignments of that read to the genome , and ( 3 ) at least 40 bp long . All sequence data have been deposited at the MIAME compliant Gene Expression Omnibus ( GEO ) database at the National Center for Biotechnology Information ( http://www . ncbi . nlm . nih . gov/geo ) and are accessible through accession number GSE21291 . Known and novel splice junctions were identified by looking for sets of read sequences whose alignments share a gap ( specifically , a deletion relative to the reference ) with the same genomic start and end coordinates . We determined empirically that by requiring at least 5 such reads , and considering only deletions of at least 50 nucleotides , we captured , and thus validated , 85% of the 421 known junctions , while also predicting 158 novel junctions or deletions . False positives were filtered from this set by requiring matches to splice motifs and by removing deletions caused by obvious artifacts ( e . g . , cleavage and polyadenylation junctions ) , yielding 45 new introns in total . The details of this method are provided elsewhere in Mitrovich et al . ( In preparation ) [33] . A TAR is a region of the strand-specific genome exhibiting a cluster of sequence coverage , most often representing the presence of an exon . We employed a sliding window approach to identify such clusters on each strand of the C . albicans genome . The approach is described in depth in the manual for Life Technologies' Novel Transcribed Region ( NTR ) finder ( http://solidsoftwaretools . com/gf/download/docmanfileversion/138/693/NTR_Finder_Manual_v1 . 1 . pdf ) . Briefly , a window of specified size is scanned base-by-base across the genome , average sequence coverage is calculated within each window , and windows with average coverage greater than a specified cutoff are marked . A set of contiguous marked regions in the genome is then joined and trimmed from each end to better fit the coverage profile , forming a putative TAR ( pTAR ) . We used the NTR finder to perform TAR-finding on the combined dataset of all four sequence libraries presented in this work . TAR-finding was performed with many different parameter sets ( i . e . , different values chosen for the size of the window and the minimum average coverage required for the marking of a region ) and it was determined that a window size of 125 and minimum average coverage of 20 were optimal for reproducing the previously annotated TARs ( aTARs ) , with the expectation that the pTARs would be slightly larger than the aTARs because the existing annotations were ORF-based only and thus did not include UTR definitions ( Figure S3 ) . Other parameters were kept fixed: min-score = 25 , trimming-fraction = 0 . 01 , min-overlap = 0 . 9 . The existing transcript annotation ( Ca21 ) , which is primarily based on putative ORF sequences , was downloaded from the Candida Genome Database ( http://www . candidagenome . org/ ) and the exons defined therein were used as our aTARs . In merging the pTARs with aTARs to define a new transcript annotation , we found that in addition to this optimal pTAR set ( pTAR_opt_set , with parameters window-size = 125 , min-window-coverage = 20 , min-score = 25 , trimming-fraction = 0 . 01 , and min-overlap = 0 . 9 ) , a more fragmented pTAR set produced from a smaller window size ( pTAR_frag_set , with parameters window-size = 10 , min-window-coverage = 20 , min-score = 25 , trimming-fraction = 0 . 01 , and min-overlap = 0 . 9 ) was also helpful ( see below ) . We also experimented with Hidden Markov Model ( HMM ) approaches to finding pTARs ( not shown ) , but found that the models we trained did not perform better than the simpler sliding window approach taken here . In fact , they tended to perform much worse , which may simply reflect that we did not find the best way of modeling the segmentation problem . Rather than providing a completely de novo transcript annotation [34] , we sought to leverage the existing annotation to provide an updated transcript annotation in which existing ORF-encoding regions , if expressed , were augmented with 5′ and 3′ UTRs and isolated TARs ( i . e . , those not overlapping an aTAR on the same strand ) were added to the transcript annotation as novel TARs ( nTARs ) . Thus , we employed a set of rules that merged the pTAR_opt_set with the aTARs in the previous transcript annotation ( Ca21 , from the Candida Genome Database [65] ) to form a new set of transcript annotations . The rules are most concisely described diagrammatically in Figure S4 . For transcripts found to contain one or more splice junctions , the internal exon coordinates defined by reads spanning those splice junctions are used in place of those defined by the pTARs ( i . e . , splice junction-derived coordinates override these purely coverage-based coordinates ) . The more fragmented pTAR_frag_set was used to define transcript boundaries in cases where two or more aTARs were overlapped by a single pTAR ( scenario ‘f’ in Figure S4 ) , which typically happens when transcripts are positioned very close to one another on the same strand . In such cases , if a pTAR was found in the more fragmented set that overlapped the edge of one aTAR without also overlapping the edge of the other aTAR , this pTAR was used to define the UTR of the overlapping aTAR in the new annotation . We performed 10 , 000 rounds of simulation to determine whether the 561 nTARs containing an ORF of length 40 amino acids or longer was more than expected by chance . In each round , 1 , 443 regions with the same size distribution as the 1 , 443 nTARs were chosen randomly in a strand-specific fashion from regions of the genome not covered by ORFs in the previous annotation ( i . e . , the Ca21 ORF-based annotation ) . The median number of ORFs found per round was 453 . 561 or more ORFs were not found in any round of the simulation ( P-value <0 . 0001 ) . For each transcript model ( in either the new or old annotation ) , reads that uniquely aligned to the genome within its exons or across its splice junctions were counted . One pseudo-count was added to this sum and the resulting modified raw transcript count was converted to a normalized measurement of abundance by normalizing for transcript length and total number of uniquely aligned reads in the sample ( i . e . , RPKM; reads aligned per kb of transcript per million uniquely aligned reads ) [39] , [66] . The fold-change of each transcript between cell types was then computed by dividing its mean RPKM across opaque cell replicates by its mean RPKM across white cell replicates . We employed a recently proposed likelihood ratio test combined with a fold-change cutoff to define sets of differentially expressed transcripts [37] . Specifically , a false discovery rate ( FDR ) less than or equal to 10−4 and an absolute fold-change greater than or equal to 2 defined a set of 1306 differentially expressed transcripts using the new transcript annotation and a set of 824 using the old annotation . RPKM , fold-change estimates , P-values and FDRs for each transcript can be found in Table S3 . Microarray data were normalized and differentially expressed transcripts were identified using limma v2 . 16 . 5 [67] in R v2 . 8 . 1 . Background correction was performed with the “normexp” method and an offset value of 50 . Normalization was then performed within arrays using the “loess” method and between arrays using the “quantile” method . Finally , differential expression of transcripts between white and opaque cells was determined on our dye-swapped replicate arrays using the “lmFit” and “eBayes” methods , which produced fold-change estimates and Benjamini-Hochberg multiple test-corrected P-values for each probe on the array . For each transcript , only the expression value given by the probe with the highest average expression value ( i . e . , AveExpr value ) was used in downstream analysis . As with the analysis of the RNA-Seq data , we applied an adjusted P-value cutoff of 10−4 and required an absolute fold-change greater than or equal to 2 . This defined a set of 512 differentially expressed transcripts . Wor1-bound regions were identified as peaks of binding enrichment in the Wor1 ChIP-Chip data using the “Extract peaks from Data Set ( s ) ” utility of MochiView v1 . 311 [68] . The algorithm is described in detail in the MochiView manual . Briefly , a smoothing function is applied to the log2 enrichment values of the Wor1 ChIP-Chip tiling arrays followed by the application of an algorithm to detect local regions of maximal enrichment ( i . e . binding peaks ) , which are assigned a P-value using permutation testing . Note that this algorithm is not based on deconvolution of binding events using shearing profiles – in the case of the Wor1 ChIP-chip data , the binding peaks are atypically broad and varied , and thus tend to confound deconvolution-based algorithms . Peak extraction was applied independently to the normalized ChIP-Chip data derived from antibodies targeting the N- and C-terminus of Wor1 [13] . Peak-finding significance thresholds were kept at their default values ( P≤0 . 001 in the Wor1 ChIPs of wild-type cells and P>0 . 05 in the Wor1 ChIPs of wor1ΔΔ controls ) , though the amount of sampling was increased 10-fold from default to improve significance estimates . The minimum value for peak inclusion/consideration was set to 0 . 25 . All other settings were kept at their default values . It was subsequently determined that the union of Wor1-bound regions defined independently from the N- and C-terminal datasets gave the best concordance with microarray-based and RNA-seq-based gene expression measurements of differential expression . Thus , the 504 Wor1-bound regions used throughout this work result from taking the union of Wor1-bound regions generated from the N- and C-terminal ChIP-Chip datasets . For the purposes of comparing Wor1 binding to differential expression , Wor1-bound regions were associated with nearby divergently transcribed transcripts as depicted in Figure S7 . For the purposes of calculating the distribution of intergenic lengths “in the Wor1 circuit” a slightly different approach was taken to associate Wor1-bound regions with nearby transcripts than described above . In this case , Wor1-bound regions that fall within intergenic regions were associated with all divergent transcripts within 1 kb and intergenic regions that associated with one or more such transcripts were determined to be “in the Wor1 circuit” . This approach avoids the problem of length correction required under the null model that binding sites are distributed randomly throughout the genome ( i . e . , that longer intergenic regions are inherently more likely to have random binding ) . Similarly , to avoid length bias when determining the distribution of 5′ and 3′ UTR lengths “in the Wor1 circuit” , we only considered Wor1-bound regions that resided in the intergenic space immediately upstream of the transcript , thereby avoiding the possibility that random binding to the longer UTRs themselves would drive artificial UTR length discrepancies . Putative cases of UTR length change between cell types were isolated by comparing changes in UTR expression to changes in coding sequence ( CDS ) expression between the cell types . We first calculated differential expression ( in white versus opaque cells ) independently for the 5′ un-translated , coding , and 3′ un-translated regions of each coding transcript . The number of reads aligned within each region of a transcript was counted in the merged set of alignments from each cell type ( i . e . , the two biological replicates for each cell type were combined ) and a single pseudocount was added . The counts for the opaque cell type , whose dataset had 4% more uniquely aligned reads overall , were normalized by the ratio of uniquely aligned reads in the datasets of the two cell types ( i . e . , they were multiplied by a constant factor of 0 . 96 ) . Fold-changes were calculated for each transcript region by dividing the normalized count in opaque by the count in white cells . We then scanned for UTRs whose expression changed more or less than their corresponding coding sequence , as determined by a χ2 test of independence comparing the observed , normalized UTR counts to the expected counts in the two cell types . The expected count for each CDS region in each cell type was calculated by redistributing the total reads counted across cell types for the corresponding UTR in a fashion proportional to the fold-change calculated for the CDS . To ensure accurate fold-change estimates for the CDS regions , only transcripts with a CDS that had at least 50 reads aligned in at least one cell type were considered . By also requiring a minimum 2-fold absolute difference in fold-change values for the UTR and CDS regions and a χ2 P-value less than 10−5 , we identified 145 transcripts with putative UTR length changes ( Table S4 ) . The analysis of transcript features in the Oct4 circuit was performed on publicly available data . Lists of Oct4-bound regions in mouse ES cells determined independently by Chen et al . [25] and Marson et al . [31] were downloaded from supplemental tables provided by these groups in their respective publications . The intersection of bound regions from these two sources was taken to define a high confidence set of Oct4-bound regions that was used for all further analysis . Gene expression measurements of differentiating mouse ES cells were downloaded from a supplemental table provided by Loh et al [29] . For the purposes of our analysis , we considered transcripts that were significantly ( multiple test-corrected P-value ≤10−4 ) up- or down-regulated across the 18 profiling experiments ( median fold-change of at least 1 . 5 ) to be differentially expressed between cell types . Mouse transcript annotations were downloaded from the UCSC Genome Browser ( http://genome . ucsc . edu/ ) and are based on alignments of RefSeq transcripts to assembly mm8 of the mouse genome sequence [69] . The distribution of intergenic lengths “in the Oct4 circuit” was calculated as described above for the Wor1 circuit , except that in the mammalian circuit transcripts could be up to 10 kb away from an Oct4-bound region . We allow a longer distance here since intergenic regions are overall much longer in mouse and because regulation is generally expected to occur over longer distances . The distribution of 5′ and 3′ UTR lengths “in the Oct4 circuit” was calculated as described above for the Wor1 circuit .
The differentiation of cells into distinct cell-types , each of which is “remembered” for many generations , underlies the development of both healthy and cancerous tissues . Such differentiation , however , is not restricted to multi-cellular organisms: “white” and “opaque” cells of the unicellular fungal pathogen Candida albicans are two heritable cell-types , each thought to be adapted to unique niches within their human host . Here we examine the differences between these two cell-types by sequencing their RNA contents and subsequently reconstructing and comparing their gene expression profiles . We know that the transcription factor Wor1 plays a central role in mediating these expression differences . As with many other transcriptional regulators , however , a major unresolved issue is the apparent discordance between the genomic locations to which Wor1 binds and whether neighboring genes are differentially expressed . Here we resolve this discordance , showing that hundreds of Wor1 binding sites , previously without apparent function , actually flank differentially-expressed genes that were undiscovered , or not measured accurately , before . Additionally , we find that transcripts regulated by Wor1 have many unusual properties , several of which we also observe for transcripts regulated during the development of mammalian embryonic stem cells , suggesting they may be general hallmarks of cell differentiation .
You are an expert at summarizing long articles. Proceed to summarize the following text: The translocation of single-stranded DNA ( ssDNA ) across membranes of two cells is a fundamental biological process occurring in both bacterial conjugation and Agrobacterium pathogenesis . Whereas bacterial conjugation spreads antibiotic resistance , Agrobacterium facilitates efficient interkingdom transfer of ssDNA from its cytoplasm to the host plant cell nucleus . These processes rely on the Type IV secretion system ( T4SS ) , an active multiprotein channel spanning the bacterial inner and outer membranes . T4SSs export specific proteins , among them relaxases , which covalently bind to the 5' end of the translocated ssDNA and mediate ssDNA export . In Agrobacterium tumefaciens , another exported protein—VirE2—enhances ssDNA transfer efficiency 2000-fold . VirE2 binds cooperatively to the transferred ssDNA ( T-DNA ) and forms a compact helical structure , mediating T-DNA import into the host cell nucleus . We demonstrated—using single-molecule techniques—that by cooperatively binding to ssDNA , VirE2 proteins act as a powerful molecular machine . VirE2 actively pulls ssDNA and is capable of working against 50-pN loads without the need for external energy sources . Combining biochemical and cell biology data , we suggest that , in vivo , VirE2 binding to ssDNA allows an efficient import and pulling of ssDNA into the host . These findings provide a new insight into the ssDNA translocation mechanism from the recipient cell perspective . Efficient translocation only relies on the presence of ssDNA binding proteins in the recipient cell that compacts ssDNA upon binding . This facilitated transfer could hence be a more general ssDNA import mechanism also occurring in bacterial conjugation and DNA uptake processes . Agrobacterium tumefaciens is a Gram-negative pathogenic bacterium able to transfer and integrate up to 150 , 000-bases-long single-stranded DNA ( ssDNA ) into the infected cell nuclear genome [1] . In Agrobacterium pathogenesis , the sequence of ssDNA to be transferred ( T-DNA ) and the genes encoding the virulence ( Vir ) proteins required for transfer of T-DNA into the host are localized on a large plasmid called the tumor-inducing plasmid [2] . Some virulence proteins have a function in the bacterium , namely the 11 VirB proteins and VirD4 , which compose the Type IV secretion system ( T4SS ) machinery . T4SS exports T-DNA and effector proteins out of the bacterium [3–5] . The effectors are proteins , which are synthesized in the bacterium but exert their function in the recipient cell . The export signal of the effector proteins is localized at their C terminus and is recognized by VirD4 [6] . Among the effectors , the relaxase VirD2 binds covalently to the 5′ end of the ssDNA . The combined action of the three NTP-binding/hydrolysing proteins VirB4 , VirB11 , and VirD4 has been proposed to energize the transfer of the proteins and VirD2-T-DNA through the T4SS [7] . How the T-DNA then crosses the plasma membrane of the host remains unknown , but the effector protein VirE2 might be involved . In vitro , VirE2 was shown to form channels , which transport ssDNA , and VirE2 was hence proposed to mediate transfer of T-DNA through the eukaryotic plasma membrane [8–10] . VirE2 is a necessary , multifunctional protein [11] and another important function of VirE2 is to bind cooperatively T-DNA in the host cytosol . The interaction of VirE2 with T-DNA mediates its import into the nucleus . As evidenced by scanning transmission electron microscopy ( STEM ) , the VirE2–ssDNA complex consists of a helical structure in which 19 nucleotides are bound per VirE2 monomer [12] . This structure prevents exonuclease degradation in vitro [13] . Moreover , recent in vitro experiments demonstrate the microtubule-guided transport of such DNA–VirE2 complexes [14] . Using single-molecule technology , we measured the binding properties of VirE2 to ssDNA , and we suggest here that VirE2 binds to ssDNA nucleotides in a zipper-like mode . This property was confirmed biochemically with the ability of the VirE2 protein to bind to a shorter oligonucleotide than its footprint of 19 nucleotides . We also show that cooperative VirE2 binding compacts the ssDNA against high loads ( 50 pN ) , which could , in vivo , help to actively pull the T-DNA into the recipient cell . Using cell biology detection techniques , VirE2 was localized at the plant cell periphery , an ideal localization for VirE2-mediated pulling of the incoming T-DNA . Altogether , a combination of very different techniques allowed the emergence of a completely new view on T-DNA transfer energetics upon translocation into the host plant cell . Binding of VirE2 to ssDNA was studied using different optical tweezers modes ( Figure 1B , inset ) . First , the VirE2 binding rate was determined at a pre-set force that was kept constant by a feedback system ( force-feedback experiment , [Figure 1B , inset]; VirE2 concentration of 20 μg/ml , 330 nM ) . Polymerization of VirE2 dramatically affected the length of the tethered ssDNA ( Figure 1A ) . From a detailed analysis of the time traces [Figure S1 , showing a plot where the transition takes place; Text S1 , section: Rate of polymerization ( experimental determination ) ] , we found a value of ( 1510 ± 200 ) nm/s ( n = 5 ) for the polymerization rate originating from a single nucleation site ( 5 pN ) . For the 4 , 502-bases-long DNA and taking 19 as the number of nucleotides bound per VirE2 monomers ( as determined by scanning transmission electron microscopy ) [12] , this yields a binding rate of ∼10 VirE2/ms at a VirE2 concentration of 20 μg/ml ( 5 pN ) . These force-feedback experiments were performed at different forces . For forces ≤22 pN , the normalized extension at full polymerization was found to be about 0 . 11 ± 0 . 02 ( n = 21 ) . Compaction also occurred even when the ssDNA was forced to remain in an extended form ( 50 . 5 pN; Figure 1A ) . At this force , the polymerization rate was found to be considerably slower ( ∼ 50 nm/s , Figure S1 ) . Force-feedback experiments performed at high forces ( >22 pN ) yield a normalized extension at full VirE2 coverage of 0 . 66 ± 0 . 05 ( n = 11 ) , much longer than the one observed at low force ( about 0 . 1 ) ( Figure 1A ) . This 6-fold difference in normalized extension ( observed at full coverage ) indicates that VirE2 filaments adopt a different global structural arrangement depending on the preset force . This point will be discussed in details below ( section: Global Structural Arrangement of ssDNA upon VirE2 Binding ) . Force-feedback measurements at different forces allow the force dependence of the polymerization rate k ( f ) ( originating from a single polymerization front , see Figure S1 ) to be determined ( Figure 1B ) . Using the Arrhenius law , k ( f ) is described by k ( f ) = k0 exp ( -<w ( f ) >/kBT ) , where <w ( f ) > is the work produced by VirE2 per locally bound single nucleotide [15] , k0 isthe rate at zero force , kB is Boltzmann's constant , and T is temperature . In a local model , <w ( f ) > is approximated to f ( LSS<cosθ> – LV ) , ( Text S1 , section: Rate of polymerization ( theory ) , and Figures S2 and S3 , showing a detailed analysis of the model ) , where LV ( LSS ) is the DNA base-to base backbone distance in the presence ( absence ) of VirE2 . From structural data , a value of 0 . 7 nm is found for LSS [16] . In a freely jointed chain ( FJC ) model , <cosθ> follows the Langevin formula [17] , yielding an analytical expression for k ( f ) . The local model gives a good description of the experimental data when the base-to-base distance of the VirE2-bound ssDNA LV equals 0 . 41 nm ( Figure 1B and Figure S3 ) . This value ( 0 . 41 nm ) is estimated from electron microscopy ( EM ) studies ( assuming the ssDNA to lie concentrically within the protein helix [18] , Figure 2A ) and is in good agreement with a statistical analysis of the compaction steps ( Figure S4 , probability density function ( PDF ) analysis of the time trace at 50 . 5 pN ) . The good description of the experimental data by the local model suggests that VirE2 monomers bind one nucleotide at a time in a zipper-like motion and that the probability of binding a nucleotide is site-independent , a prerequisite for the local model . Such a model predicts that VirE2 could bind stably to less than 19 nucleotides . Experimental proof was provided by a gel-shift assay ( Figure 2B ) , which demonstrated that VirE2 can bind a 12-bases-long oligonucleotide . Finally , the good description of the force-feedback measurements ( Figure 1B ) by the local model suggests that the base-to-base distance of ssDNA bound to VirE2 is force independent . Standard force-versus-extension curves ( pulling and relaxing the tethered DNA molecule without any feedback; “pull and relax” ) ( Figure 1B , inset ) at lower protein concentration ( 6 μg/ml , 100 nM ) were recorded ( Figure 3A ) . These curves show the progressive compaction of bare ssDNA ( red ) as coverage with VirE2 proteins occurs , up to a state where the filament adopts a stable conformation ( black ) . This final conformation ( for which subsequent pulls did not noticeably change the shape of the force-versus-extension curves ) yields an average compaction factor of 9 . 7 ± 2 . 0 ( n = 15 ) . Previous EM studies have reported a compaction factor of 11 . 9 for a perfect VirE2-ssDNA helical structure ( Text S1 , section: Length reduction upon protein binding ) . This suggests that the final state we observe ( also confirmed by distance-clamp experiments , Figure S6 ) corresponds to a conformation where the VirE2 proteins rearrange into a helix ( Figure 2A ) . As seen in Figure 3A , the final state ( black curve ) is extremely stiff ( as compared to ssDNA ) . Curves recorded at intermediate stages of polymerization ( green , blue ) can be fitted with a FJC model considering the ssDNA compaction factor upon VirE2 binding of 11 . 9 , the persistence length of bare ssDNA and a normalized contour length l = λlssDNA + ( 1 - λ ) /11 . 9 ( 0 < λ < 1 ) , where lssDNA is the normalized contour length of ssDNA ( Figure 3A , gray lines ) . Therefore , partially coated ssDNA-VirE2 filaments ( blue and green curves ) exhibit two domains . First , the flexible , uncoated ssDNA of length λlssDNA , and second the almost nondeformable , fully VirE2-coated domain of length ( 1 - λ ) /11 . 9 . Through the sequential binding and subsequent release of VirE2 proteins from bare ssDNA molecule ( red curve ) , the final helical conformation is obtained ( black curve ) . In Figure 3A , the intermediate state of polymerization ( blue curve , normalized extension of about 0 . 2 ) shows the detachment of a large amount of VirE2 proteins at ∼50 pN ( yielding a decrease in the VirE2 coverage , i . e . , an increase in the fraction of bare ssDNA in the filament from λ = 0 . 27 to λ = 0 . 46 ) . When the force applied to ssDNA was relaxed , VirE2 molecules bound to ssDNA again , achieving a more stable coverage , since almost no VirE2 was driven off upon restretching of the DNA–protein complex up to 70 pN ( green curve ) . These findings correlate nicely with the binding mode of VirE2 . VirE2 is a non–sequence specific ssDNA binding protein , and the interaction of VirE2 with ssDNA ( at a low protein concentration of 6 μg/ml ) leads to multiple nucleation sites . This yields a number of different VirE2-ssDNA helical domains , which might not be in register ( i . e . , yielding a nonperfect helical structure over the whole length of the ssDNA , Figure 3B ) . When the VirE2-ssDNA filament is pulled , short VirE2 domains seem to progressively detach from the ssDNA molecule . When the tension is relaxed , VirE2 proteins bind again . Subsequent pulls yield an increase in the average length of VirE2 helical domains , which then resist higher forces ( green curve ) . The final state therefore corresponds to an extremely stable conformation in which no VirE2 release from the filament is observed when proteins were removed from the fluid chamber . The fully polymerized nucleoprotein complex was unusually stiff ( Figure 3A , black curve ) . From the critical force for buckling |FB| ∼ 3 . 5 pN ( obtained while compressing the filament; Figure 3A , arrow ) , we estimate a persistence length ( |FB|l2/4π2kBT ) [19] of ∼14 μm , about 4 orders of magnitude larger than that of bare ssDNA [20] . Because binding of ssDNA to VirE2 in a zipper-like way requires some initial protein flexibility , the high stiffness measured in the final ( fully covered ) VirE2-ssDNA filaments suggests that VirE2 , the DNA , or both are stiffened by their interaction . A similar increase in stiffness upon DNA binding has been observed for other ssDNA binding proteins such as RecA [21] . As mentioned in the Text S1 ( section: Mechanical properties of ssDNA-VirE2 filaments: Helix model ) , a mechanical model consisting of a pure helical structure gives a value of 110–2 , 200 nm for the persistence length , much lower than reported here . This difference could be attributed to the presence of axial interactions along the protein helix ( reported by EM studies [18] ) that could considerably stiffen the structure ( Figure 2A , circles ) . For force-clamp experiments performed at low forces ( ≤22 pN , Figure 1 ) , the value for the normalized extension at full coverage was estimated to be at 0 . 11 ± 0 . 02 ( value obtained from a total of 21 experiments performed between 2 and 22 pN ) . This value for the normalized extension is in good agreement with that of EM studies for a perfect helical arrangement ( 0 . 084 or 1/11 . 9 [18] ) , suggesting that the helical VirE2-ssDNA structure can even form against loads up to ∼20 pN . This helical conformation was not achieved when performing force-feedback experiments at >22 pN . For these forces and at full coverage , the normalized extension was found to be 0 . 66 ± 0 . 05 ( estimated from a total of eight experiments performed at 30 , 36 , 45 , and 50 . 5 pN ) . This normalized extension corresponds to an average base-to-base distance of ssDNA ( projected along the direction of the applied force ) of 0 . 46 ± 0 . 04 nm ( Figure S5 , showing typical force versus extension curves of both ss- and double-stranded ( ds ) DNA ) , in close agreement with that found from EM studies ( 0 . 41 nm , Figure 2A ) [18] . From this result , we deduce that the rearrangement of the VirE2-ssDNA filament into a helix ( Figure 2A ) cannot proceed against large forces and that the normalized extension reduction observed at forces >22 pN corresponds to the sole binding of VirE2 on ssDNA . The small discrepancy between the expected value and the experimental observation , although significant , can be attributed to the large footprint of VirE2 ( 19 nucleotides ) as well as the possible loss of cooperativity at high forces . The local model ( Figure 1B and Figure S3 ) was shown to give a good description of the force dependence of the rate of polymerization . However , this model only considers the zipper binding mode of VirE2 to ssDNA and does not take into account the rearrangement into a helical structure . This suggests that the helical rearrangement is much faster than the local binding of VirE2 to ssDNA . Hence , the binding of VirE2 to ssDNA is the rate-limiting step of the overall polymerization process and dominates the kinetics . Note finally that we did not observe any compaction of the ssDNA molecule for force-clamp experiments performed at low protein concentrations ( <1 μg/ml ) . This correlates with gel-shift retardation experiments ( Figure S7 ) , which demonstrate that binding of VirE2 to 170-bases-long ssDNA occurs over a small range of protein concentration without intermediate bands . In vivo , the VirE2 protein exerts its role in the plant . It is sufficient to express the VirE2 protein in the plant to restore full virulence: transgenic plants expressing VirE2 allow efficient T-DNA transfection by nearly avirulent virE2-null-Agrobacterium [22] . If the VirE2 proteins accumulate at the periphery of the plant , then the interaction of VirE2 and ssDNA would not only protect the T-DNA from exonuclease degradation but also greatly facilitate the import of the T-DNA thanks to the capability of VirE2 to work against large forces when binding to ssDNA ( see above ) . Because localization of VirE2 protein originating from the bacterium has proven to be extremely challenging and has so far not yielded a usable result , we chose to use the fact that , when VirE2 is expressed in the plant , it is active . Hence , we transiently expressed VirE2HA in tobacco BY-2 cells . VirE2HA is a biologically active fusion ( Figure 4A ) and was used to perform immunofluorescence experiments . Figure 4B demonstrates the localization of VirE2 around the nucleus , at the cell periphery , in cytosolic strands , and in a few cytoplasmic spots . This non-nuclear cytoplasmic localization is supported by VirE2-GFP localization ( also an active fusion protein when expressed in plant cells , S . Gelvin , personal communication ) . On the contrary , β-glucuronidase ( GUS ) -VirE2 fusion protein was reported to localize in the nucleus [22] . This controversial results might be explained by the fact that the GUS-VirE2 fusion protein mimics the conformation of VirE2 when bound to ssDNA and hence get imported into the nucleus ( V . Citovsky , personal communication ) . Also , it is widely accepted by the community that the VirE2-ssDNA complex already forms in the host cytoplasm , allowing subsequent nuclear import of the nucleo-protein complex into the nucleus [2] . Hence , there must be some free VirE2 proteins in the host cytosol , which is consistent with our localization data ( Figure 4B ) . The Agrobacterium pathogenesis mechanism allows for the efficient transfer of long ssDNA molecules into eukaryotic cells [2] . The VirE2 protein is involved in this process by protecting the ssDNA from nuclease degradation and by mediating nuclear import [2] . Here , based on new experimental findings , we propose that VirE2 is an effector that is transported into the host cytoplasm at an early stage to actively pull the T-DNA into the host and protect it from nuclease degradation from the very first moment it enters the cell . In a first step , a single VirE2 protein binds to T-DNA as it enters the plant cell . This binding , occurring in a zipper-like motion , is mainly limited by thermal fluctuations of T-DNA . In a second step , the fast cooperative binding of VirE2 facilitates the formation of a helical structure and actively pulls T-DNA into the plant cytosol ( Figure 5 ) . This model has indirect assumptions . First , VirE2 and T-DNA should not interact in the bacterium , even though they are both synthetized there . Indeed , in Agrobacterium's cytoplasm , VirD2-T-DNA and VirE2 do not interact , and VirE2 only binds to the T-DNA once it is in the plant cytosol [23] . Second , the VirE2 protein should be present at the site of entry of the T-DNA , namely at the periphery of plant cells . This was evidenced by immunofluorescence experiments ( Figure 4B ) , suggesting that VirE2 is properly localized to assist T-DNA pulling as it enters the plant cytosol . Finally , the interaction between VirE2-bound ssDNA and the rigid microtubule network could provide an anchor point that would facilitate the VirE2 mediated-force transduction at an early stage of the translocation process [14] . According to our model , which identifies VirE2 as an essential factor that pulls T-DNA into the plant cytoplasm , the free energy released upon the formation of the nucleoprotein complex allows VireE2 proteins to work against large forces , which might be required to translocate T-DNA into the host ( see below ) . The production of mechanical energy occurs solely through the free energy gain during the binding of VirE2 to ssDNA without the need for an external source of energy , e . g . , nucleotide hydrolysis . To our knowledge , this is the first time that a glimpse at forces involved in ssDNA translocation into the recipient cell is obtained . Their magnitude compares to forces produced by dsDNA translocating molecular motors ( see , e . g . , [24] ) . Other competing mechanisms might tend to pull the DNA back out of the host cytosol . For instance , during conjugation , pili can retract after binding to the host cell [25] . Moreover , during DNA transfer into the host , the Type IV pilus of Neisseria gonorrhoeae can undergo a series of extension and retraction cycles , generating retraction forces up to a few tens of pN [25] . Thus , binding of a protein to the transferred ssDNA to form a complex that prevents recoiling of the ssDNA in the T4SS by such forces would be a great advantage . Tato et al . have proposed that the coupling protein TrwB of the Escherichia coli R388 conjugative system acts as an ATP-driven ssDNA transporting molecular motor [26] . This analogue to VirD4 is located at the bacterial inner membrane and is thought to pump ssDNA through the Type IV secretion channel . Considering the short persistence length of ssDNA ( ∼0 . 7 nm ) and the large distance between the coupling protein and the host membrane ( at least 30 nm [27] ) , just pushing the flexible ssDNA through the T4SS would be inefficient . Transfer would be facilitated if it were also actively pulled through by VirE2 present in the host . Single-molecule experiments have shown that the “final” VirE2-ssDNA helical filament obtained is a very stable and stiff structure . Washing the complex with buffer without VirE2 protein does not destabilize the complex . But in vivo , the uncoating of the filament is necessary for the integration of the T-DNA into the nuclear genome of the recipient cell . Hence , the question is how the rigid VirE2-ssDNA complex is freed from VirE2 . Indeed the very tight interaction between VirE2 and the ssDNA and between VirE2 molecules seem to need a specific mechanism of degradation to remove the VirE2 protein . It was shown recently that VirE2 is specifically targeted for degradation by the VirF-containing Skp1-Cdc53-cullin-F-box complex for proteolysis [28] . The critical role of proteasomal degradation in Agrobacterium-mediated genetic transformation was also evident from the inhibition of T-DNA expression by a proteasomal inhibitor . In summary , our findings and these data correlate nicely and explain why such a specific degradation mechanism would be needed . The unique mechanism that Agrobacterium exploits to translocate any ssDNA molecule has paved the way for genetic engineering of plants and fungi but also offers novel possibilities for gene transfer into mammalian cells [2] . However , the Agrobacterium pulling mechanism proposed here might be more general . It does not rely on VirE2 but needs the following: ( i ) an ssDNA binding protein compacting ssDNA upon interaction and ( ii ) occurrence of this single-strand binding ( SSB ) activity only in one compartment . In bacterial conjugation and DNA-uptake processes , SSB proteins are also present and might have an important funtion . For instance , the SBB homologs ( YwpH ) accumulate preferentially at the cell poles of B . subtilis [29] . Hence these proteins could be , as is VirE2 , capable of generating a force without external source of energy and pull the ssDNA into the recipient compartment . VirE2-His6 proteins were expressed in E . coli and purified as described in [30] , with the addition of glycerol ( final concentration 20% w/v ) to the sample buffer ( 50 mM NaH2PO4 , pH 8 , 300 mM NaCl ) before storage of the protein at −80 °C . Two types of DNA handles were prepared and used either for force-feedback ( type A ) or pull and relax ( type B ) experiments . Type A: DNA molecules were prepared by PCR amplification ( Taq DNA Polymerase , Roche , http://www . roche . com ) of the pTYB1 plasmid ( 7 , 477 bp ) [New England Biolabs ( NEB ) , http://www . neb . com] using 5′-Thiol- TGG TTT GTT TGC CGG ATC AAG AGC −3′ and 5′-TCC TAA GCC AAC AAT AGC GTC CCA-3′ as forward and reverse primers , respectively . The 4 , 927-bp PCR fragment was digested with HindIII ( NEB ) . Finally , the main fragment was end-filled with Klenow Exo- ( NEB ) with one dATP and one biotin-14-dGTP ( Invitrogen , http://www . invitrogen . com ) , yielding a 4 , 502-bp-long dsDNA . Type B: DNA molecules were prepared by PCR amplification ( Expand Long Template PCR System , Roche ) of the pPIA plasmid ( 15 , 071 bp ) using 5′-thiol-TAT CGT CGC CGC ACT TAT GAC TGT-3′ and 5′-TAT GTC GAT GTA CAC AAC CGC CGA-3′ as forward and reverse primers , respectively . The resulting 14 , 107-bp PCR fragment was digested with EagI ( NEB ) . After digestion , the longest fragment ( 13 , 883 bp ) was end-filled with Klenow Exo- ( NEB ) with two dGTPs and two biotin-14-dCTPs ( Invitrogen ) . DNA molecules were covalently coupled to 2 . 17-μm amino-modified beads ( Spherotech , http://www . spherotech . com ) using sulfo-SMCC ( Sigma ) as a cross-linker [21] . The experimental apparatus for optical tweezers experiments has been described [31] . DNA beads were trapped by the laser and the free biotinylated DNA end was attached to a 2 . 20-μm streptavidin bead ( Spherotech ) , which was held by suction on a micropipette . The bead-to-bead distance was determined from both the movement of the micropipette ( controlled with a closed-loop piezoelectric element ) and the deflection of the laser producing the optical trap ( monitored by a two-dimensional , position-sensitive detector ) . The pipette bead was moved away from the trapped bead at a constant velocity of 0 . 8 nm/ms . At this rate , complete force-extension curves were recorded within a few seconds . Forces were obtained from the direct measurement of the change in light momentum flux [31] . All signals ( distance , force ) were low-pass filtered at 159 Hz . Force curves were measured in assembly buffer ( 50 mM NaH2PO4 , pH 8 . 0 , 150 mM NaCl , and 5% w/v glycerol ) . To obtain ssDNA molecules , dsDNA was exposed to 150 mM NaOH . Subsequently , the chamber was rinsed with assembly buffer and VirE2 proteins were injected . Prior to injection , proteins were centrifuged at 14 , 000g for 20 min . The supernatant was kept at 4 °C and injected at a protein concentration ranging from 6 to 20 μg/ml in assembly buffer . Forces were monitored in a constant VirE2 flow . Experiments were performed at room temperature . The force-clamp mode uses a digital “P” ( proportional gain ) -like feedback that runs at 150 Hz ( taking into account the time for the acquisition , some CPU time for the calculations , and communication with the different instruments ) . In details , the feedback works as follows: if the change in force |Δf| is smaller than 0 . 7 pN , we do not feedback at all; for larger changes in force , the pipette if moved by ±5 nm ( |Δf|≤ 2 pN ) or Δf × 7 nm ( |Δf| > 2 pN ) . During a force-clamp operation , the data are only recorded and plotted when |Δf|≤ 0 . 7 pN . In that case , an additional ∼6 ms is required to process the different routines of the software . The oligonucleotide 5′-ACA TTG ACC CCT-3′ was radioactively labeled at the 5′ terminus by incubating 100 pmoles of oligonucleotide with 20 units of polynucleotide kinase ( Roche ) and 30 mCi of 32P γ-ATP ( Pharmacia ) for 30 min at 37 °C . The amount of incorporated radioactivity was measured using a TRI-CARD 2100 TR Liquid Scintillation Analyzer . Five pmoles ( 5 , 000 cpm ) of the 12-nucleotides-long 32P 5′-labeled oligonucleotide were added to the VirE2 protein in 50mM NaH2PO4 , pH 8 , 300 mM NaCl , and the reaction was incubated on ice for 1 h . The mixture was then loaded on a native , 10% acrylamide gel and run in 0 . 25× TBE at 100 mV for 2 h at 4 °C . The gel was dried and exposed on a Kodak x-ray film . See Figure 2B . See Figure S7 and [30] for details . To clone VirE2H6 into pCAMBIAmod [32] , the entire open reading frame ( ORF ) of pET-VirE2H6 [8] was amplified by PCR at 43 °C . A BamHI site was added at the 5′ terminus using the primer 5′-CGC GGA TCC TTT AAC TTT AAG AAG GAG ATA TAC-3′ and a PstI site was added to the 3′ terminus using the primer 5′-AAG ACG TCC TCA GTG ATG GTG ATG GTG ATG AAA GC-3′ . The PCR product was cloned into pGEMT ( Promega ) , cut with BamHI and PstI , and cloned into pCAMBIAmod that had been digested with the same enzymes , resulting in pCAMBIA-VirE2H6 . pCAMBIA-VirE2HA was generated by digesting pCAMBIAmod with BamHI and XbaI and inserting the VirE2HA gene extracted from pcDNA3 . 1-VirE2HA ( see below ) with the same enzymes . Cloning of pcDNA3 . 1-VirE2HA was performed using the primers 5′-TCA TGG ATC CAC CAC CAT GGA TCT TTC TGG CAA TGA GAA A-3′ ( adding a BamHI site and the Kozak sequence on the 5′ of VirE2 ) and 5′-ACT CTC TAG ATC AAG CGT AAT CTG GAA CAT CGT ATG GGT AAA AGC TGT TGC TTT GGC T-3′ ( adding an hemaglutinin ( HA ) tag to the 3′ terminus of VirE2 as well as an XbaI site ) were used to generate VirE2HA by PCR amplification of the VirE2 gene using pET- VirE2H6 as a template [8] . The PCR product was cut with BamHI/XbaI and ligated into pcDNA 3 . 1 ( Invitrogen ) cut with the same enzymes . The resulting construct was named pcDNA3 . 1-VirE2HA . For production of transgenic tobacco plants expressing VirE2 or mutants and to prevent expression of VirE2H6 and VirE2HA in Agrobacterium , an intron of potato ST-LSI [33] was inserted into pCAMBIAmod VirE2H6 and VirE2HA as a BamHI/BglII fragment . The resultant plasmids were named pCAMBIAmod VirE2H6-int and VirE2HA-int . The plasmids were subsequently electroporated into electrocompetent Agrobacterium strain GV1301 ( pPM6000 ) cells using a GenePulser ( Biorad ) at 2 . 5 kV , 200 Ω , 25 μFd . Transgenic plants expressing VirE2H6 , VirE2HA were obtained by transforming tobacco ( SR1 ) leaf discs with Agrobacterium GV1301 ( pPM6000 , pCAMBIAmodVirE2H6-int/ VirE2HA-int ) . Control plants were generated by transformation with the empty vector pCAMBIAmod . The selection was performed on Murashig and Skoog ( MS ) medium supplemented with BAP ( 4 μM ) , naphthalene acetic acid ( NAA ) ( 0 . 5 μM ) , cefotaxime ( 500 mg/l ) , timentin ( 150 mg/l ) , and hygromycin ( 20 mg/l ) . Individual plants were regenerated , and five plants from each category were transferred to soil for seed production . WT-VirE2 expressing plants were obtained from the laboratory of Andrew Binns [34] . Seeds from transgenic plants ( VirE2HA , VirE2H6 ) were sterilized and allowed to germinate on MS medium supplemented with hygromycin ( 50 mg/l ) . Fourteen-day-old seedlings were infected with diluted Agrobacterium GV1301 ( pPM6000E , pCAMBIA 2201; Agrobacterium strain where the virE2 gene has been deleted ) , cocultivated for 48 h to an optical density of 1 , followed by extensive washing with MS medium [35] . For the last wash the medium was supplemented with timentin ( 150 mg/l ) . The histochemical GUS staining was performed as described [35] . Virulence was quantified as GUS positive spots per 100 seedlings . ssDNA fragments ( M13 ) were incubated with VirE2 as described in [30] ( Figure 4B ) . Tobacco BY-2 cells were plasmolyzed on MS-agar plates with 0 . 25 M mannitol/sorbitol ( Merck ) for 3 h . DNA of pCAMBIA-VirE2HA and pCAMBIA-GFP was precipitated on 1-μm-diameter gold particles ( Biorad ) . The particles coated with DNA were bombarded on the plasmolyzed BY-2 cells with a PDS-1000/He Biolistic Particle Delivery System ( Biorad ) . VirE2HA protein was transiently coexpressed with green fluorescent protein ( GFP ) after particle bombardment of tobacco BY-2 cells with plasmids pCAMBIA-VirE2HA and pCAMBIA-GFP . GFP was used as a positive marker for transformation . After 16 h recovery , the cells were fixed for 1 h in 3 . 7% paraformaldehyde ( Sigma ) in MSB/Gly buffer ( 50 mM Pipes , pH 6 . 9 , 5 mM EGTA , 1 mM MgCl2 , 2% glycerol ) . The cells were then washed three times with MSB/Gly buffer and deposited on polylysine-coated slides ( polylysine L , Sigma ) . The cell wall was digested for 5 min with the following mix of enzymes from Yakult Honsha ( Pectolyase 0 . 02% , Macerozyme 0 . 1% and Caylase 0 . 3% ) diluted-10 fold in digestion buffer ( 25 mM MES , pH 5 . 5 , 8 mM CaCl2 , and 600 mM Mannitol ) . The cells were permeabilized with 0 . 1% Triton ( Merck ) in PBS ( phosphate-buffered saline ) for 5 min . Unspecific binding of antibody was prevented by incubation of the cells with 5% normal goat serum ( Calbiochem ) . The rat monoclonal anti-HA antibody ( Boehringer ) was diluted 1:100 and the reaction carried out overnight at 4 °C . After washing the cells in PBS , the secondary antibody ( goat anti-rabbit TRITC , Jackson Immuno Research Laboratories ) , was added at 1:30 dilution for 1 h at room temperature . DAPI ( 4′ , 6-diamidino-2-phenylindole , Calbiochem ) , a nucleic acid stain , was added to the cells at 1 mM concentration and incubated for 5 min . Following a PBS wash , fading of the fluorescent signal was minimized by fixing the cells in Vectashield ( Vector Laboratories ) . The cells were observed using a Leica DMRD fluorescence microscope , at 430 nm for DAPI , 488 nm for GFP , and 543 nm for rhodamine . Signals were recorded sequentially using PL APO x63 / 1 . 32 oil / PH3 */ 0 . 17/ D oil immersion objectives equipped with a filter for Nomarski . The VISIOLAB 200 program and a Sony 3CCD color video camera “Power HAD” were used for image processing .
The importation of genetic material into cells is a common and fundamental mechanism occurring in bacterial conjugation , DNA uptake , and Agrobacterium plant infection and is , for instance , responsible for antibiotic resistance spread . Previous studies suggested that this process relied only on the activity of complex molecular machines pumping the single-stranded DNA ( ssDNA ) into the recipient cell . Here , we show that proteins provided by the pathogenic organism and translocated prior to the arrival of ssDNA into the recipient cell also play a fundamental role . These proteins not only bind to ssDNA to protect it but also rearrange ssDNA into a compact helix , thus generating a contractile force that pulls the DNA into the host . Interestingly , the production of mechanical energy occurs solely through the free-energy gain during the binding of VirE2 to ssDNA without the need for an external source of energy , such as nucleotide hydrolysis .
You are an expert at summarizing long articles. Proceed to summarize the following text: Sterol regulatory element binding proteins ( SREBPs ) are a class of basic helix-loop-helix transcription factors that regulate diverse cellular responses in eukaryotes . Adding to the recognized importance of SREBPs in human health , SREBPs in the human fungal pathogens Cryptococcus neoformans and Aspergillus fumigatus are required for fungal virulence and susceptibility to triazole antifungal drugs . To date , the exact mechanism ( s ) behind the role of SREBP in these observed phenotypes is not clear . Here , we report that A . fumigatus SREBP , SrbA , mediates regulation of iron acquisition in response to hypoxia and low iron conditions . To further define SrbA's role in iron acquisition in relation to previously studied fungal regulators of iron metabolism , SreA and HapX , a series of mutants were generated in the ΔsrbA background . These data suggest that SrbA is activated independently of SreA and HapX in response to iron limitation , but that HapX mRNA induction is partially dependent on SrbA . Intriguingly , exogenous addition of high iron or genetic deletion of sreA in the ΔsrbA background was able to partially rescue the hypoxia growth , triazole drug susceptibility , and decrease in ergosterol content phenotypes of ΔsrbA . Thus , we conclude that the fungal SREBP , SrbA , is critical for coordinating genes involved in iron acquisition and ergosterol biosynthesis under hypoxia and low iron conditions found at sites of human fungal infections . These results support a role for SREBP–mediated iron regulation in fungal virulence , and they lay a foundation for further exploration of SREBP's role in iron homeostasis in other eukaryotes . Fungal pathogens face numerous environmental challenges during growth in mammalian hosts that can determine outcomes of host-pathogen interactions . A major factor in host defense against invading fungi is the sequestration of iron , which prevents in vivo fungal growth [1] . Consequently , most fungal pathogens have evolved mechanisms to obtain iron from their hosts and these mechanisms are established fungal virulence attributes [2] , [3] , [4] , [5] , [6] , [7] , [8] . Intriguingly , an association between responses to iron and oxygen limitation emerged from studies in rodents demonstrating increased iron absorption in response to hypoxia [9] . Moreover , hypoxia is known to increase the expression of transferrin , which increases iron availability to host cells under hypoxic stress [10] . The key transcriptional regulator of mammalian responses to hypoxia , hypoxia inducible factor-1 ( HIF ) , has been found to regulate several genes involved in iron metabolism [11] , [12] , [13] , [14] . Thus , an intimate link exists between cellular responses to low oxygen environments and iron availability in eukaryotes . Yet , mechanisms of regulation of this potential link in human pathogenic fungi are largely unknown . Previous results strongly suggest that mechanisms of both iron acquisition and hypoxia adaptation are critical for fungi to cause disease in humans . Strains of the human fungal pathogen Aspergillus fumigatus that no longer make any iron-sequestering siderophores are fully avirulent , while strains deficient in either extracellular or intracellular siderophore production display attenuated virulence [4] , [5] , [6] . Regulation of iron acquisition in A . fumigatus and other fungi that make siderophores is mediated by two key transcription factors SreA and HapX [2] , [15] . Null mutants of SreA display increased siderophore production and as expected remain fully virulent in animal models of invasive pulmonary aspergillosis . Conversely , null mutants of HapX have a reduced ability to produce siderophores and are consequently significantly attenuated in virulence . Recently , a third transcription factor , AcuM , has been hypothesized to repress SreA and transcriptionally induce HapX via transcriptome profiling experiments [16] . Though it is unclear if the effects of AcuM on SreA and HapX are indirect or direct , AcuM null mutants have decreased siderophore production and attenuated virulence [16] . Data from these studies strongly suggest the presence of additional unidentified regulators of iron metabolism in fungi . An appreciation for the involvement of hypoxia in fungal pathogenesis is recent and strongly supported by characterization of fungal sterol regulatory element binding protein ( SREBP ) null mutants that are incapable of growth in hypoxia , attenuated in fungal virulence , and more susceptible to triazole antifungal drugs [17] , [18] , [19] , [20] , [21] , [22] . SREBPs are a unique family of membrane bound basic helix-loop-helix ( bHLH ) transcription factors that mediate a diverse array of biological processes in eukaryotic organisms [23] . In mammals , SREBPs have been observed to regulate cholesterol , lipid , and carbohydrate metabolism whereas in cholesterol auxotrophs such as Drosophila melanogaster and Caenorhabditis elegans SREBPs function to regulate fatty acid biosynthesis and development [24] , [25] , [26] , [27] , [28] . In Schizosaccharomyces pombe and Cryptococcus neoformans , SREBPs transcriptionally regulate genes involved in responses to low oxygen with the ergosterol biosynthesis pathway being an important downstream effector [17] , [18] , [29] , [30] . A preliminary characterization of the A . fumigatus SREBP affected transcriptome adds further support to the conclusion that fungal SREBPs are key transcriptional regulators of ergosterol biosynthesis [19] . Yet , the key SREBP mediated downstream effectors in A . fumigatus remain to be fully elucidated . Discovering the SREBP mediated regulon in A . fumigatus and other human pathogenic fungi is critical for fully understanding the role of this transcriptional regulator in fungal pathogenesis . In this study , we utilized microarray-based transcriptomics and molecular genetics to further define the role of the SREBP SrbA in A . fumigatus . We report that in A . fumigatus SrbA is an unidentified regulator of iron homeostasis . Additionally , we observe that SrbA's role in iron metabolism is intimately linked with SrbA's previously identified role in hypoxia adaptation and triazole drug susceptibility . Together , these results advance our understanding of regulation of fungal iron homeostasis and provide new evidence for understanding the role of fungal SREBPs in fungal virulence , hypoxia adaptation , and antifungal drug susceptibility . Previously , we reported that loss of the SREBP , SrbA , in the human fungal pathogen A . fumigatus resulted in loss of hypoxia growth , increased susceptibility to triazole antifungal drugs , and a significant attenuation in virulence [19] . To better understand the mechanisms of the previously observed SrbA dependent clinically relevant phenotypes , we sought to identify potential SrbA downstream effectors in A . fumigatus . We compared whole genome transcript level profiles of wild-type and the SREBP null mutant , ΔsrbA , in response to hypoxia ( 1% O2 , 5% CO2 , 94% N2 ) . Because ΔsrbA cannot grow in hypoxia , a shift experiment was done whereby both strains were grown in normoxic conditions to the germling stage , then shifted to hypoxia conditioned media for defined time points . Transcriptome profiles at 1 , 2 , and 4 hours post exposure to hypoxia were measured with microarrays and reveal dramatic changes in the fungal transcriptome due to loss of SrbA activity ( Tables S1 , S2 , S3 , S4 , S5 , S6 and Figure 1 ) . At one-hour post exposure to hypoxia , levels of mRNA from 639 genes were reduced ≥2 fold in the absence of SrbA ( 6 . 5% of the genome ) ( Table S1 ) . mRNA from an additional 524 genes was increased ≥2 fold due to absence of SrbA ( 5 . 3% of the genome ) ( Table S2 ) . Thus , upon initial exposure to hypoxia , approximately 12% of A . fumigatus genes are affected by SrbA activity . At 2 hours post-exposure to hypoxia , the number of mRNAs whose abundance decreased ≥2 fold increased to 773 ( Table S3 ) and the number of mRNAs whose abundance increased ≥2 fold increased to 727 ( Table S4 ) . Finally , at 4 hours post-exposure to hypoxia , 602 mRNAs remained transcriptionally decreased ≥2 fold ( Table S5 ) while 667 mRNAs remained ≥2 fold transcriptionally increased ( Table S6 ) . Manual gene ontology analysis as well as Gene set enrichment analysis ( GSEA ) of available GO terms suggested an SrbA dependency for ergosterol biosynthesis , iron acquisition , glycolysis , ribosome biogenesis , and amino acid biosynthesis ( Figure S1 , S2 , S3; Tables S7 , S8 , S9 ) . Taken together , these results suggest that SrbA is a major transcriptional regulator in A . fumigatus that may act as both a positive and negative regulator of transcription . Previous studies in S . pombe , C . neoformans , and A . fumigatus strongly suggested that fungal SREBPs are key regulators of ergosterol biosynthesis . Thus , not surprisingly , levels of mRNAs encoding Erg24 , Erg3 , and Erg25A were all at least 20 fold less abundant at one hour post-exposure to hypoxia in the absence of SrbA ( Figure 1A , Table S1 ) . The levels of mRNA from these genes remained substantially reduced at 2 and 4 hours and confirm our previously reported sterol profiles of the SrbA null mutant that demonstrated a partial block in ergosterol biosynthesis at the level of C4-demethylation [19] . In addition , and in contrast to our previously published analysis of a 24 hour time point transcriptome , levels of mRNA from several key genes encoding enzymes involved in iron homeostasis were found to be reduced at least 6 fold in the absence of SrbA ( Figure 1B , Tables S1 , S3 , S5 ) . The decrease in mRNA of genes associated with iron metabolism suggests that the initial response to hypoxia of A . fumigatus involves transcriptional induction of genes involved in iron acquisition . This result supports previous studies in mammals that demonstrate a tight link between hypoxia adaptation and iron homeostasis . In A . fumigatus , mRNA levels of sidA , an L-ornithine monooxygenase that catalyzes the first step in siderophore biosynthesis were reduced in the absence of SrbA ( Figure 1B and Figure 2 ) . Reduction in sidA mRNA levels would be expected to decrease both extracellular and intracellular siderophore production in A . fumigatus . mRNA from other genes involved in iron acquisition were also less abundant in the absence of SrbA including , the siderophore transporters mirB and sit1 , the high affinity iron transporter ftrA , and the ferrooxidoreductase fetC involved in reductive iron assimilation . We further confirmed the SrbA dependency for transcription of iron associated genes in response to hypoxia utilizing qRT-PCR ( Figure 2 ) . At 1 , 2 , and 4 hours , exposure to hypoxia reduced transcript levels of fetC , sidA , and sit1 in the absence of SrbA . Transcript levels of ftrA were SrbA dependent at 1 hour post-exposure to hypoxia , but increased at 2 and 4 hours via an unknown mechanism . Taken together , these results suggest that SrbA is a critical regulatory factor for iron homeostasis during the initial response to hypoxia . We next asked the question whether SrbA directly or indirectly regulated transcriptional regulation of ergosterol biosynthesis and iron acquisition . Wild-type and ΔsrbA strains were cultivated as for the microarray and qRT-PCR experiments , and at 4 hours post-exposure to hypoxia chromatin immunoprecipitation ( ChIP ) was performed using IgG and polyclonal SrbA ( amino acids 1–275 ) antibodies . Immunoprecipitated DNA was quantified for erg25A , erg11A , sit1 , and sidA using primers targeted to the promoter regions of these genes . Significant enrichment for SrbA binding to the promoters of erg11A , erg25A , and sit1 , was observed indicating that SrbA likely directly binds to the promoters of these genes ( Figure 3 ) . However , no enrichment was observed for sidA indicating that SrbA regulation of this important siderophore biosynthesis gene may be indirect . Taken together , these results strongly suggest that SrbA coordinately regulates ergosterol biosynthesis and iron uptake in response to hypoxia . Because iron is a critical co-factor for enzymes involved in ergosterol biosynthesis , we explored the hypothesis that SrbA is a positive regulator of iron acquisition independent of the known fungal iron regulators SreA and HapX . In order to compare the function of SrbA with that of SreA and HapX , ΔsreA and ΔhapX mutants were generated in CEA10 and ΔsrbA backgrounds as previously described for ATCC46645 [2] , [15] . We then tested the consequences of SrbA-deficiency in the generated strains in submersed liquid cultures under iron replete and iron limiting conditions . In iron replete conditions , ΔsrbA biomass was 54% ( 54 . 0/100 . 0 ) of the wild-type and in iron depleted conditions , ΔsrbA biomass diminished even further to 32% ( 18 . 52/57 . 47 ) of wild-type ( Table 1 ) . Importantly , the ΔsrbA reconstituted strain completely restored wild-type biomass in response to iron depletion indicating that the iron phenotype observed is specifically due to loss of SrbA ( Table 1 ) . Consequently , the −Fe/+Fe biomass ratio decreased from 57% ( 57 . 47/100 . 00 ) for CEA10 to 34% ( 18 . 52/54 . 00 ) for ΔsrbA ( Table 1 ) . Thus , loss of SrbA negatively affects the ability of A . fumigatus to deal with low iron environments supporting the observed transcriptional profiling data . As expected , SreA-deficiency did not affect liquid biomass production during either iron-replete or iron-depleted conditions . Intriguingly , inactivation of SreA in the ΔsrbA strain increased fungal biomass by 47% in iron-depleted conditions and 20% in iron-replete conditions compared to the ΔsrbA strain itself . Consequently , this increased the −Fe/+Fe biomass ratio to 42% ( Table 1 ) . As found previously for A . fumigatus strain ATCC46445 [2] , HapX-deficiency decreased liquid biomass production during iron starvation in A . fumigatus strain CEA10 by about 60% but had no significant effect during iron sufficiency because hapX is mainly expressed during iron starvation [2] . Compared to ΔsrbA , additional inactivation of HapX in ΔsrbA decreased the biomass by 37% in iron depleted conditions but had no effect in iron-replete conditions ( Table 1 ) . Taken together , these data strongly support the hypothesis that SrbA is required for adaptation to iron starvation independent of the known fungal iron metabolism regulators SreA and HapX . We next explored the mechanism behind the detrimental effects of iron starvation on ΔsrbA . Our transcriptome profiling experiments implied a potential role for SrbA regulation of siderophore biosynthesis and uptake . As shown previously for A . fumigatus strain ATCC46445 [4] , [5] , A . fumigatus strain CEA10 produces extracellular TAFC ( triacetylfusarinine C ) exclusively during iron starvation . Intriguingly , SrbA-deficiency decreased TAFC production by 90% compared to wild-type CEA10 in iron starvation conditions ( Figure 4A ) . Similar to previous findings with A . fumigatus strain ATCC46445 [2] , [15] , HapX-deficiency in CEA10 decreased TAFC production during iron starvation by 79% during iron starvation while SreA-deficiency in CEA10 caused a 7% derepression of TAFC production during iron sufficiency ( Figure 4A ) . Compared to ΔsrbA , additional deletion of hapX in ΔsrbA decreased TAFC production by 79% during iron depleted conditions ( Figure 4A ) . In contrast , additional deletion of sreA in ΔsrbA increased TAFC production compared to ΔsrbA by 163% during iron starvation and derepressed TAFC production to 40% of the ΔsreA level during iron sufficiency ( Figure 4A ) . As shown previously for A . fumigatus strain ATCC46445 [4] , [5] , A . fumigatus strain CEA10 accumulates intracellular FC ( ferricrocin ) in the ferri-form during iron sufficiency and about 20 fold higher amounts in the desferri-form during iron starvation ( Figure 4B ) . SrbA-deficiency had no effect of the FC during iron sufficiency . However , SrbA-deficiency decreased the FC content by 71% compared to CEA10 during iron starvation ( Figure 4B ) . Similar to previous findings with A . fumigatus strain ATCC46445 [2] , [15] , HapX-deficiency in CEA10 decreased the FC content during iron starvation by 47% during iron starvation while SreA-deficiency in CEA10 increased the FC content during iron sufficiency 3 . 7 fold ( Figure 4B ) . Compared to ΔsrbA , the FC content in ΔsrbAΔhapX is decreased by 46% during iron starvation ( Figure 4B ) . In contrast , additional deletion of SreA in ΔsrbA increased the FC content compared to ΔsrbA by 19% during iron starvation and 181% during iron sufficiency ( Figure 4B ) . Together , these data indicate that SrbA activates production of extra-and intracellular siderophores independent of SreA and HapX . Moreover , this biochemical data supports the observed transcriptome profile of ΔsrbA that strongly suggests a critical role for SrbA in regulation of siderophore biosynthesis and uptake . However , the mechanism by which SrbA regulates siderophore production remains to be elucidated . As production of extra- and intracellular siderophores plays a crucial role in adaptation to iron starvation [4] , [5] , these data explain , at least in part , the observed growth and morphological defects of ΔsrbA during iron starvation . In further support of SrbA's role as a positive regulator of iron homeostasis , previous genome-wide transcriptome profiling experiments indicated that srbA transcript levels are reduced within 30–60 minutes during a shift from iron starvation to iron sufficiency independent of SreA and HapX [2] , [15] . Thus , we next confirmed that srbA transcript levels are substantially higher during iron starvation compared to iron sufficiency , and that srbA transcript levels are not influenced by inactivation of SreA or HapX ( Figure 5 ) . These results further support the hypothesis that SrbA transcript levels increase under low iron conditions and that this increase is independent of the known transcriptional regulators of iron homeostasis SreA and HapX . Consistent with the transcriptome profile of ΔsrbA upon early exposure to hypoxia and biochemical analysis of siderophore production in the absence of SrbA , inactivation of SrbA reduced mRNA levels of genes involved in siderophore metabolism ( siderophore biosynthetic sidA , TAFC-biosynthetic sidF , and siderophore importer-encoding mirB ) , reductive iron assimilation ( ftrA ) , and iron transcriptional regulation ( hapX ) in iron limited conditions ( Figure 5 ) . All of these genes belong to the SreA regulon [15] . Moreover , SrbA deletion decreased the degree of derepression of these genes in ΔsreA during iron sufficiency ( compare ΔsreA and ΔsrbAΔsreA ) . The reduction of hapX mRNA levels in the absence of SrbA in iron limiting conditions suggests a previous unreported link between SrbA and HapX in iron limiting conditions . Together , these data support a role for SrbA in adaptation to iron starvation and demonstrate that SrbA impacts siderophore biosynthesis at the transcriptional level both in response to hypoxia and iron starvation through an unknown mechanism . Importantly , mRNA levels of genes involved in ergosterol biosynthesis were also found to be more abundant under iron starvation conditions ( Figure 5 ) . Similar to the hypoxia mRNA transcriptome profiling data , this increase in mRNA levels is SrbA dependent ( Figure 5 ) . In further support of SrbA's role in regulating iron metabolism , mRNA levels of the iron center-ergosterol biosynthetic enzymes Erg3 ( C-5 sterol desaturase ) and Erg25 ( C-4 methyl sterol oxidase ) were independent of SreA and HapX ( Figure 5 ) . As mRNA levels from both erg3 and erg25 are also reduced in ΔsrbA in response to hypoxia , these data further support the SrbA mediated link between ergosterol biosynthesis and iron metabolism in response to hypoxia in A . fumigatus . Also of interest , SrbA inactivation decreased mRNA levels of the heme biosynthetic gene hem13 ( encoding coproporphyrinogen III oxidase ) independent of SreA and HapX in iron limited and hypoxia conditions . Importantly , mRNA levels of acoA , which encodes the iron-sulfur cluster-containing aconitase and whose expression is subject to HapX-mediated repression during iron starvation are decreased during iron-replete conditions in ΔsrbA and ΔsrbAΔhapX but not in ΔsrbAΔsreA [2] . These data indicate that SrbA-deficiency decreases cellular iron supply during iron-replete conditions due to reduction of iron uptake . Importantly , this defect can be partially suppressed by derepression of iron uptake via SreA-deficiency ( ΔsrbAΔsreA ) . We next explored the hypothesis that the partial suppression of decreased cellular iron supply in ΔsrbAΔsreA would rescue the clinically relevant phenotypes of ΔsrbA: increased fluconazole susceptibility , inability to grow in hypoxia , and ability to cause invasive pulmonary aspergillosis . E-test mediated fluconazole susceptibility testing confirmed our previously published results that the inherent fluconazole resistance of A . fumigatus CEA10 depends on SrbA activity ( Figure 6 ) . Fluconazole susceptibility in ΔsrbA was consistent in both iron depleted and iron replete conditions . Intriguingly , high iron conditions were able to increase the resistance of ΔsrbA against fluconazole ( high iron MIC = 12 µg/ml compared to an MIC of 1 µg/ml during iron replete or iron starvation ) . Additional deletion of sreA , but not hapX , in ΔsrbA partially rescued fluconazole resistance , and this effect , as expected , was potentiated under high iron conditions . Importantly , deletion of either sreA or hapX alone did not affect fluconazole susceptibility . Taken together , these results suggest that the increase in A . fumigatus fluconazole susceptibility in the absence of SrbA is partially due to loss of iron homeostasis . The direct binding of SrbA to the promoter of erg11A ( also called cyp51A in A . fumigatus ) led us to explore the potential mechanism behind this result . We thus examined transcript levels of erg11A , erg11B ( cyp51B ) , erg25A , and srbA in response to varying levels of iron in wild-type and ΔsrbA . Addition of high iron to either wild-type or ΔsrbA significantly increased erg11A transcript levels ( Figure 7A and 7B ) . As expected from the ChIP experiment , this effect on erg11A transcript was SrbA dependent ( Figure 7A and 7B ) . Of note , erg11A is not contained on the microarray and thus erg11A transcript levels were not previously observed to be SrbA dependent . Consistent with the previous Northern blot experiments , loss of iron stimulated an increase in srbA transcript levels ( Figure 7A ) . However , the effect of iron on erg25A transcripts was minimal , though as observed with the microarray data , erg25A transcript levels are SrbA dependent ( Figure 7A and 7B ) . Next , we examined total ergosterol levels in wild-type , ΔsrbA , and ΔsrbAΔsreA strains ( Figure 7C ) . Addition of high iron was able to increase total ergosterol levels in the ΔsrbA and ΔsrbAΔsreA backgrounds consistent with the increase in erg11A transcript levels . No difference in ergosterol levels was observed between the wild-type and ΔsreA strains ( Figure 7C ) . These data , however , do not rule out the potential for increased enzyme efficiency in the presence of more available iron , or a restoration of membrane fluidity due to increases in ergosterol level that may affect triazole drug uptake . However , these data suggest that loss of iron homeostasis due to absence of SrbA affects ergosterol biosynthesis and triazole drug interactions in A . fumigatus . As high iron levels or deletion of SreA were able to partially rescue the fluconazole susceptibility and decrease in ergosterol content in ΔsrbA , we hypothesized that these effects may also rescue the hypoxia growth defect of ΔsrbA . In further support of a link between hypoxia adaptation and iron homeostasis in A . fumigatus , supplementation of media with high iron concentrations plus inactivation of SreA partially rescues growth of ΔsrbA in hypoxia ( Figure 8 ) . This result can be explained by derepression of iron uptake due to SreA inactivation , which works best in the presence of high iron concentrations . Thus , in the absence of SreA , iron uptake is increased in ΔsrbA . HapX-deficiency had no effect on hypoxic growth and not surprisingly , is not transcriptionally induced in response to hypoxia . These data indicate that the hypoxic growth defect of ΔsrbA is at least partially explained by a defect in iron accumulation . In further support of this conclusion , susceptibility to cobalt chloride in the absence of SrbA is also rescued by further inactivation of SreA ( Figure S4 ) . Thus , the ability of either high iron or loss of SreA activity to partially rescue the fluconazole and hypoxia phenotypes of ΔsrbA strongly suggests that ΔsrbA cells are iron deficient . These results further support an important link between iron homeostasis and ergosterol biosynthesis as mediated by SrbA . As the avirulence phenotype of ΔsrbA is hypothesized to at least partially be the result of its inability to grow in hypoxia , we next tested the ability of ΔsrbAΔsreA to cause disease in a chemotherapeutic murine model of invasive pulmonary aspergillosis . We have previously shown that ΔsrbA is fully avirulent in this murine model of IPA; however , inactivation of SreA in ΔsrbA was not able to rescue virulence in the absence of SrbA ( Figure 9A ) . Histopathological examinations of wild-type , ΔsrbA , and ΔsrbAΔsreA strains revealed significant fungal growth and tissue necrosis in mice infected with CEA10 ( Figure 9B ) . However , as previously reported , a significant reduction in ΔsrbA growth is observed in vivo and further inactivation of SreA did not visibly change the observed histopathology . As iron availability is extremely limited in vivo , and previous results demonstrating that A . fumigatus strains defective in siderophore biosynthesis have attenuated virulence , this result is likely not surprising and does not rule out a role for SrbA mediated iron homeostasis in the avirulence phenotype of ΔsrbA . Additional experiments examining the impact of loss of iron homeostasis in ΔsrbA on A . fumigatus virulence are ongoing . Iron starvation has previously been observed to cause a significant remodeling of the amino acid pool [2] . HapX , which is activated by iron-starvation , affects the amino acid composition during iron starvation but not during iron sufficiency and is crucial for coordination of the production of siderophores and their precursor ornithine . Given SrbA's role in mediating responses to low iron conditions and reduction of siderophore biosynthesis in the absence of SrbA , we next tested the hypothesis that loss of SrbA would also alter the amino acid pool of A . fumigatus . In support of this hypothesis , the transcriptome profile data suggest significant changes in the mRNA levels of genes involved in amino acid biosynthesis in the absence of SrbA upon exposure to hypoxia ( Figure 10 ) . In contrast to HapX-deficiency , SrbA-deficiency dramatically changed the composition of the amino acid pool during both iron-replete and depleted conditions ( Table 2 ) . Similar to HapX-deficiency , SrbA-deficiency decreased the cellular ornithine pool during iron starvation , which indicates together with the decrease in siderophore biosynthesis , that SrbA also plays a role in supply of the siderophore precursor ornithine . Thus , loss of siderophore biosynthesis in ΔsrbA may be due to regulation of critical precursor levels . Understanding the in vivo microenvironment conditions encountered by human pathogenic fungi is a promising line of inquiry for identifying novel therapeutic options for these frequently lethal infections . The importance of iron availability in host pathogen interactions is well established , and its role in invasive pulmonary aspergillosis is no exception . Previous studies have clearly demonstrated a critical role for iron acquisition mechanisms in fungal pathogenesis for A . fumigatus and other human pathogenic fungi [4] , [5] , [7] , [8] , [31] , [32] , [33] , [34] , [35] , [36] , [37] , [38] , [39] , [40] . More recently , it has been hypothesized that adaptation to low oxygen microenvironments during fungal infection may also be a critical virulence attribute of human pathogenic fungi [17] , [18] , [19] , [20] , [22] . Support for this hypothesis partially stems from studies with fungal SREBP null mutants in C . neoformans and A . fumigatus that are incapable of growth in hypoxia and unable to cause lethal disease in murine models of fungal infections [17] , [18] , [19] , [41] , [42] . However , as SREBPs are transcription factors that regulate a significant number of genes in fungi , it is unclear if the hypoxia growth phenotype of fungal SREBP null mutants is the primary factor for loss of virulence in these mutants . Moreover , fungal SREBP mutants display increased susceptibility to the triazole class of antifungal drugs . Thus , several key questions remain regarding the role of SrbA in fungal pathogenesis . Important questions include what is the mechanism behind the inability of SREBP null mutants to grow in hypoxia ? Does this directly correlate with the avirulence of fungal mutants that lack SREBPs ? And what is the mechanism behind the increased susceptibility to triazole drugs in the absence of SREBP ? To begin to answer these potentially clinically relevant questions , we utilized whole-genome transcriptome analysis of A . fumigatus ΔsrbA exposed to hypoxia to identify SrbA downstream effectors . Here , we report that the A . fumigatus SREBP is a key positive regulator of iron homeostasis , particularly with regard to iron acquisition , that is essential for adaptation to hypoxia and low iron microenvironments . Although previous transcriptome profiling experiments with the C . neoformans SREBP null mutant also suggest a potential role for fungal SREBPs in iron acquisition [7] , [17] , here we definitively show that SREBP is required for adaptation to low iron conditions in A . fumigatus . We further observe that ΔsrbA cells are likely iron deficient and this partially explains the hypoxia growth and triazole susceptibility phenotypes of ΔsrbA . Importantly , SrbA's effect on iron homeostasis appears to be primarily independent of the well-studied iron transcriptional regulatory factors HapX and SreA . SreA-deficiency in A . fumigatus and A . nidulans has been observed to partially derepress siderophore production and expression of respective genes involved in iron acquisition in iron replete conditions [15] , [43] . Importantly , this result strongly suggested the existence of additional regulatory mechanisms involved in iron homeostasis in A . fumigatus . Next , the transcription factor HapX was demonstrated to be required not only for repression of iron-consuming pathways but also for activation of siderophore biosynthesis and uptake during iron starvation in A . nidulans and A . fumigatus [2] , [44] . SreA and HapX are interconnected by a negative transcriptional feed back loop and simultaneous inactivation has been shown to be synthetically lethal in A . nidulans and A . fumigatus [2] , [44] . Recently , the transcription factor AcuM , that is required for gluconeogenesis , was found to also activate siderophore biosynthesis most likely via repression of SreA in A . fumigatus but not A . nidulans [16] . Here we present data that strongly suggest that SrbA is another critical activator of high-affinity iron acquisition systems in A . fumigatus including the siderophore system and reductive iron assimilation [45] . Figure 11 depicts a proposed model linking SreA , HapX , and SrbA in regulation of iron acquisition and ergosterol biosynthesis . Clearly , additional studies are needed to definitively define the relationship between these three important transcriptional regulators of iron homeostasis . Future studies will also seek to incorporate AcuM into our model . Importantly , in response to hypoxia , our microarray data did not detect transcript changes in either HapX or AcuM in the SrbA null mutant . However , in iron depleted conditions , HapX transcript was clearly reduced in ΔsrbA , which indicates that SrbA may directly or indirectly regulate HapX transcript levels under these conditions . Deletion of HapX in ΔsrbA did increase the magnitude of reduction in siderophore levels , further suggesting a possible link between these two transcription factors that remains to be fully elucidated . Defining a regulatory role for SrbA in iron acquisition is consistent with previous reports in other organisms that have linked SREBPs with regulation of sterol biosynthesis and adaptation to hypoxia . Previous studies have suggested a tight link between iron , oxygen and ergosterol biosynthesis in response to hypoxia in yeast . For example , in the model yeast S . cerevisiae , low iron conditions decrease the activity of the C4-sterol demethylase Erg25 , and moreover , sterol synthesis in this organism requires heme [46] . While S . cerevisiae lacks an SREBP ortholog , S . pombe and C . neoformans Sre1 and A . fumigatus SrbA SREBPs appear to be key regulators of Erg25 and sterol biosynthesis [19] , [30] , [47] ( Figure 1B , Figure 2 , and Figure 5 ) . Our A . fumigatus hypoxia transcriptome profiling data are also in agreement with similar studies in S . pombe and C . neoformans that demonstrate an increase in transcripts associated with heme , sterol biosynthesis , and iron uptake in response to hypoxia [17] , [42] , [48] . A recent proteomic analysis of A . fumigatus grown in a chemostat culture under hypoxia demonstrated that the cellular contents of heme and iron substantially increase in these conditions [49] . Thus , taken together , our results here and the results of prior seminal studies in yeast establish a tight link between iron , oxygen , ergosterol biosynthesis and fungal responses to hypoxia , which are mediated in part by SREBPs . Further support for this conclusion comes from our results demonstrating that addition of high iron concentrations to ΔsrbA , or derepression of iron uptake by simulataneous deletion of SreA , is able to partially rescue the triazole susceptibility and hypoxia growth phenotypes of this fungal SREBP null mutant . As a major goal of our study was to better understand the mechanisms behind the clinically relevant antifungal drug and virulence phenotypes of the SrbA null mutant , these results are particularly significant . An important question is how increased iron availability rescues these important ΔsrbA phenotypes . To this end , the observed increase in total ergosterol levels in ΔsrbA and ΔsrbAΔsreA strains in high iron conditions suggest a direct SREBP mediated link between cellular iron levels and ergosterol biosynthesis . This was also reflected in the SrbA dependent decrease in erg11A ( cyp51A ) transcript levels that could also be partially rescued by high iron . This result may explain , at least partially , the restoration of fluconazole resistance and hypoxia growth of ΔsrbA under high iron conditions . It is important to note that A . fumigatus contains 2 functional 14α-demethylases ( Erg11A/Cyp51A and Erg11B/Cyp51B ) [50] , [51] . Loss of Erg11A but not Erg11B function results in increased fluconazole susceptibility . Moreover , intriguingly , it was recently observed that fluconazole preferentially binds Erg11B , thus likely explaining A . fumigatus's inherent resistance to fluconazole [52] . In Candida albicans , a link between iron availability and fluconazole susceptibility has been suggested [53] . The authors observed a 30% reduction in ergosterol levels in low iron conditions and speculate that the increased fluconazole susceptibility in these conditions was due to a subsequent increase in membrane fluidity [53] . Thus , the partial rescue of fluconazole resistance in A . fumigatus ΔsrbA by high iron may in part be due to a reduction of membrane fluidity . In support of this hypothesis , ergosterol levels in ΔsrbA are approximately 50% less than wild-type , and increases in exogenous iron partially rescue this defect , which in theory could decrease membrane fluidity . How iron increases ergosterol levels is unknown , but it could be argued that the increased iron levels improve the efficiency of ergosterol biosynthetic enzymes whose levels appear to be reduced in the absence of SrbA . Both Erg11A and Erg25 require iron as a co-factor for their enzymatic functions . Thus , the observed increases in erg11A transcript levels in the presence of high iron could be due to a positive feedback loop activated by an increase in sterol intermediates that result from increased enzyme efficiency . With regard to the potential link between hypoxia growth and fungal virulence , high iron conditions or concomitant inactivation of SreA could partially rescue the hypoxia growth phenotype of ΔsrbA , but not fungal virulence ( Figure 9 ) . Derepression of siderophore biosynthesis and iron uptake in ΔsrbA was not dramatic enough to rescue the virulence defect of ΔsrbA leaving the exact mechanism of SrbA's role in fungal virulence undefined . However , given that iron is a major limiting micronutrient in vivo , and that the effect of high iron on ΔsrbA growth was modest , this result is not surprising . As A . fumigatus mutants that lack siderophore biosynthesis also have attenuated virulence in vivo , it seems clear that SrbA's role in siderophore biosynthesis and iron uptake is at least partially related to the inability of ΔsrbA to cause lethal disease . Attempts to fully restore hypoxia growth of ΔsrbA via genetic manipulation of iron homeostasis and ergosterol biosynthesis pathways are currently underway . In conclusion , our data suggest a new role for SREBPs in linking hypoxia adaptation , iron acquisition and ergosterol biosynthesis in fungi . We believe that untangling the web of SrbA regulated effectors will lead to a better understanding of SrbA's role in fungal pathogenesis and triazole drug susceptibility , which should provide a clearer picture regarding the potential of fungal SREBP modulation as a clinical therapeutic for human disesases caused by fungi . Thus , future studies will continue to seek to elucidate the genetic regulatory network mediated by SrbA in A . fumigatus and its relationship to fungal virulence and triazole drug interactions . It might also be intriguing to determine the extent to which SREBPs in other eukaryotic organisms are involved in iron homeostasis mechanisms and how this potential regulation is linked with sterol biosynthesis homeostasis especially in hypoxic stress environments . A . fumigatus strains were grown at 37°C in Aspergillus minimal medium ( AMM ) according to Pontecorvo et al . [54] containing 1% glucose as carbon source and 20 mM glutamine as nitrogen source or glucose minimal medium ( GMM ) with 1% glucose as carbon source as previously described [19] . Iron-repleted media ( +Fe ) were supplemented with 30 µM FeSO4 and high iron media contained 1 . 5 mM , 3 . 0 mM , 5 mM or 10 mM FeSO4 , respectively . Media used and concentrations of key elements are denoted according to the respective experiments . For iron depleted conditions ( −Fe ) addition of iron was omitted . For hypoxic conditions , 13 . 45 g AneroGen™ was used or an INVIVO2 Hypoxia Chamber ( Ruskinn ) set at 1% O2 , 5% CO2 , 94% N2 . For liquid growth assays , 108 conidia were inoculated in 200 ml minimal medium . Standard molecular techniques were performed using the pGEM-T vector system ( Promega ) and the bacterial strain Escherichia coli DH5α cultivated in LB medium ( 1% bacto-tryptone , 0 . 5% yeast extract , 1% NaCl , pH 7 . 5 ) as we have previously described [2] , [15] . RNA was isolated using TRI reagent ( Sigma-Aldrich ) . 10 µg of total RNA were used for electrophoresis on 1 . 2% agarose −2 . 2 M formaldehyde gels and blotted onto Hybond N membranes ( Amersham Biosciences ) . Probes used in this study were generated by PCR with the digoxigenin labeling system ( Roche Molecular Biochemicals ) ; Oligonucleotides used for Northern analysis are provided in Dataset S3 . Deletion of sreA and hapX in CEA10 or ΔsrbA backgrounds was carried out as described previously for A . fumigatus ATCC46645 using the same deletion constructs [2] , [15] . Oligonucleotides used for deletions are provided in Dataset S4 . Isolation and analysis of extra- and intracellular siderophores from culture supernatants and cellular extracts , respectively , was carried out as described previously [58] , [59] . Quantification of free amino acid pools was carried out as described previously [60] . E-test strips ( AB bioMérieux ) , plastic strips impregnated with a gradient of fluconazole were used per manufacturers' instructions . Each strip was placed onto a AMM agar plate without iron or supplemented with 30 µM or 10 mM FeSO4 containing a lawn of conidia of the respective strain and growth inhibition was measured after 24 and 48 h by direct observation of the plates at 37°C . No difference in results was observed between 24 and 48 hours . Total ergosterol content was measured as previously described [61] . Total ergosterol content results are the mean and standard deviation from 2 biological replicates with 6 total technical replicates for each strain . 6 to 8 weeks old outbred CD-1 mice were immunosuppressed with intraperitoneal ( i . p . ) injections of cyclophosphamide at 150 mg/kg 2 days prior to inoculation and 40 mg/kg Kenalog injected subcutaneously ( s . c . ) 1 day prior to inoculation . Repeat injections were given on day 3 post inoculation with cyclophosphamide ( 150 mg/kg i . p . ) and on day 6 post inoculation with Kenalog ( 40 mg/kg s . c . ) . Mice were housed six per cage and had access to food and water ad libitum . Twelve mice per A . fumigatus strain ( CEA10 and ΔsrbAΔsreA ) were inoculated intranasally with 1×106 conidia/40 µl following brief isofluorane inhalation . Mock control mice were inoculated with sterile 0 . 01% Tween 80 . Mice were monitored twice daily over a time period of 14 days . Any animals showing distress were immediately sacrificed and recorded as deaths within 24 hrs . No mock infected animals perished during the time course of the experiment . All experiments were approved by the Montana State University IACUC and adhere to NIH policies on animal welfare . This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . The animal experimental protocol was approved by the Institutional Animal Care and Use Committee ( IACUC ) at Montana State University ( Federal-Wide Assurance Number: A3637-01 ) . For histopathology , five CD-1 mice per A . fumigatus strain ( CEA10 , ΔsrbA , ΔsrbAΔsreA ) were immunosuppressed and inoculated as described above . On day 4 post A . fumigatus challenge , mice were sacrificed by pentobarbital anesthesia ( 100 µg/g body weight ) followed by exsanguinations . Lungs were removed immediately , fixed in 10% phosphate-buffered formalin , embedded in paraffin , sectioned at 5 µm , and stained with hematoxylin , and eosin ( H&E ) or Gomori methenamine silver ( GMS ) by using standard histological techniques . Microscopic examinations were performed on a Nikon Eclipse 80i microscope and imaging system ( Nikon Instruments Inc . , Melville , NY , USA ) .
Advances in medical technologies over the past several years have led to an increasing population of patients susceptible to fungal infections . Despite the immunocompromised condition of most patients that acquire these infections , the majority are caused by three fungi: Candida albicans , Cryptococcus neoformans , and Aspergillus fumigatus . Of these , A . fumigatus is least studied , and the ability of this fungus to cause lethal disease in these patients needs more examination . We previously identified a transcription factor in the sterol-regulatory element binding protein family , SrbA , in this pathogenic mold that is critical for virulence and susceptibility to triazole antifungal drugs . The mechanism by which SrbA mediates these clinically relevant phenotypes is unclear . Here , we discover that SrbA is critical for regulation of iron metabolism , particularly through regulation of siderophore production and uptake . We find that A . fumigatus requires iron uptake during the initial phases of adaptation to hypoxic microenvironments and that restoration of iron uptake in the srbA null mutant is able to partially restore the hypoxia growth defect and triazole susceptibility of this mutant . Taken together , our results identify a new role for this important fungal SREBP and give new insights into the clinically relevant roles of SrbA .
You are an expert at summarizing long articles. Proceed to summarize the following text: Severe fever with thrombocytopenia syndrome ( SFTS ) is a tick-borne infectious disease with a high case fatality rate , and is caused by the SFTS virus ( SFTSV ) . SFTS is endemic to China , South Korea , and Japan . The viral RNA level in sera of patients with SFTS is known to be strongly associated with outcomes . Virological SFTS diagnosis with high sensitivity and specificity are required in disease endemic areas . We generated novel monoclonal antibodies ( MAbs ) against the SFTSV nucleocapsid ( N ) protein and developed a sandwich antigen ( Ag ) -capture enzyme-linked immunosorbent assay ( ELISA ) for the detection of N protein of SFTSV using MAb and polyclonal antibody as capture and detection antibodies , respectively . The Ag-capture system was capable of detecting at least 350–1220 TCID50/100 μl/well from the culture supernatants of various SFTSV strains . The efficacy of the Ag-capture ELISA in SFTS diagnosis was evaluated using serum samples collected from patients suspected of having SFTS in Japan . All 24 serum samples ( 100% ) containing high copy numbers of viral RNA ( >105 copies/ml ) showed a positive reaction in the Ag-capture ELISA , whereas 12 out of 15 serum samples ( 80% ) containing low copy numbers of viral RNA ( <105 copies/ml ) showed a negative reaction in the Ag-capture ELISA . Among these Ag-capture ELISA-negative 12 samples , 9 ( 75% ) were positive for IgG antibodies against SFTSV . The newly developed Ag-capture ELISA is useful for SFTS diagnosis in acute phase patients with high levels of viremia . Between 2007 and 2010 , a severe febrile illness associated with gastrointestinal symptoms , thrombocytopenia , and leukocytopenia caused by an unknown etiological agent was reported in rural areas of Hubei and Henan provinces in Central China [1] . The case-fatality rate of the disease was reported to be between 12%–30% at that time . The disease was named severe fever with thrombocytopenia syndrome ( SFTS ) , or fever , thrombocytopenia and leukopenia syndrome ( FTLS ) [1 , 2] . A novel phlebovirus , termed SFTS virus ( SFTSV and also known as Huaiyangshan virus or Henan Fever Virus ) , has been identified as the causative agent of the disease [1 , 2 , 3] . SFTSV has been detected in two tick species ( Haemaphysalis longicornis and Rhipicephalus microplus ) in epidemic areas , suggesting that these ticks are the most likely vectors for transmission of the virus to humans [1 , 3] . SFTSV antibodies were detected at various rates in goats , cattle , sheep , pigs , dogs , and chickens [4 , 5 , 6 , 7 , 8 , 9] , indicating that these animals were infected with SFTSV . There are no reports describing that the virus causes disease in these animals , suggesting that these animals and some species of ticks are the reservoirs of SFTSV . SFTS is endemic to Japan and South Korea [10 , 11] . SFTS patients show abrupt onset of fever with gastrointestinal tract symptoms in the early phase . Most patients have marked thrombocytopenia and leukocytopenia at this stage . Later stages of the syndrome are characterized by a progressive multiple organ failure in fatal cases or a self-limiting process in survivors [12] . The level of viral RNA in patient sera correlates to the clinical outcome . In fatal cases , viremia increases to 109 viral copies per mL . In contrast , the convalescent stage is characterized by decreasing levels of viremia and normalization of clinical laboratory parameters [13 , 14 , 15] . SFTSV is classified into the genus Phlebovirus , family Bunyaviridae . Tick-borne phleboviruses ( TBPVs ) including SFTSV are globally distributed . TBPVs closely related to SFTSV , such as Heartland virus , Malsoor virus , and Hunter Island Group viruses , have been discovered [16 , 17 , 18] . Phylogenetic and serological studies revealed that TBPVs are classified into four distinct groups , Uukuniemi group , Bhanja group , SFTS/Heartland virus group , and Kaisodi group [19 , 20] . SFTSV is classified into the SFTS/Heartland virus group . SFTSV has 3 segmented , single-stranded RNA genomes and is composed of large ( L ) , medium ( M ) , and small ( S ) segments . The L , M , and S segments encode an RNA-dependent RNA polymerase , a precursor of glycoproteins ( Gn and Gc ) , a nucleocapsid ( N ) protein and a nonstructural ( NS ) protein using an ambisense coding strategy , respectively [1] . The N protein is highly immunogenic and conserved among all isolates in each of the phleboviruses [21 , 22] . Therefore , N protein is often selected as a target of antigen ( Ag ) and antibody detection [23 , 24 , 25] . SFTS and other infectious diseases are difficult to diagnose clinically without microbiological tests , particularly when symptoms are non-specific . Hence , laboratory tests are necessary for SFTS diagnosis . Several genome amplification-based methods for SFTS diagnosis have been reported e . g . , conventional reverse transcription-PCR ( RT-PCR ) , quantitative RT-PCR , reverse transcription-loop-mediated isothermal amplification assay ( RT-LAMP ) , and reverse transcription-cross-priming amplification coupled with vertical flow visualization [2 , 13 , 26 , 27 , 28] . However , genome amplification techniques are limited by their requirement of expensive equipment and technical expertise . Methods for the detection of viral Ags using an Ag-capture sandwich ELISA have been previously described , and the sensitivity of this assay is comparable to that of RT-PCR for the detection of Lassa virus and filoviruses [23 , 24 , 29 , 30 , 31 , 32 , 33] . The assay is highly accurate in identifying viral Ags in a rapid and robust manner; additionally , it has been accepted as a useful method for diagnosis during the acute phase of viral infections . To our knowledge , an Ag-capture sandwich ELISA has not yet been developed for SFTS . In this study , mouse MAbs against SFTSV N protein were generated and characterized . Furthermore , Ag-capture ELISA for detection of SFTSV using the MAb was developed , and its efficacy in SFTS diagnosis was evaluated using sera collected from patients with SFTS . All samples were collected as part of public health diagnostic activities for SFTS , were pre-existing relative to the start of the study , and were examined as anonymous samples . All protocols and procedures were approved by the research ethics committee of the National Institute of Infectious Diseases for the use of human subjects ( no . 531 ) . The experiments with animals were performed in strict accordance with the Animal Experimentation Guidelines of the National Institute of Infectious Diseases . The protocol of experiments for mice and rabbits were approved by the Institutional Animal Care and Use Committee of the National Institute of Infectious Diseases ( Permit number: 112116 and 111124 , respectively ) . Mouse myeloma cells , P3X63Ag8 . 653 , obtained from the American Type Culture Collection ( Manassas , VA ) , were maintained in RPMI 1640 medium ( Sigma-Aldrich , St . Louis , MO ) supplemented with 10% heat-inactivated fetal bovine serum ( FBS ) and kanamycin sulfate ( Life Technologies , Carlsbad , CA ) . Hybridomas were maintained in Growth Medium E ( Stem Cell Technologies , Vancouver , Canada ) , RPMI 1640 medium supplemented with 10% FBS and kanamycin sulfate , or KBM-343 medium ( Kohjin Bio Co . , Ltd . , Saitama , Japan ) supplemented with antibiotics . BTI-TN-5B1-4 ( Tn5 , High Five; Life Technologies ) insect cells were maintained in TC100 ( Life Technologies ) supplemented with 10% FBS , 2% tryptose phosphate broth ( Difco , Detroit , MI ) , and kanamycin sulfate . Huh7 , Vero , and Vero E6 cells , obtained from the American Type Culture Collection , were maintained in DMEM ( Sigma-Aldrich ) supplemented with 5% FBS and kanamycin sulfate . SFTSV strains YG1 , SPL004 , and SPL010 isolated from serum samples of Japanese patients with SFTS were used [11] . SFTSV strain HB29 was kindly provided by De-Xin Li and MiFang Liang , Chinese Center for Disease Control and Prevention , Beijing , People’s Republic of China . As a negative control antigen , a supernatant of Vero E6 cells infected with Rift Valley fever virus ( RVFV ) strain MP-12 was used [23] . Experiments using infectious SFTSV and RVFV were conducted in a biosafety level ( BSL ) -3 laboratory . Forecariah virus ( FORV ) and Palma virus ( PALV ) , which were kindly gifted from Robert Tesh , University of Texas Medical Branch , USA , were handled in BSL-2 . The infectious dose of the SFTSV and RVFV stock solutions was determined by calculating the 50% tissue culture infectious dose ( TCID50 ) as described previously [11 , 13 , 34] . Preliminary experiments indicated that treatment of sera containing SFTSV ( 107 TCID50/ml ) with 1% triton X-100 in combination with UV-irradiation ( 312 nm , 2 . 5 mW/cm2 ) on a trans-illuminator for 10 min caused complete loss of viral infectivity in cells . Therefore , viruses used for Ag-capture ELISA were treated with UV-irradiation on a trans-illuminator for 10 min and followed by 1% Triton X-100 for the destruction of the virus particle . We asked medical personnel in Japan to inform us on a voluntary basis if they had seen any patients with symptoms similar to those of SFTS , as reported previously [11] . Through the courtesy of prefectural and municipal public health institutes , 63 serum samples were collected from 55 acute phase patients suspected of SFTS in Japan . Viral gene detection by the qRT-PCR and viral antibody detection by IgG ELISA and/or IFA were conducted to diagnose SFTS . From 55 patients , 34 of these were diagnosed as having SFTSV . Serum samples obtained from 18 healthy donors were used to establish the cut-off value of the IgG ELISA . Serum samples used for IgG ELISA were inactivated under the UV light in the biosafety cabinet for 1 h . Serum samples used for Ag-capture ELISA were treated with 1% Triton X-100 for the destruction of the virus particle followed by an UV-irradiation on a trans-illuminator for 10 min . The recombinant baculovirus , Ac-His-SFTSV-N expressing a histidine ( His ) -tagged SFTSV recombinant N ( rN ) protein at C-terminal , was generated as described previously [23 , 35 , 36] . Briefly , the cDNA encoding the N protein of SFTSV strain HB29 ( nucleotide position 43–780 of segment S , GenBank accession No . NC_018137 ) was artificially synthesized ( GeneScript , Piscataway , NJ ) and then was ligated into the BamHI sites upstream of the 8-His tag coding sequence of the transfer vector pAcYM1 [37] . Tn5 cells were transfected with mixtures of the transfer vector pAcYM1-SFTSV-N and BD BaculoGold Linearized Baculovirus DNA ( BD Biosciences , San Jose , CA ) according to the manufacturer’s instructions with the procedures described by Kitts et al . [38] but with modification by Matsuura et al . [37] . A baculovirus ( Ac-ΔP ) , which lacks polyhedrin expression , was used as a negative control virus . SFTSV rN protein [>75% purity as determined by ImageJ analysis ( http://rsbweb . nih . gov/ij/ ) of sodium dodecyl sulfate polyacrylamide gel electrophoresis] was generated as previously described [23 , 35 , 36] . Briefly , Tn5 cells infected with Ac-His-SFTSV-N were incubated at 27°C for 72 h . The cells were then washed three times with phosphate-buffered saline ( PBS ) solution . The cells were lysed in PBS solution containing 1% Nonidet P-40 ( NP-40 ) and sonicated . After the cell lysates were centrifuged at 17 , 800 × g for 10 min at 4°C , the supernatant fractions were collected as a source of SFTSV rN protein for purification . SFTSV rN protein was purified by Ni2+-nitrilotriacetic acid affinity chromatography ( Qiagen , Hilden , Germany ) according to the manufacturer’s instructions . RVFV rN protein was expressed and purified as described previously [23] . The histidine-tag was not removed from the rN protein . The concentration of the purified SFTSV and RVFV rN proteins were determined by the Pierce BCA Protein Assay Reagent ( Life Technologies ) . Antigens were aliquoted and stored at −80°C until use . BALB/c mice were immunized subcutaneously twice with the purified SFTSV rN protein emulsified in TiterMAX Gold ( TiterMax USA , Inc . , Norcross , GA , USA ) . Hybridomas were produced by fusion of myeloma cells with the splenic cells , obtained 4 days after the last immunization , using ClonaCell-HY Hybridoma Kit ( Stem Cell Technologies ) according to the manufacturer’s instructions . The culture supernatants of hybridoma cells were screened for the presence of antibodies against SFTSV antigen by IgG ELISA as described below . Positive hybridoma cells were cloned by limiting dilution . The isotypes of the MAbs were determined using Mouse Monoclonal Antibody Isotyping Kit ( AbD Serotec , Kidlington , UK ) . The MAbs were purified from mouse ascitic fluid ( Unitech Co . Ltd . , Chiba , Japan ) or from the culture supernatant by protein G column chromatography ( MAbTrap Kit , GE Healthcare UK Ltd . , Buckinghamshire , UK ) according to the manufacturer’s instructions . The concentration of each purified MAb was determined using the Pierce BCA Protein Assay Reagent . Two hybridoma clones ( designated as 2D11 and 9D3 ) producing MAbs reactive to SFTSV N protein were obtained . MAb 9D3 and MAb 2D11 were isotypes of IgG1 and IgG2a , respectively . The light chain of these MAbs was κ-type . Polyclonal antibodies to each of the rN proteins of SFTSV and RVFV were raised by immunization of rabbits with the respective rN protein [11 , 23] . Polyclonal antibodies to FORV and PALV were produced by infection of rabbits with FORV and PALV , respectively . Rabbit sera were obtained 7 days after infection . The experiments with animals were performed in strict accordance with the Animal Experimentation Guidelines of the National Institute of Infectious Diseases . The antigens of SFTSV strain YG1 , FORV , PALV , and RVFV were prepared for IFA as previously described [39] . Briefly , Vero cells infected with each virus ( MOI = 0 . 1 ) were cultured , harvested by trypsinization , washed with PBS , and mixed with parent uninfected cells at a ratio of 1:3 . The cells were spotted on to 14-well HT-coated slide glasses ( AR Brown Co . , Ltd . , Tokyo , Japan ) , air dried , and fixed with a mixture of methanol and acetone [1:1 ( v/v ) ] . These IFA antigen slides were stored at -80°C until use . They were thawed and dried immediately prior to use . The IFA was performed by diluting MAbs at the concentration of 1 ng/μl with PBS and were placed on the slides . As a positive control , rabbit sera diluted with PBS at 1:1 , 000 were also placed on the slides . The slides were incubated under humidified conditions at 37°C for 1 h . After washing with PBS , the slides were treated with Alexa Fluor 488 conjugated goat anti-mouse IgG ( H + L ) antibody ( Life Technologies ) or Alexa Fluor 488 conjugated goat anti-rabbit IgG ( H + L ) antibody ( Life Technologies ) diluted with PBS at 1:400 . The slides were incubated under humidified conditions at 37°C for 1 h . After washing , the slides were examined for antigen staining under a fluorescent microscope ( Olympus , Tokyo , Japan ) [11 , 39] . Immunohistochemical analysis was performed as previously described [11] . The mouse MAbs 9D3 and 2D11 were used in the immunohistochemical analysis as the primary antibodies . Lymph nodes of necrotizing lymphadenitis without SFTSV infection were used as negative controls for tissue specimens . The IgG ELISA was performed as previously described , except for antigen preparation [32 , 33 , 35 , 36] . Antigen preparation for IgG ELISA was performed by infecting Huh7 cells with SFTSV strain HB29 ( MOI = 0 . 1 ) and incubated at 37°C for 48 h . The cells were collected and washed with PBS , and then lysed with PBS solution containing 1% NP-40 . The cell lysates were centrifuged at 8 , 000 rpm for 10 min at 4°C , and the supernatant fraction was collected as a source of SFTSV antigen for IgG ELISA . Huh7 cell lysates without infection were treated in the same way as that for SFTSV antigen preparation and were used as a negative control antigen . Nunc-Immuno Plates ( Thermo Fisher Scientific Inc . , Waltham , MA ) were coated with a pre-determined optimal quantity of Huh7 cell lysates prepared from SFTSV-infected or uninfected cells diluted with PBS at 1:800 and incubated at 4°C overnight . The following procedure was performed in the same way as described previously [32] . The cut-off value was set as the average value of the control sera ( healthy donor sera ) plus three times standard deviation ( SD; mean + 3×SD ) . The sample was considered positive if it yielded an OD405 value above the cut-off value . The Ag-capture ELISA was performed as previously described [23 , 24 , 29] . Nunc-Immuno plates were coated with 100 ng of capture MAbs ( 9D3 or 2D11 ) in 100 μl of PBS at 4°C overnight , and then the wells were incubated with a blocking reagent . After removal of the blocking solution , a series of samples ( 100 μl/well ) diluted with PBST-M was added and incubated for 2 h at RT . After the plates were washed , 100 μl of the rabbit anti-SFTSV rN protein sera diluted 1:1 , 000 with PBST-M was added to each well , followed by incubation for 2 h at RT . After washing , HRP-conjugated goat anti-rabbit IgG antibody ( Life Technologies ) diluted 1:1 , 000 with PBST-M were added to each well and incubated for 2 h at RT . After further washing , 100 μl of ABTS [2 , 2azinobis ( 3-ethylbenzthiazolinesulfonic acid ) ] substrate solution ( Roche Applied Science , Penzberg , Germany ) was added and incubated for 30 min at RT . The optical density at 405 nm ( OD405 ) was measured against a reference of 490 nm using a microplate reader ( Model 680 Microplate Reader; Bio-Rad Laboratories Inc . , Hercules , CA ) . The adjusted OD405 value was calculated by subtracting the OD405 value of the negative antigen-coated wells from that of the corresponding wells . The cut-off value was set at the average value of the control sera ( antigen free ) plus three times the standard deviation ( SD; mean + 3×SD ) . The sample , which yielded an OD405 value above the cut-off value , was thus considered positive . Protein or viral quantities detected per 100 μL reaction in 96-well micro-plates in the Ag-capture ELISA were presented as “/100 μL/well . ” The qRT-PCR method using the qRT-PCR primer and probe sets targeted to N protein or glycoprotein genes was performed as described previously [13] . Genome copies obtained from qPCR assays were presented as “/mL” of serum samples . Unpaired t-test with Welch's correction was used to determine significant differences in the data using the GraphPad Prism 6 software program ( GraphPad software , La Jolla , CA ) . A significant difference was considered to be present for any p value <0 . 05 . Novel MAbs ( 9D3 and 2D11 ) against SFTSV N were generated in this study . SFTSV N protein characterized by a diffuse granular cytoplasmic staining was detected by these MAbs through indirect immunofluorescence ( IFA ) for SFTSV infected Vero cells , but was not detected in Rift Valley fever virus ( RVFV ) infected cells ( Fig 1A ) . Since the serologic relationships between SFTSV and Bhanja group virus , including Forecariah virus ( FORV ) and Palma virus ( PALV ) , have been demonstrated [19] , we also examined the cross-reactivity of MAbs to these phleboviruses . As shown Fig 1A , both MAbs did not react to FORV and PALV . SFTSV antigens were detected in the lymph node specimens obtained from patients with SFTS clearly through immunohistochemistry ( IHC ) staining using the MAbs , but not in that of the patients without SFTS ( Fig 1B ) . The minimum amounts of SFTSV rN protein detected in the Ag-capture ELISA with MAb 2D11 and MAb 9D3 were 40 pg and 10 pg /100 μL/well , respectively ( Fig 2A ) , while levels of up to 2 . 6 ng of RVFV rN protein were not detected ( Fig 2A ) . The Ag-capture ELISA using MAb 9D3 was more sensitive in detection of SFTSV rN protein than the MAb 2D11 . The Ag-capture ELISA using both MAbs ( 9D3 and 2D11 ) as capture antibody was less sensitive than using MAb 9D3 alone ( S1 Fig ) . Therefore , the MAb 9D3-based Ag-capture ELISA was selected for further experiments . Four SFTSV strains , including a Chinese strain ( HB29 ) that we tested were detected in the Ag-capture ELISA . The minimum levels of SFTSV detected in the assay were 1 , 100 , 1 , 200 , 350 , and 540 TCID50 /100 μL/well for SFTSV strains HB29 , YG1 , SPL010 , and SPL004 , respectively ( Fig 2B ) . Because 1 . 0 TCID50 of SFTSV corresponds to approximately 15 . 4 copies of the SFTSV genome [13] , the sera containing theoretical value of more than 5 , 000 copies of the SFTSV genome/100 μL/well could be used for Ag detection in this assay . In contrast , RVFV antigens prepared from virus infected culture supernatants were not detected in this assay . In order to evaluate the efficacy of the Ag-capture ELISA in SFTS diagnosis , these systems were tested using acute phase sera collected from patients suspected of having SFTS . In a total of 63 serum samples , 24 samples were negative by qRT-PCR , and they were also negative for IgG antibodies to SFTSV determined by IgG ELISA and IFA . The patients , whose sera were negative for virus genome by qRT-PCR and IgG antibodies to SFTSV , were confirmed to be patients without SFTS . In a total of 63 serum samples , 27 samples showed a positive reaction in the Ag-capture ELISA ( Table 1 ) . Thirty-nine samples including all the Ag-capture ELISA-positive samples were SFTSV genome positive in the qRT-PCR . All 24 samples containing SFTSV genome copy numbers higher than 105 copies/ml showed a positive reaction in the Ag-capture ELISA , while only 3 of 15 SFTS-genome positive samples with the viral RNA copy numbers of less the 105 copies/ml had a positive reaction in the assay . The sensitivity and the specificity of the Ag-capture ELISA were 69% ( 27/39 ) and 100% ( 24/24 ) , respectively , based on the qRT-PCR results . The viral RNA copy number in the Ag-capture ELISA-positive samples ( mean ± SD: 6 . 548 ± 0 . 227 log10 copies/ml ) was significantly higher than that observed in the Ag-capture ELISA-negative samples ( 4 . 077 ± 0 . 178 log10 copies/ml; p < 0 . 0001; Fig 3A ) . We then determined the antigen titers of each of the serum samples by using serially-diluted serum samples for the Ag-capture ELISA . There was no statistically significant difference in the antigen titers between patients with SFTS with fatal and non-fatal outcomes ( p = 0 . 08; Fig 3B ) . However , high antigen titers ( ≥160 ) were detected in 82% ( 9/11 ) of serum samples collected from patients with fatal outcomes , and only 18% ( 2/11 ) of serum samples collected from patients with non-fatal outcomes were detected ( Table 2 ) . Furthermore , significant high antigen titers ( ≥10 , 240 ) were detected in serum samples collected from three patients with fatal outcomes ( Table 2 ) . We performed the IgG ELISA against SFTSV in the samples to determine the antibody responses , because the presence of the antibodies against SFTSV might inhibit the capture capacity of the MAb for SFTSV N protein . The IgG antibody status was compared between the Ag-capture ELISA positive and negative groups among the total of 39 serum samples positive for qRT-PCR . In 27 Ag-capture ELISA-positive samples , 11 ( 41% ) samples were IgG ELISA-positive , while 9 of 12 ( 75% ) Ag-capture ELISA-negative samples were IgG ELISA-positive . The OD values of the Ag-capture ELISA-positive samples in IgG ELISA ( mean ± SD; 0 . 143 ± 0 . 024 ) were significantly lower than those of Ag-capture ELISA-negative samples in the assay ( mean ± SD; 0 . 401 ± 0 . 087; p < 0 . 05 ) . We demonstrated that both the novel 2 MAbs ( 9D3 and 2D11 ) generated in this study reacted to SFTSV , but not to RVFV , FORV , and PALV in the genus Phlebovirus ( Fig 1A ) . However , a close antigenic relationship between FORV , PALV and SFTSV was demonstrated by the serological tests [19] . In addition , these MAbs did not react to the recombinant N protein of Heartland virus in IFA ( S2 Fig ) . Therefore , we speculate that the MAbs may not be cross-reactive to Malsoor virus and Hunter Island Group virus , which are closely related to SFTSV . This is because the amino acid sequence homology of N protein of SFTSV strain HB29 with those of Hearland virus , Malosoor virus , and Hunter Island Group virus were shown to be 61 . 6% , 55 . 6% , and 60 . 9% , respectively [20] . As amino acid sequence identities among the N protein of SFTSV strains available from databases are conserved with more than 98% homology , it is thought that the N protein of the Japanese strains and also the Chinese strains and South Korea strains are detectable in the Ag-capture ELISA and IHC using the MAbs developed in the present study . Furthermore , MAbs may be useful for future development of rapid dipstick , flow-through devices that require minimal training and do not require electricity . The rN protein concentration detectable using the Ag-capture ELISA for detecting SFTSV ( 10–40 pg /100 μL well ) was the same level as that for detecting RVFV with the previously developed Ag-capture ELISA [23] . However , the detection limit of the concentration of authentic viral antigens detectable by the Ag-capture ELISA for SFTSV ( 350–1 , 200 TCID50/100 μL/well seems to be higher than that for RVFV ( 7 . 8–31 . 3 pfu /100 μL/well ) [23] . Although it is difficult to simply compare the detection limits between the two ELISAs , a more sensitive detection of RVFV in Ag-capture ELISA in the previous study may be due to an abundant non-virion associated N protein in the viral supernatants collected from infected cell cultures that exhibit an obvious CPE as described by Shafagati et al [40] . In contrast , since SFTSV do not exhibit obvious CPE on infected Vero cells . Therefore , relatively lower detection limits of authentic SFTSV N protein in the Ag-capture ELISA seems to be attributable to a low amount of non-virion associated SFTSV N protein in the viral supernatants , despite virions being lysed by treatment with the detergent , Triton X-100 . The viral RNA level in sera of patients with SFTS was reported to be associated with the outcomes [13 , 14 , 15] . During the first stage of the disease ( day 1 to 7 post-onset of illness , taking the day on which symptoms , fever , first appeared as day 0 ) , the serum viral load is high ( average 105–106 copies/ml ) regardless of the outcomes of fatal or non-fatal cases [41] . During the second stage of the disease ( day 7 to 13 post-onset of illness ) , the serum viral load decreased in non-fatal cases but still remained high in fatal cases ( average 108 copies/ml ) [41] . It has also been reported that the amounts of Ag detected by the Ag-capture ELISA are well correlated with viremia of ebolavirus or Lassa virus following experimental animal infection [32 , 42] . Also , a moderate difference has been demonstrated in the serum ebolavirus Ag levels between patients who died and those who survived [43] . These findings suggested that the patient outcomes were expected from the results of Ag-capture ELISA . Indeed , we found that high antigen titers ( ≥160 ) were detected at a higher rate in serum samples collected from patients with fatal outcomes than from serum samples collected from patients with non-fatal outcomes ( Table 2 ) . However , there was no statistically significant difference in antigen titers between patients with SFTS with fatal and non-fatal outcomes ( p = 0 . 08; Fig 3B ) . This might be due to the small-scale samples used in this study . Thus , further large-scale investigation is required to elucidate the correlation between the results of Ag-capture ELISA and patient outcomes . Among qRT-PCR positive-patients , the Ag-capture ELISA-negative patients showed significantly higher IgG responses than the Ag-capture ELISA-positive patients ( Fig 3C ) . We speculate that the amount of N protein in serum samples collected may be lower than the detectin limit of the Ag-capture ELISA , since these patients had already reached convalescence phase , where IgG antibodies to SFTSV N protein had been induced . The induced antibodies against SFTSV N protein in serum samples may inhibit the reaction of the MAb the N protein in the Ag-capture ELISA . A similar event was reported in the case of development of Crimean–Congo hemorrhagic fever virus ( CCHFV ) N protein detection ELISA system . The presence of antibodies to CCHFV N protein in the samples inhibited the reactivity of MAb with antigens in the CCHFV N protein Ag-capture ELISA [24] . Direct evidence of an inhibitory effect on Ag detection by anti-N Abs has been provided by experiments using mixtures of viremic serum with increasing amounts of immune serum [44] . Our data indicate significantly high IgG levels in the serum samples of Ag-capture ELISA negative patients . Based on these findings , the underlying immune status of patients may be characterized using this assay . It is concluded that the Ag-capture ELISA developed is available for serum samples collected during the early phase of SFTS before antibody responses become detectable . Furthermore , the specific reaction of the MAbs to SFTSV antigens in tissues of patients with SFTS was confirmed ( Fig 1B ) . Therefore , the MAbs were demonstrated to be of use in the detection of SFTSV antigen in the autopsied materials for SFTS diagnosis with IHC . In this study , novel MAbs to SFTSV N protein were generated . The Ag-capture ELISA used for the MAbs in detecting SFTSV in the serum samples of the SFTS suspected patients was developed . Furthermore , MAbs were applied for the detection of SFTSV antigen in autopsied materials . These SFTSV antigen detection methods were useful for SFTS diagnosis .
Severe fever with thrombocytopenia syndrome ( SFTS ) is a tick-borne emerging infectious disease caused by a novel bunyavirus , SFTS virus ( SFTSV ) . Since first discovered in China in 2011 , SFTSV has been detected from SFTS patients and ticks with expanding geographic ranges from China to Japan and South Korea . The potential for SFTS spread to other warm or sub-tropical regions makes it a serious concern to the public health . It is of great importance to detect SFTSV rapidly and specifically for the effective control of the disease . For the diagnosis of viral infections , a sandwich antigen ( Ag ) -capture ELISA detecting viral nucleoprotein ( N ) in viremic serum samples has been widely applied to detect the agents , since it is the most abundant viral antigen and has highly conserved amino acid sequence . In this study , using the novel monoclonal antibodies raised against SFSTV-N , an Ag-capture ELISA system was developed , and the validation of this system was performed using sera collected from SFTS-suspected patients . Our data show that the Ag-capture ELISA was useful for the diagnosis of SFTS patients in the acute phase of the disease . This study shows a novel methodology for the diagnosis of SFTS , which may provide helpful information for the effective control of the disease .
You are an expert at summarizing long articles. Proceed to summarize the following text: The pathogen Mycobacterium tuberculosis employs a range of ESX-1 substrates to manipulate the host and build a successful infection . Although the importance of ESX-1 secretion in virulence is well established , the characterization of its individual components and the role of individual substrates is far from complete . Here , we describe the functional characterization of the Mycobacterium marinum accessory ESX-1 proteins EccA1 , EspG1 and EspH , i . e . proteins that are neither substrates nor structural components . Proteomic analysis revealed that EspG1 is crucial for ESX-1 secretion , since all detectable ESX-1 substrates were absent from the cell surface and culture supernatant in an espG1 mutant . Deletion of eccA1 resulted in minor secretion defects , but interestingly , the severity of these secretion defects was dependent on the culture conditions . Finally , espH deletion showed a partial secretion defect; whereas several ESX-1 substrates were secreted in normal amounts , secretion of EsxA and EsxB was diminished and secretion of EspE and EspF was fully blocked . Interaction studies showed that EspH binds EspE and therefore could function as a specific chaperone for this substrate . Despite the observed differences in secretion , hemolytic activity was lost in all M . marinum mutants , implying that hemolytic activity is not strictly correlated with EsxA secretion . Surprisingly , while EspH is essential for successful infection of phagocytic host cells , deletion of espH resulted in a significantly increased virulence phenotype in zebrafish larvae , linked to poor granuloma formation and extracellular outgrowth . Together , these data show that different sets of ESX-1 substrates play different roles at various steps of the infection cycle of M . marinum . Mycobacterium tuberculosis , the etiological agent for the disease tuberculosis ( TB ) , is still one of the most dangerous pathogens for global health [1] . Successful infection requires secretion of multiple virulence factors , facilitated by type VII secretion systems ( T7SS ) . Pathogenic mycobacteria have up to five T7SS , called ESX-1 to ESX-5 [2] , of which at least three are essential for growth and/or virulence [3 , 4] . The ESX-1 locus was the first T7SS to be identified . The loss of ESX-1 function in Mycobacterium bovis BCG is considered a decisive factor of attenuation of this vaccine strain [5] . Mouse infection experiments utilizing M . tuberculosis with a partial deletion in ESX-1 showed reduced granuloma formation , the characteristic pathological hallmark of mycobacterial disease [6 , 7] . Similarly , efficient granuloma formation , dissemination of disease and invasion of endothelial cells in the fish-pathogen Mycobacterium marinum is dependent on a functional ESX-1 secretion system [8–10] . More detailed analysis showed that ESX-1 substrates are required for phagosomal membrane rupture [11 , 12] . Thus far , about a dozen different proteins have been identified to be secreted through ESX-1 , which can be divided in three subgroups , the Esx proteins , the PE/PPE proteins and the Esp proteins . Of these substrates , the Esp proteins are ESX-1 specific [13] . The ESX-1 substrates EsxA ( ESAT-6 ) and EsxB ( CFP-10 ) are secreted as an antiparallel heterodimer [14] . Interestingly , the limited structural data available for PE and PPE proteins also show that these proteins form a heterodimer [15–17] . These heterodimers form a four-helix bundle and contain a YxxxD/E secretion motif directly after the helix-turn-helix on one of the Esx proteins and on the PE protein [15 , 18] . The ESX-1 substrate EspB forms a similar four helix bundle with the conserved secretion motif at the same position in the structure and therefore does not seem to require a partner protein [17 , 19] . EsxA and EsxB are most intensively investigated of the different ESX-1 substrates [11 , 20–22] and EsxA is thought to be responsible for ESX-1 related virulence determinants [11 , 21–24] . EspA and EspB have additionally been implicated to be important for virulence [25 , 26] . However , studying the exact role of each substrate is complicated , as deletion of esxA/esxB abolishes secretion of all different Esp proteins [8 , 27] , while espA and espB deletion mutants are unable to secrete EsxA/EsxB [25 , 27] . The ESX-1 secretion system consists of a membrane complex composed of the ESX conserved components ( Ecc ) EccB1 , EccCab1 , EccD1 and EccE1 [28 , 29] , which is stabilized by the MycP1 protein [29] . The ESX-1 secretion system additionally contains the cytosolic accessory components EspG1 and EccA1 . EspG functions as a specific chaperone of cognate PE/PPE substrates [30 , 31] and deletion of espG1 leads to a block in the secretion of PE35/PPE68_1 in M . marinum [31] . Loss of EspG1 in M . tuberculosis caused severe attenuation , both in cell infection and in mice [32] . EccA1 is a cytosolic AAA+ ATPase ( ATPases Associated with diverse cellular Activities ) , which is essential for the EsxA secretion in both M . tuberculosis and M . marinum [33 , 34] . The M . marinum eccA1-null strain has been shown to be attenuated in zebrafish larvae [34] . However , its exact function is not further characterized . In the M . marinum , the genes espG1 ( MMAR_5441 ) and eccA1 ( MMAR_5443 ) are separated in the esx-1 locus by espH ( MMAR_5442 ) . EspH-like proteins are unique for the ESX-1 system . EspD is a homologue of EspH , sharing 55% sequence identity in M . tuberculosis . EspD is encoded by the espACD locus , located more than 260 kb upstream of the ESX-1 gene cluster . Interestingly , M . tuberculosis EspD has a role in stabilizing the intracellular levels of the secreted substrate dimer EspA/EspC [35] . These observations suggest that EspH might function as a molecular chaperone . Here , we study the role of three accessory proteins EspG1 , EccA1 and EspH in M . marinum and could show that mutants in the corresponding genes displayed distinctive and contrasting virulence phenotypes , demonstrating that ESX-1 substrates play different roles in virulence . We additionally identified several potential new ESX-1 substrates . To study the role of accessory ESX-1 proteins EspG1 , EccA1 , and EspH in secretion , we created targeted knocked-out strains for espH and eccA1 and used the previously described espG1 knockout in M . marinum [31] . Deletion of the individual genes had no effect on bacterial growth in 7H9 medium ( S1A Fig ) . However , colonies of the eccA1 mutant appeared dry with a rough-surface , while no phenotypic change was observed for the ΔespG1 and ΔespH colonies . In addition , qRT-PCR on total RNA extractions showed that the different deletions had no polar effect on the transcription of neighboring genes ( S1B Fig ) . Next , secretion analysis was performed using immunoblotting and a set of antibodies directed against known ESX-1 substrates . GroEL2 was included as a loading and lysis control . As a known ESX-1 negative mutant we included the Mvu strain , which has a frameshift mutation in eccCb1 [4 , 36] ( Fig 1B , lane 6 and lane 7 , respectively ) . Our analysis showed that EsxA was no longer secreted in the ΔespG1 strain ( Fig 1B , lane 9 ) , similarly as observed in a previous study from our group [31] , but in contrast to the results obtained in M . tuberculosis [33] . Interestingly , the deletion of espH also resulted in a dramatic decrease in the secretion of EsxA ( Fig 1B , lane 10 ) . Surprisingly , and in contrast to what has been published previously [8 , 34] , we observed that secretion of EsxA was reduced in the eccA1 mutant , but not completely aborted ( Fig 1B , lane 8 ) . Next , we analyzed another ESX-1 substrate EspE ( MMAR_5439 ) , a highly abundant cell surface protein of M . marinum , which can be extracted from the cell surface using the mild detergent Genapol X-080 [37] . The surface localization of the ESX-5 dependent PE_PGRS proteins was included as controls . In the WT strain , EspE was secreted in two forms: a full-length protein of ~ 40 kDa and a putatively processed form of ~ 25 kDa ( Fig 1C , lane 6 ) . Surface localization of EspE was abolished in all the mutant strains ( Fig 1C , lane 7 to lane 10 ) . Notably , while EspE accumulated in the cell pellet of all non-secreting strains , this protein was not detected in the pellet fraction of the espH mutant ( Fig 1C , lane 5 ) , indicating that secretion of EspE was blocked at a different stage as compared to the other mutants . To confirm that the observed secretion defects were caused by the targeted mutations , complementation plasmids were constructed . Two different complementation plasmids were used: the first one includes the genomic region from espF ( MMAR_5440 ) to eccA1 ( MMAR_5443 ) , whereas in the second plasmid only the espG1-espH-eccA1 locus was present . Complementing the knockout strains with either of these plasmids fully restored the secretion of EsxA and EspE in all of the mutants ( Fig 1D and 1E ) . A major discrepancy with previous publications was our finding that EccA1 has a limited effect on EsxA secretion . Previously , Gao et al . showed , using the same M . marinum background strain , that EccA1 is crucial for ESX-1 secretion [8 , 34] . We realized that there is a difference in the growth conditions between the two studies; we used 7H9 medium whereas Gao et al . used Sauton medium [8 , 34] . To test whether the observed differences could be linked to a difference in growth condition , secretion analysis was performed on cultures grown in Sauton medium . Interestingly , whereas the results for ΔespG1 and ΔespH were identical ( Fig 2 , lane 9 and lane 10 , respectively ) , EsxA was no longer secreted in the eccA1 mutant strain ( Fig 2 , lane 8 ) , which shows that the role of EccA1 in EsxA secretion is dependent on the growth condition . The proteome of a number of ESX-1 targeted knockout strains of M . marinum has been determined previously [38] . However , this study did not include an espH mutant and the cell surface proteome was not analyzed . In order to obtain a comprehensive and detailed view , the complete secretomes of our mutant strains , the corresponding complemented strains and both the WT and ESX-1 secretion mutant eccCb1 were analyzed by mass spectrometry . As some ESX-1 substrates are efficiently secreted into the culture supernatant , while others mainly remain attached to the cell surface [37] , cells were grown with or without Tween 80 to study secreted proteins in the medium or the cell surface proteins , respectively . The cell surface proteins were extracted from the bacterial cells using Genapol X-080 . For the ESX-1 secretion ( eccCb1 ) mutant , a massive reduction in the secretion of all known ESX-1 substrates , i . e . EsxA ( MMAR_5449 ) , EsxB ( MMAR_5450 ) , EspB ( MMAR_5457 ) , EspC ( MMAR_4167 ) , EspE ( MMAR_5439 ) , EspF ( MMAR_5440 ) , EspJ ( MMAR_5453 ) , EspK ( MMAR_5455 ) and PPE68 ( MMAR_5448 ) , was observed , both in the cell surface-enriched fractions ( Fig 3A ) and the culture supernatants ( Fig 4A ) . These results are in line with published data [38] . Also the secretion of several other proteins , including the PE protein MMAR_2894 and PPE protein MMAR_5417 , was blocked , suggesting they are novel ESX-1 substrates . This notion is strengthened by the fact that these two proteins are homologous to the PE and PPE protein encoded by the esx-1 locus . For the other proteins that showed reduced spectral counts in the cell surface fractions it is more difficult to draw any conclusion . First of all , the difference in secretion levels are smaller as compared to the known ESX-1 substrates ( Fig 3 ) , but furthermore they lack known characteristics of T7SS substrates , such as the YxxxD/E secretion motif preceded by a predicted helix-turn-helix structure . The espG1 mutant showed similar secretion profiles as the eccCb1 mutant ( Fig 3B and Fig 4B ) , although the secretion of EspB , EspK and EspE seemed to be slightly less severely affected . This suggests that EspG1 is not only required as a chaperone for its cognate PE/PPE substrates , but plays a more central role in the secretion of all ESX-1 substrates . The secretion of all ESX-1 substrates returned to WT levels in the espG1 mutant carrying the pMV361::espF-eccA1 complementation plasmid ( S2A and S2B Fig ) . The secretome profiles of the eccA1 mutant in 7H9 medium showed only a mild reduction of ESX-1 substrates in both cell surface and supernatant fractions ( Fig 3D and Fig 4D ) . For instance , EsxA and EsxB secretion was five and two-fold decreased , respectively , while in the eccCb1 mutant the reduction of both was 10 fold ( Fig 4D ) . The substrates EspE , EspF , EspJ and EspK are more affected by the eccA1 mutation than the other substrates in both protein fractions . In concordance with the data obtained by immunoblotting , the complementation of the eccA1 mutant with pMV361::espF-eccA1 plasmid restored the secretion of all ESX-1 substrates ( S2A and S2B Fig ) . Deletion of espH resulted in a severe reduction of EspE and EspF ( Fig 3C ) , in line with our immunoblot analysis . This reduction was in fact almost complete , both in the fraction of the surface proteins ( determined LC-MS/MS ) and in the bacterial pellet ( determined by immunoblotting ) , which again suggests instability of intracellular EspE/EspF in the absence of EspH . This effect was restored when the complementation plasmid was introduced ( S2A and S2B Fig ) . Interestingly , the effects of the espH deletion on secretion of EsxA and EsxB was only mild as compared to the eccCb1 mutant , while the effects on other ESX-1 substrates , such as EspB , EspK and EspJ were also only minor ( Fig 4C ) . This indicates that ΔespH has a specific secretion defect for a subset of ESX-1 substrates and there is no substrate dependency between EspE/EspF and other Esp proteins . Surprisingly , we also identified some proteins that were present in significantly increased amounts in the cell surface enriched fractions of various mutants . One of these proteins is SecA2 , a cytosolic component of the Sec transport system and proposed to contribute to the virulence of M . tuberculosis and M . marinum [39 , 40] . SecA2 was present in higher amounts in all mutants except the ΔespH , suggesting a link with intracellular accumulation of EspE/EspF . Another intriguing observation is an increase of Mak in the ΔespG1 , ΔespH and the ΔeccA1 ( Fig 3B , 3C and 3D , respectively ) . Mak is a mycobacterial maltokinase whose function is involved in the glycan synthesis from trehalose [41] and considered to be essential for the growth of M . tuberculosis [42] . This could suggest that there is an indirect effect of ESX-1 secretion on the synthesis of the mycobacterial capsule . The observation that EspH mainly affects the secretion of EspE/EspF and that EspE could not be detected in the espH mutant pellet fraction raised the hypothesis that EspH could either regulate the transcription of espE/espF or stabilize EspE/EspF at the protein level . To get more information on the putative function of EspH we used the protein structure prediction program Phyre2 [43] . This analysis showed that part of EspH ( region between amino acid 65 and 135 ) is predicted to share structural similarity to YbaB proteins of Escherichia coli and Haemophilus influenza . Although the sequence identity with these proteins is low ( 15% ) the confidence of the structural homology is very high ( 97% ) . Because YbaB is reported to be a small DNA-binding protein that plays a regulatory role [44] , an effect on transcription regulation could be possible . Therefore , we measured the effect of espH deletion on espE and espF mRNA levels . Because the EsxA secretion was reduced in the espH mutant , esxA mRNA level was checked as well . Total mRNA was extracted from the WT MUSA , eccCb1 mutant and the ΔespH strain , and qRT-PCR was performed using primer sets for espE , espF and esxA . The results showed that the mRNA levels of all three genes were comparable to those of the eccCb1 mutant strain analyzed ( S3A Fig ) . Thus , we could disprove the possibility that EspH regulates espE at the transcriptional level . Next , we studied a direct interaction of EspH with EspE and/or EspF . Based on the high homology of EspE with EspA and EspF with EspC , we speculated that , similarly to EspC/EspA [45] , EspF might be secreted together with EspE . We therefore constructed a plasmid containing espE/espF in which espE was modified to express a C-terminal Strep tag . We also introduced a His tag at the C terminus of EspH in the espG1/espH/eccA1 complementation plasmid . Introduction of both plasmids in the WT and ΔespH mutant resulted in surface localized EspE , as judged by immunoblot analysis of the cell surface extracted protein preparations ( S3B Fig ) . These results show that the addition of the Strep tag to the C terminus of EspE and the His-tag to EspH did not affect the functionality of these proteins in the secretion process . To study the interaction of EspE and EspH , we overexpressed EspE-Strep/EspF and EspH-His in the eccCb1 mutant strain . The ESX-1 secretion system is defective in this strain and therefore EspE and EspH accumulate in the cytosol , which allows their analysis and co-purification . The subcellular localization of EspE and EspH was examined by a subcellular fractionation procedure , showing that EspE-Strep was partially soluble while EspH-His was exclusively present in the soluble fraction ( S3C Fig ) . Next , we used StrepTactin beads to purify Strep-tagged EspE from these soluble fractions . Immunoblot analysis showed that EspE-Strep was efficiently purified . Importantly , EspH-His , appearing as a ~ 25 kDa band , was only present in the elution fractions when expressed in the presence of EspE-Strep ( Fig 5A ) . In contrast , the ESX-1 substrates PPE68 and EsxA were not co-purified and both remained in the flow-through fraction . To confirm this EspE-EspH interaction , a reciprocal pull-down assay was performed using Ni-NTA beads and lysates of the eccCb1 mutant containing EspE-strep/EspF only or EspE-strep/EspF and EspH-His . Immunoblot analysis confirmed the efficient purification of EspH-His ( Fig 5B ) . Using anti-EspE on these samples showed co-elution of endogenous EspE only in the presence of the His-tagged EspH ( Fig 5B ) . Again , PPE68 and EsxA were only found in the flow-through fraction , indicating that they do not bind EspH . In conclusion , these data confirmed that EspH specifically interacts with EspE in the cytosol of M . marinum and this interaction is probably required for EspE secretion . ESX-1 functioning in M . marinum has been associated with lysis of red blood cells [8] . Because of this , the hemolysis assay has been employed as a model for the ESX-1-dependent lysis of ( phagosomal ) membranes [8] . Prior work suggested that the ESX-1 associated membrane lytic activity was mediated by EsxA through its pore-forming activity [21 , 46] . Because the deletion of espG1 , espH and eccA1 differently affected the secretion of EsxA , we examined to what extend these mutant strains were able to disrupt erythrocytes . While we confirmed that our WT strain showed hemolysis ( Fig 6A ) , both the eccCb1 and ΔespG1 mutant strain lost this ability , in line with the absence of ESX-1 substrates in the culture supernatant ( Fig 6A ) . Interestingly , the ΔespH and ΔeccA1 strains were also non-hemolytic , although these strains were still able to secrete EsxA to significant levels ( Fig 6A ) . The defects in hemolysis by the knockout strains were restored when the complemented plasmids were introduced into these mutant strains ( Fig 6B ) . As in the ΔespH and ΔeccA1 mutants mainly the secretion of different Esp proteins are specifically affected , our findings indicate that not a single ESX-1 substrate , such as EsxA , but a combination of different Esp proteins , are responsible for the hemolytic phenotype . To further characterize the function of the different ESX-1 substrate subsets , we used different phagocytic cells to study the ability of the mutant strains to survive and replicate within these cells . Phagocytic cells from mice ( RAW macrophage cell line ) and the protozoa Acanthamoeba castellanii were infected with green fluorescent protein ( GFP ) -expressing mycobacteria and infection levels were quantified by flow cytometry at different time points . As shown before , the eccCb1 mutant was strongly attenuated in both A . castellanii and RAW cells ( Fig 7; [47] ) , showing a 2-fold reduction in the number of infected cells after 24 h . As expected , based on the proteome profiles , the ΔespG1 strain showed an attenuated phenotype similar to the eccCb1 mutant . For the ΔespH mutant , the proportion of infected A . castellanii cells did not change over time ( Fig 7B ) , while in RAW macrophages a slight reduction of infected cells at 24 hpi could be observed ( Fig 7C , p = ns ) . Infection with the ΔeccA1 mutant resulted in an increase of infected cells over time , for both A . castellanii and RAW cells , and was therefore less attenuated as compared to the other mutants ( Fig 7B and 7D ) . Although this strain was able to infect A . castellanii to the same extend as the WT strain , infection with this mutant was not as successful as WT infection in RAW macrophages ( Fig 7A , ns; Fig 7C , p < 0 . 001 ) . Taken together , our data show the importance of espG1 in achieving successful infection of phagocytic cells , while the loss of eccA1 only marginally affects the ability of M . marinum to survive and replicate in a phagocytic host cell . These findings are in line with the proteomic analysis , i . e . the espG1 mutation has a strong effect on secretion of all ESX-1 substrates , while deleting eccA1 only results in a mild secretion defect . EspH , which seems to mainly influence EspE and EspF secretion , is also important for infecting phagocytes , but to a lesser extent than EspG1 . To study whether the individual ESX-1 proteins play a role during infection in vivo , we used the zebrafish larva-M . marinum infection model . Larvae were systemically infected with the fluorescently labeled mutant , complemented and WT strains and infection was analyzed 4-days post infection ( dpi ) by fluorescence microscopy . In addition , L-plastin staining was performed to visualize phagocytic cells in order to study the formation of early granulomas by confocal microscopy . Infection of zebrafish larvae with the ΔespG1 and ΔeccA1 mutant strains resulted in infection levels as expected from the previous experiments , i . e . the ΔespG1 showed a similar level of attenuation as the eccCb1 mutant , while the ΔeccA1 mutant infections were similar to wildtype infection ( Fig 8A , 8D and 8H for ΔeccA1; Fig 8B , 8F and 8J for ΔespG1 ) . Higher magnification of individual infection loci in ΔeccA1 infected larvae revealed recruitment of phagocytic cells and formation of early granulomas comparable to infection with WT ( Fig 8E for WT , n = 12 larvae; Fig 8I for ΔeccA1 , n = 8 larvae ) . In contrast , confocal imaging of ΔespG1 infected fish showed a predominance of single infected macrophages and formation of very small clusters of these infected macrophages similar to infection with the eccCb1 mutant ( Fig 8G for eccCb1 mutant , n = 10 larvae; Fig 8K for ΔespG1 , n = 7 larvae ) . Together , this shows that espG1 , but not eccA1 , plays a major role in early stages of infection in vivo . Moreover , since these strains show a comparable behavior during in vitro and in vivo infections , this indicates functional similarities for these genes in protozoa , mouse macrophages and zebrafish larvae . In contrast to the ΔespG1 and ΔeccA1 strain , the behavior of ΔespH in zebrafish larvae was completely different from its attenuated phenotype in vitro . Systemic infection of zebrafish larvae resulted in an increased bacterial load as compared to WT infection ( Fig 8C; p < 0 . 05 ) . Large bacterial clusters and a phenotype known as cording were seen in fluorescence images ( Fig 8L , arrow ) , especially at higher magnification of individual clusters ( Fig 8L , closed arrow , n = 15 larvae ) . Cording in zebrafish has been associated with extracellular growth [48] . In addition , very limited numbers of intact phagocytic cells and the presence of fluorescent spots suggestive for phagocytic cell debris were observed ( Fig 8L , open arrow ) . These observations raised the question whether this phenotype is still preceded by granuloma formation or if this mutant strain is preventing early granuloma formation by inducing rapidly host cell death . Therefore , larvae were systemically infected with either ΔespH or WT M . marinum as control and monitored daily for 4 consecutive days ( Fig 9 ) . Mycobacteria were phagocytosed by L-plastin positive phagocytic cells at 1 dpi in both groups ( Fig 9A and 9D ) . Subsequently , phagocytic cells were recruited and early granulomas started to form ( Fig 9B and 9E ) . However , at 4 dpi , in larvae infected with the ΔespH strain a strong decrease in phagocytic cells and increase in bacterial growth was observed ( Fig 9C and 9F ) . In the absence of phagocytic cells bacteria were able to show cording in both blood vessels ( Fig 9F , closed arrow ) and tissue ( Fig 9F , open arrow ) . Taken together , the ΔespH mutant seems to have a host-specific or in vivo-specific effect , illustrated by a hypervirulent phenotype seen in zebrafish larvae , but not in cell infections in vitro . Therefore , our data indicates that EspH is not required for initial phagocytosis , recruitment of cells and primary establishment of early granulomas , but this protein , and therefore a subset of ESX-1 substrates , seems to be essential for the maintenance of a stable granuloma . A number of studies have shown that the mycobacterial ESX-1 system plays a pivotal role in mycobacterial pathogenesis [6 , 21 , 27 , 33] . The system affects virulence through secretion of protein effectors with host-modulatory effects . Here , we show that EccA1 is not strictly required for the secretion of ESX-1 substrates . The finding that EccA1 is important for secretion is in line with previous reports [8 , 34] , but the fact that the role of EccA1 is depending on the growth medium is entirely surprising . This difference could also explain the variable results described for the role of EccA1 in EsxA secretion by M . tuberculosis [49] . Of all ESX-1 substrates , EspE , EspF , EspJ and EspK secretion was mostly affected in our eccA1 mutant strain , while secretion of EspB , EsxA/EsxB and PE/PPE was hardly altered . An interesting observation here is the discrepancy between the active secretion of EsxA in the ΔeccA1 strain and at the same time loss of hemolytic activity . Although this observation has been described before , this was always linked to a reduced secretion of EsxA in these strains [8 , 34] . In a recent study , the importance of EsxA in lysing membranes was questioned [50] . Our results also supports an alternative mechanism: we find a strong correlation between ESX-1 functionality and hemolysis , but this correlation is not seen for EsxA secretion . Our finding is in line with several other recent studies , who showed that both EsxA and the cell-surface lipid PDIM are important for phagosomal rupture and escape by M . tuberculosis [51–53] . We propose that it is not the loss of secreted EsxA , but the loss of ( multiple ) surface-exposed Esp proteins that results in hemolytic deficiency . Even though the ΔeccA1 strain lost its ability to induce membrane lysis , virulence in isolated phagocytes and in zebrafish larvae was only mildly affected in our study . This is in contrast with other studies , who described an attenuated phenotype for similar mutants in M . tuberculosis and M . marinum in murine macrophages and zebrafish [8 , 34] . The latter observations were made after a longer incubation time , which might explain the discrepancy with our study . Distinct phenotypes of the eccA1 mutant in different host cells have also been reported in a genome-wide transposon mutagenesis study [47] . Here , transposon insertions in M . marinum E11 eccA1 led to severe attenuation in mammalian phagocytic cells but these mutants were hypervirulent in protozoan cells [47] . This suggests that M . marinum can employ host-specific virulence mechanisms to adapt to different intracellular environments and that EccA1 might be essential for secretion and virulence under specific circumstances or in a subset of specific hosts . The role of EspG as a specific chaperone for the recognition and secretion of cognate PE/PPE proteins has been well established in M . marinum [30 , 31] . Our extracellular proteomic study not only confirms the loss of PE/PPE substrate secretion in the M . marinum ΔespG1 strain , but also reveals the secretion block of other ESX-1 dependent substrates , including EsxA/EsxB . This effect on EsxA/EsxB secretion however was not observed in an M . tuberculosis espG1 knock-out strain [33] . EspG proteins bind specifically to the extended helices of the PPE protein , which are absent in Esx proteins . Therefore , the strong effect of espG1 deletion on Esx ( and also Esp ) protein secretion in M . marinum is likely indirect due to a mutual dependency in secretion among the ESX-1 substrates [27 , 31 , 35] . This co-dependency of PE/PPE and other ESX-1 substrates for secretion is possibly less strict in M . tuberculosis , explaining that mutating espG1 had no effect on EsxA/EsxB secretion in this species . Because of the severe secretion defect of all detectable ESX-1 substrates , the M . marinum espG1 mutant is non-hemolytic and strongly attenuated in macrophage and amoeba , which is in good agreement with previous reports [8] . Furthermore , the loss of espG1 resulted in a strong attenuation in zebrafish , to the same extend as the eccCb1 mutant . Our most significant and surprising results were obtained for EspH . EspH is specific for the ESX-1 secretion system and is highly conserved among pathogenic mycobacterial species , including M . tuberculosis and M . leprae . The latter species has been streamlined into a minimal genome by a process of extensive genome decay . In our study , deletion of this gene abolishes the expression and secretion of two specific ESX-1 substrates EspE and EspF . Furthermore , we could show that EspH specifically interacts with EspE in the cytosol , indicative of chaperone activity . However , the Phyre2 structural prediction program [43] indicated that EspH is shares similarity to YbaB . The first structural study on YbaB strongly indicated that this protein binds DNA as a dimer [44] . However , recent studies indicated that the function of YbaB might be more diverse . One study showed that YbaB is associated with and a target of ClpYQ proteases in E . coli [54] , while another study indicated that overexpression of YbaB enhanced the production of heterologous membrane proteins [55] . Based on the direct interaction of EspH with EspE and that the EspH-like protein EspD stabilizes intracellular EspA/EspC substrates [35] , we propose that these YbaB-like proteins encoded by the esx-1 cluster of pathogenic mycobacteria function as dedicated chaperones for specific ESX-1 substrates . Recently , a study of M . tuberculosis EspL also predicted a high resemblance to YbaB [56] , making it tempting to speculate that EspL may as well function as a dedicated chaperone , for instance the ESX-1 substrates encoded by the adjacent genes EspK or EspB . It becomes clear that multiple chaperones , such as EspG1 , EspD and EspH , are responsible for stabilizing their cognate substrates PE35/PPE68 , EspC/EspA and EspF/EspE , respectively . Interestingly , secretion of other substrates of the ESX-1 system , such as EspB , EspK and EspJ , did not seem to be affected by disruption of the espH gene . A similar phenotype was observed previously in an espA::tn mutant of M . tuberculosis [26] , where secretion of EsxA/EsxB but not EspB was aborted . These results show that interdependence in ESX-1 secretion is not a general feature . Deletion of espH did result in reduced secretion of EsxA/EsxB , which was not due to differences in mRNA levels . This hints towards a possible regulation mechanism between the secretion of the central components EsxA/EsxB and the individual Esp substrate ( pairs ) but not among the Esp proteins themselves . The espH mutant strain showed a loss of hemolytic activity and a reduction of intracellular growth in phagocytic host cells in our study . Strikingly , zebrafish larvae were heavily infected with this mutant strain and showed even hypervirulence at later time points , even though EsxA/EsxB secretion was reduced in this mutant . More detailed analysis showed that initial phagocytosis and primary establishment of an early granuloma was not affected in this mutant . Eventually , a stable cluster of immune cells could not be maintained in larvae infected with the espH mutant , with subsequent extracellular bacterial outgrowth and apparent phagocyte death . The discrepancy between in vitro and in vivo results indicate an essential role for a , yet unknown , host factor involved . It is tempting to speculate that EspE/EspF , the two proteins that are most severely affected by the espH deletion , interact with this host factor in order to induce the homeostatic balance between host and pathogen in developing granulomas . Furthermore , because EsxA and EsxB secretion was diminished , other ESX-1 substrates in addition to these central substrates might be involved in the infection process . A candidate might be EspB , whose secretion was not affected in espH mutant strain , and was shown to be able to facilitate M . tuberculosis virulence in vitro and in vivo in an EsxA-independent way [26] . In summary , this study highlights the complexity of the ESX-1 secretion machinery . We unravel valuable information about the functions of the individual ESX-1 components EccA1 , EspG1 and EspH , all having their unique role in secretion of the different substrate classes . We can conclude that ESX-1 has several different sets of substrates that are involved in distinctive processes required for virulence . All M . marinum strains used in this study were derived from the wild-type strain MUSA [57] . The eccCb1 ( MVU ) strain was previously identified as an ESX-1 secretion mutant with a spontaneous out of frame mutation in eccCb1 [36] and also the knock-out strain espG1 was described before [31] . The knockout strains of eccA1 and espH were generated using the mycobacteriophage approach ( see below ) . All strains were routinely cultured on Middlebrook 7H10 plates or in Middlebrook 7H9 medium ( Difco ) containing ADC supplement or on Sauton medium [58] supplemented with 2% glycerol and 0 . 015% Tween-80 . When required , 0 . 05% Tween-80 and the appropriate antibiotics were added ( 25 μg/ml kanamycin ( Sigma ) and/ or 50 μg/ml hygromycin ( Roche ) . M . marinum cultures and plates were incubated at 30°C . E . coli TOP10F’ was used for cloning experiments to generate the complemented plasmids and was grown at 37°C on LB plates and in LB medium . Different antibiotics were added to the cultures or plates when necessary at similar concentrations as for M . marinum cultures . All DNA manipulation procedures followed standard molecular biology protocols . Primers were synthesized and purified by Sigma . Phusion polymerase , restriction enzymes and T4 DNA ligase were obtained from New England Biolabs ( NEB ) . Macrogen performed DNA sequencing . Bacterial RNA was extracted from various M . marinum strains as described previously [31] and cDNA was synthesized using SuperScript VILO cDNA Synthesis kit ( Thermoscientific ) according to manufacturer protocol . For the PCR mix the SYBR GreenER qPCR SuperMix ( Thermoscientific ) was used according to manufacturer instructions , including the addition of ROX dye reference . qRT-PCR was performed in Applied Biosystems 7500 Fast system . The primer sequences used for qRT-PCR are listed in S3 Table . Controls without reverse transcriptase were done on each RNA sample to rule out DNA contamination . The sigA gene was used as an internal control . An eccA1 and espH knockout was produced in M . marinum MUSA by allelic exchange using a specialized transducing mycobacteriophage as previously described [59] . High phage titers were prepared following the previously described protocol [31] . Subsequently , the M . marinum wild-type strain was incubated with the corresponding phage to create eccA1 and espH knockouts . Colonies that had deleted the endogenous eccA1 and espH were selected on hygromycin plates and tested for sucrose sensitivity , induced by the presence of the sacB gene . The deletion was confirmed by PCR analysis and sequencing . To remove the resistance and sacB gene , a temperature sensitive phage encoding the γδ-resolvase ( TnpR ) ( a kind gift from Apoorva Bhatt , University of Birmingham , UK ) was used , generating an unmarked deletion mutation . M . marinum cultures were grown in 7H9 medium supplemented with ADC and 0 . 05% Tween 80 to mid-logarithmic phase . Bacteria were washed two times and set to OD600 of 0 . 35 in 7H9 medium containing 0 . 2% glycerol , 0 . 2% dextrose and 0 . 05% Tween 80 for overnight growth . Supernatants were filtered using 0 . 2 μm filter , concentrated by trichloric acid ( TCA ) precipitation , washed with acetone and the supernatant pellets were resuspended in solubilisation/denaturation ( S/D ) buffer ( containing 100mM DTT and 2% SDS ) . Bacteria were washed once with PBS . Aliquots were taken for the whole cell lysate preparations and for Genapol X-080 extraction of cell-surface-attached proteins . Bacteria were incubated with 0 . 5% Genapol X-080 in PBS for 30 minutes with head-over-head rotation at room temperature . Genapol extracted supernatants were denatured in S/D buffer . The bacterial pellet and Genapol extracted cells were lysed by bead beating for 1 minute two times after which S/D buffer was added . All samples were boiled for 10 minutes at 95°C before loading on SDS-PAGE . For His-tag pulldown , mycobacterial cultures grown to an OD600 of 1 . 0 were incubated for 1 h with 100 g/ml ciprofloxacin ( Sigma ) , pelleted , washed twice with PBS , and subsequently resuspended in PBS supplemented with Complete protease inhibitor mixture ( Roche Applied Science ) , 1 mM EDTA , and 10 mM imidazole . Cells were broken by two-times passage through a One-Shot cell disrupter ( Constant Systems ) at 0 . 83 kbar . Unbroken cells were spun down by repeated centrifugation at 3000 g , and subsequently the cell envelope and soluble fractions were separated by ultracentrifugation at 100 , 000 g for 1hr . Membrane-cleared lysates of M . marinum expressing proteins of interest were incubated with Ni-NTA agarose beads ( Qiagen ) for 1 h at room temperature with head-over-head rotation . After washing the beads five times with phosphate buffer containing 50 mM NaH2PO4 and 300 mM NaCl , ( pH 8 . 0 ) , supplemented with 20 mM imidazole , bound proteins were eluted three times by incubation with phosphate buffer containing 400 mM imidazole . Immunoprecipitation of strep-tagged proteins was performed using the Strep-Tactin Sepharose kit ( IBA ) , following the manufacturers protocol . Proteins were separated by SDS-PAGE and stained with Coomassie Brilliant Blue G-250 ( CBB; Bio-Rad ) , or transferred to nitrocellulose membranes by Western blotting . The membranes were then incubated with different antibodies followed by enhanced chemiluminescence . Primary antibodies used in this study include: anti- GroEL2 ( CS44 , Colorado state university ) , anti-PE_PGRS antibody ( 7C4 . 1F7 ) [36] , anti-EsxA ( Hyb76-8 ) [60] , polyclonal anti-EspE and anti-PPE68 [61 , 62] . To investigate the cell-surface attached proteome , samples for LC-MS/MS analysis were prepared using the mild detergent Genapol X-080 as previously described [63] . To prepare the secreted material , the M . marinum MUSA wild-type and the studied ESX-1 mutant and complemented strains were grown to stationary phase in 7H9 medium supplemented with ADC , 0 . 2% glycerol and 0 . 05% Tween 80 . The supernatant fractions containing secreted proteins were collected and spun at 2500 × g for an additional 20 min at 4°C and subsequently filtered through a 0 . 2 μm pore size membrane to remove residual cells and cell debris . The filtered supernatants were 20 times concentrated using Amicon Ultra-15 Centrifugal 3 kDa molecular weight cut off membrane at 4°C . The retained proteins were TCA precipitated , pelleted , washed in acetone , dried and resuspended in S/D sample buffer to the corresponding OD of 200 units/ml . All samples were analyzed with SDS-PAGE and CBB staining . Total protein lanes of cell surface and culture supernatant proteins were excised in 3 or 1 fragment ( s ) per lane , respectively , and analyzed by LC-MS/MS as described before [63] . The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD008905 . M . marinum strains were grown in 7H9 medium supplemented with ADC and 0 . 05% Tween-80 till the mid-logarithmic phase . All strains were washed once with PBS and inoculated in 7H9 medium with or without Tween-80 at 0 . 35 OD600/ml and inoculated for 20 hours . Bacteria were collected by centrifugation , washed once in PBS and diluted in fresh DMEM medium without phenol red ( Gibco , Life technologies ) . Bacteria were quantified by absorbance measurement at OD600 with an estimation of 2 . 5*108 bacteria in 1 ml of 1 . 0 OD600 . At the same time , defibrinated sheep erythrocytes ( Oxoid-Thermo Fisher , the Netherlands ) were washed five times and diluted in the same fresh DMEM medium . 4 . 2*107 erythrocytes and 1*108 bacteria were added for one reaction of 100 μl in a round-bottom 96 well-plate , gently centrifuged for 5 minutes and incubated at 32°C . After an incubation of 3 hours , cells were resuspended , centrifuged and 80 μl of supernatants were transferred to a flat-bottom 96-wells plate and measured at an absorbance of 405nm to quantify hemoglobin release . The mouse macrophage line RAW264 . 7 ( American Type Culture Collection ) was cultured in RPMI 1640 with Glutamax-1 ( Gibco ) supplemented with 10% fetal bovine serum ( FBS; Gibco ) , 100 U of penicillin/ml , 100 μg of streptomycin/ml at 37°C , 5% CO2 . A total of 3 × 107 cells was seeded in T175 flasks ( Corning ) . Acanthamoeba castellanii was seeded in T175 flasks and grown in PYG medium , which is 0 . 4M MgSO4 . 7H2O , 0 . 05M CaCl2 , 0 , 1 M Sodium citrate . 2H2O , 0 . 05M Fe ( NH4 ) 2 ( SO4 ) 2 . 6H2O , 0 . 25M Na2HPO4 . 7H2O , 0 . 25M KH2PO4 in distilled water with 2% proteose peptone ( W/V , BD 211684 ) and 0 . 01% yeast extract . After pH adjustment to 6 . 5 , 2M glucose was added . All bacterial strains were grown until the exponential growth phase , washed with 0 . 05% Tween 80 , spun down and resuspended in RPMI medium . RAW macrophages were infected with a MOI of 5 for 3 hours and incubated at 30°C , 5% CO2 . Cells were washed in RPMI to remove extracellular mycobacteria and either analyzed immediately or incubated for another 21 hours at 30°C , 5% CO2 . A . castellanii ( ATCC 30234 ) infection was performed with a MOI of 1 , 3 , 9 , 27 , 54 , and 108 to determine optimum MOI , for the remaining experiments MOI 3 was chosen . Incubation for 3 hours or 24 hours was done at 30°C , 5% CO2 . Uptake of strains in host cells was quantified for all cell lines with a BD Accuri C6 flow cytometer ( BD Biosciences ) with a 488-nm laser and 585/40-nm filter to detect mEos3 . 1 . A minimum of 5000-gated events was collected per sample per time point , percentage of living cells , percentage of infected cells and median fluorescent intensity per cell was analyzed using BD CFlow software . Injection stocks were prepared by growing bacteria until the logarithmic phase ( OD600 of 0 . 7–1 ) . Bacteria were spun down at low speed for 1 min to remove the largest clumps , washed with 0 . 3% Tween-80 in phosphate buffered saline ( PBS ) and sonicated shortly for declumping . Bacteria were than resuspended in PBS with 20% glycerol and 2% PVP and stored at −80°C . Before use , bacteria were resuspended in PBS containing 0 . 17% ( V/V ) phenol red ( Sigma ) to aid visualization of the injection process . Transparent casper zebrafish larvae [64] were removed from their chorion with tweezers and infected at 1 day post fertilization ( dpf ) via the caudal vein with bacterial suspension containing 50–200 CFU . Injection was performed as described previously [65] . To determine the exact number of bacteria injected , the injection volume was plated on 7H10 plates containing the proper antibiotic selection . At 4 days post infection ( dpi ) larvae were analyzed with a Leica MZ16FA fluorescence microscope . Bright field and fluorescence images were generated with a Leica DFC420C camera . Infection levels were quantified with a custom-made fluorescent pixel counting software . The software is in house developed and freely available under MIT license . Following analysis , larvae were fixed overnight in 0 . 4% ( V/V ) paraformaldehyde ( EMS , 100122 ) in PBS , washed and stored in PBS for immunohistochemistry . All procedures involving Danio rerio ( zebrafish ) larvae were performed in compliance with local animal welfare laws and maintained according to standard protocols ( zfin . org ) . The breeding of adult fish was approved by the institutional animal welfare committee ( Animal Experimental licensing Committee , DEC ) of the VU University medical center . All protocols adhered to the international guidelines specified by the EU Animal Protection Directive 86/609/EEC , which allows zebrafish larvae to be used up to the moment of free-living ( approximately 5–7 days after fertilization ) . In the current study , zebrafish larvae were used between 1 and 5 days post fertilization . Larvae were rinsed with 1% PBTx , ( 1% Triton X-100 in PBS ) , permeated in 0 . 24% trypsin in PBS and blocked for 3 hours in block buffer ( 10% normal goat serum ( NGS ) in 1% PBTx ) . Samples were incubated with anti-L-plastin [1:500 ( V/V ) dilution] in antibody buffer ( PBTx containing 1% ( V/V ) NGS and 1% ( W/V ) BSA ) overnight at RT . Samples were washed with PBTx , incubated for 1 hour in block buffer and stained with an Alexa-Fluor-647 goat-anti-rabbit antibody ( Invitrogen A21070 , 1:400 ) , overnight at 4°C . Confocal analysis was performed on larvae , embedded in 1% low melting-point agarose ( Boehringer Mannheim , 12841221–01 ) in an 8-well microscopy μ-slide ( ibidi ) , Analysis was performed with a confocal laser scanning microscope ( Leica TCS SP8 X Confocal Microscope ) . Leica Application Suite X software and ImageJ software were used to adjust brightness and contrast and to create overlay images and 3D models . Graphs were made using Graph Pad Prism 6 . 0 . Pixel counts were logarithmic transformed; error bars represent mean and standard error of the mean . A one-way ANOVA was performed followed by a Bonferroni’s multiple comparison test to analyze statistical significance . Graphs with results of RAW264 . 7 and A . castellanii infection experiments show percentage-infected cells of total cells; error bars represent mean and standard error of the mean . Data representing the fold change between 3 and 24 hpi was logarithmic transformed . A two-way ANOVA followed by a Sidak’s multiple comparison test was performed for statistical significance .
M . tuberculosis is a facultative intracellular pathogen that has an intimate relationship with host macrophages . Proteins secreted by the ESX-1 secretion system play an important role in this interaction , for instance by orchestrating the escape from the phagosome into the cytosol of the macrophage . However , the exact role of the ESX-1 substrates is unknown , due to their complicated interdependency for secretion . Here , we study the function of ESX-1 accessory proteins EccA1 , EspG1 and EspH in ESX-1 secretion in Mycobacterium marium , the causative agent of fish tuberculosis . We found that these proteins affect the secretion of different substrate classes , which offers an approach to study the roles of these substrate groups . An espG1 deletion broadly aborts ESX-1 secretion and thus resulted in severe attenuation in a zebrafish model for tuberculosis , whereas EccA1 is only crucial under specific growth conditions . The most surprising results were obtained for EspH . This protein seems to function as a molecular chaperone for EspE and is as such involved in the secretion of a small subset of ESX-1 substrates . Disruption of espH showed a dual character: whereas this gene is essential for the successful infection of macrophages , deletion of espH resulted in significantly increased virulence in zebrafish larvae . These data convincingly show that different subsets of ESX-1 substrates play different roles at various steps in the mycobacterial infection cycle .
You are an expert at summarizing long articles. Proceed to summarize the following text: The evolution of insecticide resistance is a global constraint to agricultural production . Spinosad is a new , low-environmental-risk insecticide that primarily targets nicotinic acetylcholine receptors ( nAChR ) and is effective against a wide range of pest species . However , after only a few years of application , field evolved resistance emerged in the diamondback moth , Plutella xylostella , an important pest of brassica crops worldwide . Spinosad resistance in a Hawaiian population results from a single incompletely recessive and autosomal gene , and here we use AFLP linkage mapping to identify the chromosome controlling resistance in a backcross family . Recombinational mapping with more than 700 backcross progeny positioned a putative spinosad target , nAChR alpha 6 ( Pxα6 ) , at the resistance locus , PxSpinR . A mutation within the ninth intron splice junction of Pxα6 results in mis-splicing of transcripts , which produce a predicted protein truncated between the third and fourth transmembrane domains . Additional resistance-associated Pxα6 transcripts that excluded the mutation containing exon were detected , and these were also predicted to produce truncated proteins . Identification of the locus of resistance in this important crop pest will facilitate field monitoring of the spread of resistance and offer insights into the genetic basis of spinosad resistance in other species . Insecticide resistance has become one of the major driving forces altering the development of integrated pest management programs worldwide . The diamondback moth , Plutella xylostella , is a global agricultural pest of crucifers and commonly develops resistance to insecticides in the field [1] . Resistance , defined as a change in response to selection by toxicants [2] , has been reported to a wide range of chemicals with different modes of action , including pyrethroids , carbamates and organophosphates [3] as well as biologically derived insecticides Bt [4] and spinosad [5] . Understanding the mode of action of insecticides , and identifying the genetic mechanisms and mutations that confer resistance , will ultimately enable early detection of resistance alleles in the field and improve management strategies . Resistance to spinosad emerged in field populations of P . xylostella at a remarkably rapid rate . For example , after only ≈2 . 5 years of commercial application of spinosad in Hawaii , six of 12 field collected populations were highly resistant , with toxicity ratios of >100 relative to a susceptible control strain [5] . Spinosad resistance in diamondback moth has subsequently been reported in additional populations in the USA , Thailand and Malaysia [5]–[7] . Resistance to spinosad has also been selected in laboratory strains of Heliothis virescens [8] , Musca domestica , [9] and Bactrocera dorsalis [10] and reported in western flower thrips , Frankliniella occidentalis , collected from greenhouses [11] . Since its introduction in 1997 , spinosad has been approved in more than 30 countries for use on over 150 different crops [12] . The insecticide targets a range of lepidopteran and dipteran pests [13] , yet is relatively safe to non-target organisms [14] , [15] . The active ingredients of spinosad are macrocyclic lactones , spinosyn A ( primary component ) and spinosyn D , produced by the actinomycete Saccharopolyspora spinosa [16] during fermentation [17] , [18] . Upon spinosad exposure , insects experience tremors and paralysis caused by neuromuscular fatigue as the insecticide interferes with the central nervous system , which ultimately leads to death [19] . Spinosad primarily targets the nicotinic acetylcholine receptor ( nAChR ) [20] , which plays an essential role in excitatory synaptic transmissions of insect nervous systems [21] , [22] . nAChRs consist of five subunits , with extracellular N-terminal domains that bind acetylcholine , and four transmembrane domains . Five insect genomes have been mined for nAChRs , with 12 identified from Tribolium castaneum [23] and Bombyx mori [24] , 11 from Apis mellifera [25] and 10 from both Drosophila melanogaster [26] and Anopheles gambiae [27] . Although insects generally have fewer nAChRs than vertebrates , increased subunit diversity has been reported through alternate exon splicing , exon exclusion or A-to-I pre-mRNA editing . For example , it has been estimated nAChR Dα6 of D . melanogaster is theoretically capable of producing >30 , 000 different subunit variants [28] and there are at least 18 reported transcripts ( 8 of which include premature stop codons ) in T . castaneum Tcasα6 [29] . It has already been demonstrated that a nAChR Dα6 deficiency strain of D . melanogaster with one chromosome carrying a deletion of Dα6 shows 1181 fold resistance to spinosad [30] . One of the breakpoints in the opposite balancer chromosome CyO occurs within an exon of Dα6 , fusing it to another gene . Although this prematurely truncates the coding sequence , it confers resistance without being lethal , making this gene a prime candidate for field based resistance in insect pests . However , Gao et al . ( 2007 ) found no significant differences in sequence or expression of the Musca domestica orthologue , Mdα6 in a laboratory selected resistant strain ( rspin ) [31] . We have focused on field-based resistance to spinosad in a Plutella xylostella strain originally collected from Pearl City , Hawaii . Following further laboratory selection , resistance in the Pearl-Sel strain was shown to be a recessive and inherited as a single autosomal locus , and not due to metabolically mediated detoxification [5] . Crossing experiments have recently shown the same field evolved spinosad resistance mechanism is shared among populations isolated from Hawaii , California and Georgia [32] . Here we take a genetic linkage mapping approach to identify the chromosome carrying a field derived spinosad resistance mechanism . The nAChR Dα6 orthologue , Pxα6 , was mapped to the resistance locus PxSpinR by recombinational mapping , and a mutation in the 5′ donor site of intron 9 was found to cause mRNA mis-splicing thereby introducing an additional 40 bases into the mRNA of the resistant strain . This mutation leads to a premature termination codon between transmembrane domains 3 and 4 and is the likely functional cause of resistance . Further analysis around this gene region revealed complex transcript splice patterns that result in multiple frame shift mutations in the resistant , but not susceptible strain . Spinosad resistance in Plutella xylostella was predicted to be caused by a single , autosomal recessive gene [5] . We used biphasic linkage analysis , as previously employed in mapping Bt-resistance in P . xylostella [33] , to identify the chromosome and localized region containing the resistance gene . Crosses were prepared between a spinosad susceptible Geneva 88 female and a spinosad resistant BCS3-Pearl male . Some F1 progeny were bio-assayed with a diagnostic dose of spinosad ( 10ppm ) , with no survival , demonstrating that resistance is recessive at this dosage . Single pair “female informative” backcrosses were established between an F1 female and a BCS3-Pearl male . The backcross progeny were expected to segregate 1∶1 for spinosad resistance or susceptibility . Approximately 70 sibling larvae were treated with 10 ppm spinosad to kill any heterozygous susceptible progeny , leaving 35 “bioassay survivors” , while 32 “untreated controls” were not exposed to insecticide . Bioassay survivors and untreated controls were reared to adults , and genomic DNA isolated for molecular analysis . Female Lepidoptera do not undergo crossing over between chromatids during oogenesis [34]–[36] . Consequently , the chromosomes inherited from the mother are passed to the next generation as complete units . All genes and molecular markers on the same chromosome are therefore linked; and we used this property to identify the linkage group containing PxSpinR . AFLP genotyping was performed on a BCS3-Pearl grandfather , Geneva88 grandmother , F1 mother , BCS3-Pearl backcross father , 20 F2 untreated controls and an average of 19 F2 spinosad bioassay survivors . 146 variable AFLP markers inherited from the F1 mother were scored and assigned to 30 of the expected 31 linkage groups , each containing between 2 and 10 markers . The origin of each AFLP marker from the F1 mother could be associated with the resistant grandfather or susceptible grandmother . Following this , 2×2 χ2 tests were performed for each linkage group , comparing the number of susceptible and resistant AFLP genotypes inherited in the untreated controls with the spinosad bioassay survivors . A single linkage group was significantly associated with spinosad resistance , with all bioassay survivors inheriting the resistance derived LG01 ( χ2 = 15 . 53 , P>0 . 0001 ) ( Figure 1 ) . A P . xylostella cDNA pool derived from egg and larval tissue was sequenced using 454-FLX sequencing technology ( Roche ) . This provided transcriptome sequence to search for resistance candidate genes , however , nAChR Pxα6 was not present in this dataset . Consequently , PCR with degenerate primers was used to amplify a nAChR α6 gene fragment from larval cDNA ( amino acids 105–304 ) with 92% identity to the Drosophila homologue Dα6 . Species specific primers were designed for gene mapping , and Pxα6 genetically mapped to the spinosad resistance linkage group , LG01 . All 35 backcross progeny that survived exposure to spinosad inherited the same BCS3-Pearl derived linkage group from the F1 mother , while 32 untreated controls segregated 15∶17 for the susceptible or resistant derived chromosome respectively . As chromosomal crossing over occurs during spermatogenesis , distances between markers on the same chromosome can be estimated based on recombination rates using the progeny of male informative crosses ( F1 male backcrossed to a female ) in the second step of biphasic linkage analysis . Male informative mapping families were generated from 31 F1 brothers who were backcrossed to BCS3-Pearl females in single pair matings . Bioassays with 15 ppm spinosad were performed on 2315 backcross progeny , of which 884 survived ( 38% survival ) . To determine whether nAChR Pxα6 mapped to the PxSpinR locus , DNA was extracted from 24 of the male informative backcrosses , totalling 734 bioassay survivors and 286 untreated controls . A genotyping assay using a polymorphism within intron 5 of Pxα6 showed that only 3/734 bioassay-survivors inherited the allele from the spinosad susceptible strain , compared to 48 . 9% of controls , demonstrating that this marker was tightly linked to the spinosad resistant mutation . At any polymorphism causally responsible for resistance , however , no susceptible alleles would be expected among survivors , since F1 heterozygotes cannot survive the concentration of spinosad used in the bioassay . To determine whether the resistance causing mutation was up- or down-stream of Pxα6 intron 5 , candidate markers for genes flanking Pxα6 were identified from the genome of silkmoth Bombyx mori and BLASTed against P . xylostella 454 cDNA sequences . Genotyping assays were developed for flanking genes phosphatidylserine receptor ( PPTSR ) and arginine kinase ( ArgKin ) . Genotyping in PPTSR identified 6/723 recombinants , including the same three individuals from nAChR Pxα6 intron 5 , showing this was further from the resistance locus . Genotyping in arginine kinase had 16/536 recombinants , none of which were present at Pxα6 intron 5 . Hence the spinosad resistance region mapped between Pxα6 intron 5 and arginine kinase . A second Pxα6 PCR genotyping assay spanning intron 11 of nAChR Pxα6 was performed on all recombinants and a subset of progeny that were nonrecombinant in this region . Here , all bioassay survivors had the same BCS3-Pearl derived resistant genotype showing complete linkage with the spinosad resistance locus , PxSpinR ( Figure 2 ) . To identify predicted coding and intragenic sequence of Pxα6 , a P . xylostella genomic BAC library was constructed using susceptible strain Geneva88 , 23K clones printed to nylon membrane filters , then hybridised with a cDNA amplicon covering a portion of the Pxα6 coding sequence . Clone Px8d14 was identified , sequenced and assembled into 7 ordered fragments covering >126 kb . The predicted full length nAChR Pxα6 coding sequence was identified , based on homology with B . mori ( GenBank ABV45518 ) , spanning twelve exons plus the alternative exon versions 3a , 3b , 8b and 8c reported from other insects . The full-length gene from start methionine to stop codon spanned >75 kb of the 126 kb BAC clone ( GenBank GU058050 , Figure 3A ) . To verify the coding sequence annotation , primers were designed in predicted 5′ and 3′ untranslated regions and amplified from cDNA of a 4th instar Geneva88 larva using a proof reading polymerase ( GenBank GU207835 , Figure 3B ) . The predicted protein sequence of the full length product was 96% , 96% and 83% similar to nAChR α6 orthologues of B . mori ( ABP96888 ) , H . virescens ( AAD32698 ) and D . melanogaster ( NP_723494 , isoform A ) respectively . Exons 2–12 of the Pxα6 were PCR amplified with gene specific primers using cDNA generated from total RNA of 4th instar spinosad susceptible ( Geneva 88 ) or spinosad resistant ( a backcross bioassay survivor ) larvae . Products were excised from agarose gels ( ≈1500 bp ) , purified and reamplified with a nested reverse primer , ( also within exon 12 ) and cloned . All 9 clones sequenced from Geneva88 ( plus single clone sequenced from exons 1–12 ) contained the full complement of exons , and all 10 clones from BCS3-Pearl contained in addition , a frame-shifting 40 bp insertion between exons 9 and 10 creating a premature stop codon in resistant larvae ( GenBank GU060294–GU060298 ) . Genomic DNA of the BCS3-Pearl grandfather , used to generate the resistance-mapping crosses , was PCR amplified across the Pxα6 40 bp insertion , cloned and sequenced ( GenBank GU060290 ) . Intron 9 was approximately 6 kb shorter ( 1515bp in BCS3-Pearl compared to 7748 bp in Geneva88 ) , and contained a point mutation at the 5′ donor site ( GT changed to AT ) . Comparison with the BCS3-Pearl cDNA sequence indicated that intron splicing occurred after 40bp , at a second “GT” splice-site , not found in Geneva88 ( Figure 4 ) . This mutation has marked effects on the protein sequence and predicted transmembrane topology of the Pxα6 subunit . Although leaving the third transmembrane segment TM3 intact , it removes the 148-aa cytoplasmic loop and the 19-aa TM4 and short extracellular carboxy-terminus . No functional variants of nAChR subunits lacking the cytoplasmic loop or TM4 are known . Considerable splice-form variation has been reported in nAChR α6 orthologues from other insect species , and this was further confirmed here for P . xylostella . Six out of 10 Geneva88 clones contained an additional 30 bp at the acceptor site of intron 10 , which added 10 amino acids to the subunit , between TM3 and TM4 . The identical 30 bp sequence was observed in BCS3-Pearl genomic DNA , but not in any of the sampled mRNA molecules . Geneva88 clones also incorporated either exon 3a or exon 3b ( 4 and 6 clones respectively ) , while all 10 BCS3-Pearl clones expressed exon 3a . Additional clone sequencing using primers positioned in the 5′ and 3′ untranslated regions confirmed the presence of exon 3b in resistant insects ( GenBank GU207836 ) . Thirty synonymous single nucleotide polymorphisms ( SNPs ) were identified within or between Geneva88 and the bioassay survivor ( Table S1 ) , excluding exon 3a and 3b splice variants and exon 5 A-to-I editing sites ( see below ) . There was no clear correlation between the different splice variants described , either the additional 30 bp and exon 5 editing , the synonymous SNP variants or the alternative forms of exon 3 seen in Geneva88 . The observation of splicing mutations at intron 9 in the resistant strain and splicing variants of exon 11 in the susceptible strain prompted further investigation of transcripts of this specific gene region . cDNA from a resistant and a susceptible 4th instar were PCR amplified using primers in exons 6 and 12 , products column purified , reamplified with exon 7 and 11 primers and products cloned . Colonies were picked and amplified directly then carefully chosen for sequencing based upon amplicon size differences . In the susceptible strain , one additional splice form lacking exon 8b was detected , removing transmembrane domain 2 , without a change in reading frame . Three additional splice forms were identified in the resistant strain , all of which introduced in-frame premature stop codons including i ) a 4 bp insertion following the intron 9 point mutation , ii ) an exon 9 exclusion and iii ) exclusion of exons 8b plus 9 ( Figure 5 ) . To compare these splice variants in a broader sample set , cDNA from 4th instar larvae of 12 resistant siblings from a backcross and 12 susceptible individuals were PCR amplified ( as above ) and products size separated using agarose gel electrophoresis . Diverse yet reproducible Pxα6 splice patterning was observed within both resistant and susceptible larvae , however amplicon sizes differed between these groups ( GenBank GU060299–GU060305 , Figure S1 ) . A-to-I mRNA editing in exon 5 of nAChR α6 has been reported to increase subunit diversity in many insects [28] , [37] . To determine whether editing differences occur between spinosad resistant and susceptible strains , primers within exon 5 were designed for sequencing gDNA and cDNA from the same individual . Four editing sites were confirmed in both susceptible and resistant strains and , based upon the numbering system outlined in Jin et al . ( 2007 ) , sites 5 , 6 and 10 were conserved with H . virescens , B . mori and D . melanogaster while site 4 was in the same codon but edited a different non-synonymous base ( Figure S2 ) . Several classes of insecticide target nAChR's including neonicotinoids and spinosad . Recently spinosyn A , the primary component of spinosad , was shown to act independently of known binding sites on nAChRs for other compounds , including the site for the neonicotinoid insecticide , imidacloprid [44] . Orr and colleagues conclude that a novel mode of action is responsible for spinosad toxicity that does not involve known ligand binding domains . The truncation of the Pxα6 coding sequence after exon 9 in the mutant may indicate that spinosad is interacting with the wild type nAChR molecule at the intracellular receptor loop between TM3 and TM4 , which is removed by this truncation . These loops are thought to be involved with receptor biosynthesis and assembly , and can affects the rate at which current flows through the receptor's channel [45] . Alternatively , spinosad may interact with the extracellular carboxy-terminus of the protein , although this seems unlikely as only 8 amino acids are predicted outside the membrane . Both regions are also deleted in the Drosophila spinosad-resistant CyO allele of Dα6 , as well as TM3 , due to the occurrence of one of the CyO inversion breakpoints within exon 8b . Thus any protein expressed by this Drosophila strain would lack the TM3 and downstream domains . Alternatively , transcripts with truncated CDS may produce entirely non-functional proteins , or the transcripts may be degraded through non-sense mediated decay . Whatever the exact mechanism , the high levels of resistance conferred by both the resistance mutation identified here in Plutella , and the truncation mutation previously identified in Drosophila , indicates that the nAChR α6 subunit is the prime target of spinosad action . Insect nAChR genes can exhibit extensive splice-form variation and other post-transcriptional modification . Notably , frameshifts caused by alternate exon splicing or incorrect intron splicing have been reported in nAChRs from T . castaneum , A . mellifera and D . melanogaster and Anopheles gambiae [27]–[29] . It is unclear whether these shortened fragments have a functional role , however they are likely to have a profound effect on channel properties [25] . It has been suggested that alternatively spliced products of nAChR genes may act as acetylcholine “sponges” , or influence expression of full-length transcripts [25] , [28] . The presence of truncated protein molecules in wild-type genetic backgrounds may suggest that these are only mildly deleterious , and perhaps might indicate that the recessive resistance allele could have been present even before the advent of spinosad insecticides . This may explain the rapid appearance of resistance in Plutella . To search for additional mis-splice mutations , Pxα6 exons 7 to 11 were amplified . Multiple frameshift mutations were identified in a resistant larva due to the presence of the intron 9 point mutation or complete exclusion of the mutation containing exon . In contrast , all transcripts sequenced from susceptible larvae maintained the correct translational reading frame . It is interesting to note , that in the housefly , sequence variation in subunit Mdα6 did not show an association with laboratory generated spinosad resistance . Nonetheless , a single Mdα6 clone showed a similar frameshift mutation , due to incorrect splicing of intron 9 , a mutation in the same gene region as shown here in Pxα6 [31] . Whether this region of the gene is prone to mutations remains unclear , however , we speculate that similar resistance mechanisms as those described in Plutella could arise in other insects experiencing similar selective pressures . Although there may be a fitness cost associated with resistance [46] , full length transcripts of the α6 gene are apparently not necessary for survival . High levels of protein sequence identity across insect orders would seem to indicate strong stabilising selection on protein function . However , spinosad resistant strains of Plutella xylostella have survived under laboratory conditions for more than 7 years , although costs of resistance may not be fully expressed in laboratory conditions . Whatever is the case , knockout or truncation mutations are not particularly common causes of field evolved insecticide resistance , presumably as insecticide target molecules are generally , almost by definition , functionally important and therefore knockout mutations in target molecules will tend to be lethal . However , the existence of several genes encoding nAchR α-type subunits may allow for some functional redundancy , if another subunit can be recruited to substitute for a defective Pxα6 protein . It will clearly be interesting to further investigate how and when this truncation mutation in Pxα6 arose , its molecular mode of action in conferring resistance , and to identify any associated fitness costs . Identification of the molecular changes in the Pxα6 gene associated with resistance is a key step towards all of these goals . The spinosad susceptible strain of P . xylostella , Geneva 88 , was collected from Geneva , NY in 1988 and maintained on artificial diet without insecticide exposure . The spinosad resistant strain Pearl-Sel was collected from Oahu , Hawaii in 2001 and was 1080 fold resistant to spinosad at generation F5 [5] . Selection of Pearl-Sel with spinosad under laboratory conditions increased the toxicity ratio to 18 , 600 fold . Pearl-Sel was crossed to Geneva 88 for two generations , selected for survival on artificial diet for laboratory rearing , then backcrossed to Geneva 88 for three times and re-selected for spinosad resistance , resulting in BCS3-Pearl used in this study . Spinosad bioassays were prepared by soaking artificial diet in liquid spinosad ( SpinTor 2 SC ) for two hours , excess fluid drained , and residual droplets air dried . Second instar larvae were used in bioassays and reared on diet containing insecticide until pupation . Prior to mapping crosses , BCS3-Pearl larvae were treated with a diagnostic dose ( 10 ppm ) of spinosad . Single pair matings were established between a BCS3-Pearl male and Geneva 88 female . Some F1 individuals were bio-assayed to confirm that resistance was recessive . Single pair backcrosses were then established between a BCS3-Pearl male and F1 female . Some backcross progeny were reared to adult then 32 untreated controls frozen ( −80°C ) while ∼70 of the progeny were treated with a diagnostic dose of spinosad and 35 survivors frozen . A second series of crosses were established for male informative crosses for recombinational mapping . Male informative mapping families were generated from 31 F1 brothers who were backcrossed to BCS3-Pearl females in single pair matings . Bioassays were performed using 15 ppm spinosad , and produced 2315 survivors that were related by a single grandparental cross . Genomic DNA extraction procedures were performed according to Zraket et al . ( 1990 ) [47] . Total larval RNA was extracted using RNeasy kit ( Qiagen ) . Reverse transcription of total RNA was performed with BioScript ( Bioline ) using a random hexamer ( 0 . 2 µg ) . AFLPs were performed on 100–200 ng of genomic DNA according to Vos et al . ( 1995 ) using 11 primer combinations with three selective bases ( EcoANN-MseCNN ) [48] . AFLP Eco primers were labelled with γ-32P or γ-33P and separated on 6% polyacrylamide gels and exposed on X-OMAT film ( Kodak ) for 1 to 7 days depending on the strength of the isotope . AFLP bands were analysed manually . MapMaker v2 . 0 was used to assemble raw AFLP data into linkage groups function with LOD ≥3 . 00 and θ≤0 . 40 , using both genotype phases . Specific primers were designed using Oligo 6 . 4 ( Molecular Biology Insights ) or Primer3 [49] ( Table S2 ) . PCR reaction volumes were between 10µl and 50µl using Taq polymerase ( Bioline ) with final reaction concentrations: buffer ( 1× ) , MgCl2 ( 2 mM ) , dNTP ( 0 . 1 mM ) , primer ( 0 . 2 mM ) , Taq polymerase ( 0 . 5 units ) . Extensor enzyme ( Thermo Scientific ) was used for genomic DNA and cDNA clone amplification . Template concentrations ranged from 3ng–100ng of genomic DNA and 1–2 µl of cDNA template generated from reverse transcription reactions . Clones were obtained by ligating PCR products into pGEM T-easy vector system ( Promega , WI , USA ) or CopyControl ( cambio ) . DNA sequencing reactions were prepared using Big Dye 3 . 1 and sequenced using a 3730×l Capillary Sequencer ( ABI ) . Sequence analysis was performed using CodonCode Aligner . Multiple cDNA clones were sequenced from single individuals to distinguished polymorphic sites from cloning errors . The sequences reported in this paper have been deposited in the GenBank database ( GU058050 , GU207835 , GU207836 , GU060290–GU060305 ) . Degenerate primers were designed by aligning nAChR α6 protein sequences with MacVector 7 . 0 ( Accelrys ) [H . virescens ( AAD32698 ) , D . melanogaster ( Q86MN8 ) , B . mori ( ABV45518 ) , A . gambiae ( XP_308042 ) ] . Genotyping was performed using PCR amplification and agarose gel electrophoresis for a female informative cross with PxDα6 primers Pxα6_ex7_F×Pxα6_ex8_R . In male informative crosses , Pxα6_Intron5F×Pxα6_Intron5R was digested with BsrG1 ( NEB ) and Pxα6_ex11_F×Pxα6_ex12_R digested with AluI ( NEB ) . The location of nAChR a6 was identified in the genome of Bombyx mori ( silkdb , nscaf2838 ) and flanking genes were BLAST against P . xylostella 454-ESTs to obtain gene specific sequence . PPTSR ( GenBank GU060291 ) was amplified with PPTSR_F , PPTSR_R and digested MscI ( NEB ) and arginine kinase ( GenBank GU060292 ) using ArgKin_F×ArgKin_R , digested with Taq alpha1 ( NEB ) . Messenger RNA was purified from Geneva 88 eggs and all larval stages using TRIzol reagent ( Invitrogen ) and larval midguts by the RNeasy MinElute Clean up Kit ( Qiagen ) . Genomic DNA was removed by incubation with DNAse ( TURBO DNAse , Ambion ) for 30 min at 37°C . RNA integrity and quantity was verified on an Agilent 2100 Bioanalyzer using the RNA Nano chips ( Agilent Technologies ) and Nanodrop ND-1000 spectrophotometer . Full-length enriched , normalized cDNAs were generated from 2 µg of total RNA using the Creator SMART cDNA library construction kit ( BD Clontech ) . Reverse transcription was performed with a mixture of several reverse transcription enzymes for 60 min at 42°C and 90 min at 50°C . Double-stranded cDNAs were normalized using the trimmer-direct cDNA normalization kit ( Evrogen ) to reduce abundant and increase rare transcripts . This normalized larval cDNA was used as a template for 454-FLX sequencing which resulted in a total of 68 . 9 Mb from 315367 reads , clustered into 19 , 309 contigs using Newbler software ( Liverpool , UK ) . A P . xylostella genomic BAC library was constructed using Geneva 88 after partial digestion with restriction endonuclease MboI and ligating into vector pIndigoBAC536 ( Clemson University Genomics Institute ) . The average insert size was 109 . 4 kb which provided 7 . 6× genome coverage from 23 , 808 clones . A nAChR Pxα6 sequence amplified from cDNA ( primers Pxα6_ex7_F×Pxα6_ex11_R ) was 33P labelled using Prime-a-Gene labelling kit ( Promega ) and used to screen the library . Five clones were identified ( Px7p6 , Px8d14 , Px10h8 , Px14d18 , Px17d20 , where Px = Plutella xylostella , followed by plate number and grid position ) and Px8d14 selected for sequencing ( GenBank GU058050 ) . Clone annotation was performed using the B . mori annotation program KAIKOGAAS ( http://kaikogaas . dna . affrc . go . jp/ ) and BLASTn searching against P . xylostella 454-ESTs . The BCS3-Pearl grandfather used to produce all male informative mapping families was PCR amplified with primers Pxα6_ex9_F×Pxα6_ex10_R and Pxα6_ex10_F×Pxα6_ex12_R and assembled into a single sequence ( GenBank GU060290 ) . PCR primers predicted to be within nAChR Pxα6 5′ and 3′ untranslated mRNA regions ( Pxα6_5prime_F1×Pxα6_3primeR1 ) were used to amplify a product from Geneva 88 with Extensor polymerase ( GenBank GU207835 ) . SignalP 3 . 0 predicted the signal peptide cleavage site [50] , transmembrane domains predicted with TMpred program ( http://www . ch . embnet . org/software/TMPRED_form . html ) and ProSite identified the neurotransmitter gated ion-channels signature [51] ( Figure 3 ) . A single 4th instar backcross ( R ( RxS ) ) larvae that survived a spinosad bioassay and a single Geneva 88 4th instar larva were amplified with primers in exon 2 ( Pxα6_ex2_F ) and 12 ( Pxα6_ex12_R3 ) . Products were excised from 1 . 5% agarose gel and re-amplified with the same forward primer and slightly nested reverse primer , also in exon 12 ( Pxα6_ex12_R2 ) . dATP overhangs were added and products cloned into pGEM-t-Easy vector . Nine clones from G88 and 10 clones from BCS3-Pearl were amplified with proof-reading taq polymerase and sequenced with vector primers ( T7 and SP6 ) plus one internal primer located within exon 6 ( Pxα6_ex6_F ) ( GenBank GU060294–GU060298 ) . nAChR Pxα6 was amplified from cDNA of multiple Geneva 88 and BCS3-Pearl larvae with exon 6 and 12 primers ( Pxα6_ex6_F×Pxα6_ex12_R ) , products were purified using MinElute columns ( Qiagen ) then reamplified using exon 7 and 11 primers ( Pxα6_ex7_F×Pxα6_ex11_R ) . One individual from each strain was cloned and sequenced ( GenBank GU060299–GU060305 ) , and remainder run on agarsoe gel ( 1 . 5% , 12 hour 50 volts ) .
Evolving resistance to control agents , such as antibiotics or insecticides , can have major costs to human health or agricultural food production . Once a genetic mechanism for resistance to a particular compound has been identified , other resistant species can be rapidly assessed to search for a parallel mechanism . Insecticides often target the insect nervous system as they can be toxic at low concentration and act rapidly . Here we report a genetic mutation in a global agricultural pest , diamondback moth , that is associated with resistance to the bioinsecticide spinosad . A mutation in an intron splice junction of nicotinic acetylcholine receptor ( nAChR ) alpha 6 causes mis-spliced mRNA transcripts that are predicted to produce truncated proteins lacking important functional domains . nAChRs require 5 subunits to function , and insects generally encode 10–12 subunit genes . Spinosad may therefore be targeting a redundant nAChR subunit not essential for survival in diamondback moth . Other insects that evolve field resistance to spinosad can now be tested to determine whether the same resistance mechanism is involved .
You are an expert at summarizing long articles. Proceed to summarize the following text: Telomerase-negative yeasts survive via one of the two Rad52-dependent recombination pathways , which have distinct genetic requirements . Although the telomere pattern of type I and type II survivors is well characterized , the mechanistic details of short telomere rearrangement into highly evolved pattern observed in survivors are still missing . Here , we analyze immediate events taking place at the abruptly shortened VII-L and native telomeres . We show that short telomeres engage in pairing with internal Rap1-bound TG1–3-like tracts present between subtelomeric X and Y′ elements , which is followed by BIR-mediated non-reciprocal translocation of Y′ element and terminal TG1–3 repeats from the donor end onto the shortened telomere . We found that choice of the Y′ donor was not random , since both engineered telomere VII-L and native VI-R acquired Y′ elements from partially overlapping sets of specific chromosome ends . Although short telomere repair was associated with transient delay in cell divisions , Y′ translocation on native telomeres did not require Mec1-dependent checkpoint . Furthermore , the homeologous pairing between the terminal TG1–3 repeats at VII-L and internal repeats on other chromosome ends was largely independent of Rad51 , but instead it was facilitated by Rad59 that stimulates Rad52 strand annealing activity . Therefore , Y′ translocation events taking place during presenescence are genetically separable from Rad51-dependent Y′ amplification process that occurs later during type I survivor formation . We show that Rad59-facilitated Y′ translocations on X-only telomeres delay the onset of senescence while preparing ground for type I survivor formation . Telomeres are nucleoprotein structures found at the physical ends of chromosomes . Their terminal location defines their two main functions: protection of the chromosome ends from illegitimate repair reactions and prevention of the loss of terminal DNA due to either degradation or incomplete replication [1] . In Saccharomyces cerevisiae , the first function is accomplished primarily by Rap1 , which wraps tandem telomeric DNA repeats to inhibit NHEJ [2] , and recruits Rif1 and Rif2 to restrain MRX-mediated 3′ end resection [3] , [4] , thus limiting recruitment of HR factors and checkpoint signaling [5] . The second function is mediated by Cdc13 bound to the single-stranded G-rich 3′ overhang at the extreme terminus of a telomere . Cdc13 forms alternative complexes with either Est1 or Stn1-Ten1 to coordinate telomerase-mediated synthesis of the G-rich strand with the synthesis of the complementary strand by DNA polymerase α [6] . As in mammals , telomeres in yeast cells with reduced telomerase activity progressively shorten with each cell division until they are recognized as DNA damage and recruit Mec1 kinase that initiates irreversible G2/M arrest [7]–[9] . At the level of cell population , telomere dysfunction is manifested as crisis , when majority of the cells irreversibly arrest in G2/M [7] . Most of the cells die , but at low frequency survivors emerge , which maintain their telomeres via recombination [10] , [11] , implying that homologous recombination ( HR ) can serve as a bypass pathway to sustain viability in the absence of telomerase . The survivors are classified in two types based on their telomere arrangement and growth characteristics [12] , [13] . The type I survivors have tandem arrays of subtelomeric Y′ elements separated by short tracts of TG1–3 repeats at most chromosome ends , and also short terminal TG1–3 repeats [10] . Their growth is interrupted by frequent periods of arrest and in the competitive conditions of liquid culture they are outcompeted by the more robust type II survivors . In type II survivors , terminal TG1–3 repeats are abnormally elongated and are very heterogeneous in length . It is believed that they are established by stochastic lengthening events that likely involve rolling circle replication [14] . RAD52 is required for generation of both types of survivors . RAD51 , RAD54 , RAD57 are specifically required to generate type I , whereas type II survivors depend on MRX complex , RAD59 and SGS1 , encoding the only RecQ helicase in yeast [13] , [15] , [16] . In addition , POL32 encoding a non-essential subunit of DNA polymerase δ is required for generation of both survivor types , implying involvement of the processive repair DNA synthesis in the recombination-based telomere rearrangements [17] , [18] . Recently , a genome-wide screen aimed to identify telomere-length-maintenance genes that regulate telomere structure in post-senescence survivors unveil new regulators of Type I and II recombination [19] . Notably , Type I recombination was shown to depend on the helicase Pif1 and on the chromatin remodelling complex INO80 . Although genetic requirements for the formation of two types of survivors and their telomere patterns have been well characterized , much less is known about actual recombination events that lead to reorganization of the original short telomere into the patterns observed in survivors . In budding yeast , telomere shortening does not cause end-to-end chromosome fusions , as does the removal of Rap1 from telomeres [2]; instead , gene conversion increases near short telomeres indicating de-repressed recombination [20] . There is a controversy , however , whether type II recombination preferentially takes place at long or short telomeres [14] , [21] , [22] . Little is known about the telomere length preference of type I pathway . Early studies looking at the propagation of linear plasmids in yeast uncovered that they can recombine with the yeast chromosome ends and acquire telomere-adjacent sequences called Y′ elements [23] . Y′ elements found at many chromosome ends fall into two size classes , 6 . 7 ( Y′-L ) and 5 . 2 ( Y′-S ) kb-long , that differ by a 1 . 5 kb insertion/deletion [24] . Another subtelomeric sequences called X elements are present at all chromosome ends immediately proximal to either Y's or terminal TG1-3 repeats when Y′ is absent . The junction between X and Y′ elements often , but not always , contain short tracts of TG1–3 repeats [24] , [25] . Importantly , only the Y′ and not X elements can be transferred on linear plasmids , and this is mediated by recombination between the terminal TG1–3 repeats added onto the plasmid ends by telomerase and the internal TG1–3 tracts present between the X and Y′ elements [23] . Pioneering work of Lundblad and Blackburn showed that est1Δ survivors arose as the result of the acquisition of Y′ elements by X-only telomeres and amplification of these elements on many chromosome ends . They proposed a model of telomere rescue via a recombination event between the terminal TG1–3 repeats of one telomere and an internal TG1–3 tract in another [10] . We have previously demonstrated using single cell analysis that Rad52-containing foci are assembled at the telomeres in a length-dependent manner in presenescent cells many generations before the onset of senescence [26] . The recruitment of recombination factors to short telomeres is in accord with increased recombinogenic activity of short telomeres observed in both yeast [20] and mammals [27] . Of note , inactivation of HR , particularly via deletion of RAD52 and RAD51 ( but to a much lesser extent of RAD59 ) , causes early decline in proliferative capacity of telomerase-negative yeast indicating that telomere maintenance most likely becomes dependent on HR soon after telomerase inactivation [28] , [29] . Surprisingly , the rate of telomere shortening ( population average length ) is unaffected in HR-deficient yeast . All these observations raise the question of telomere recombination dynamics in presenescent cells , the mechanism of Y′ acquisition by X-only telomeres and the role of recombination proteins in maintaining telomerase-negative strains alive during presenescence . Another unresolved issue is whether a single critically short telomere is sufficient to induce cell cycle arrest . Complete loss of a single telomere causes Rad9-dependent arrest even in telomerase-proficient cells [30] . This does not seem to be the case when a very short telomere is created in telomerase negative cells [8] , [28] , but the fate of this abruptly shortened telomere remains obscure . In this study , we aimed to characterize the primary recombination event that takes place at short telomeres in the absence of telomerase . To this end we put together a system to simultaneously shorten modified VII-L telomere and inactivate telomerase . Bulk liquid cultures turned out to be inappropriate to address the fate of the abruptly shortened VII-L telomere , so we adapted clonal analysis . We found that the subtelomereless VII-L end acquired Y′ element in clonal populations originated from transiently arrested cells . Cloning and sequencing of the Y′ translocation junctions from multiple clones revealed that Y′ acquisition was initiated by recombination between the short terminal TG1–3 repeats at VII-L and the Rap1-bound internal TG1–3-like tracts present between X and Y′ elements on other chromosome ends . Such recombination initiates Pol32-dependent BIR , which results in non-reciprocal translocation of the entire Y′ element and terminal TG1–3 tract from the chromosome-donor onto the shortened telomere . Surprisingly , Y′ translocation events were Rad51-independent , but were instead promoted by Rad59 that stimulates Rad52 strand annealing activity . We found that the same mechanism operates at short native X-only telomeres , but it is much more efficient since translocated Y's are readily detectable in bulk liquid cultures during presenescence . In addition , sequence composition of the translocation junctions is simpler at native telomere VI-R , indicating that Y′ translocation on a native end is relatively straightforward event . We further show that RAD59 deletion compromises the efficiency of Y′ translocation on native telomere XV-L , and results in both accelerated senescence and prolonged crisis . Our results extend the model of short telomere rescue proposed by Lundblad and Blackburn more than 20 years ago [10] , and they reinforce the notion that it is the overall depletion of the TG1–3 repeats on multiple chromosome ends rather than abrupt shortening of a few telomeres that defines the onset of senescence . To address the processing of a short telomere without Y′ elements in the absence of telomerase , we employed the site-specific recombination system to induce abrupt shortening of a single telomere [31] . In this system , Cre induction causes excision of the basal portion of the telomere VII-L ( TelVII-L ) flanked by loxP sites ( Figure 1A ) . In the presence of telomerase , shortened telomere is extended until its length returns to equilibrium [32] . To examine how this telomere will be processed in the absence of telomerase , we combined the abrupt shortening of TelVII-L with an inducible deletion of the plasmid-borne EST2 [33] . As expected , inactivation of telomerase completely abolished elongation of the TelVII-L after it was shortened via Cre-loxP recombination . Instead , its length decreased further until the bottom of the telomere length distribution reached a defined limit beyond which no shortening was observed ( Figure 1B ) . The lower tail of the TelVII-L length distribution in the control strain also reached the same limit albeit with a delay of ∼20 population doublings ( PDs ) consistent with its greater initial length . We estimated that the lower limit of the TelVII-L length distribution corresponds to ∼60 bp of TG1–3 repeats ( Figure S1 ) . Since the probe anneals to the unique sequence of the terminal PacI fragment ( Figure 1A ) , even complete loss of TG1–3 repeats should not affect hybridization signal . Thus , we reasoned that shortening of the TG1–3 tract beyond 60 bp causes the elimination of cells with critically short telomeres from the exponentially growing culture propagated via serial dilutions . To isolate the cells undergoing cell cycle arrest due to TG1–3 tract shortening beyond the 60 bp threshold , we conducted clonal analysis of the telomerase-negative cultures at ∼15 PD after Cre induction . To this end , single cells were micromanipulated on a grid of agar , and analyzed for their ability to form microcolonies . While many cells divided regularly , at least once every 2 hours , and formed microcolonies of more than 16 cells after 8 hours on agar , a fraction of cells never divided during this time or stopped dividing at the 2- or 4-cell stage . These arrested microcolonies were marked ( Figure 2A ) . Unexpectedly , most of the cells , which initially failed to divide , formed colonies after four days at 30°C ( Figure 2A ) . Therefore , the majority of cells was able to overcome cell cycle arrest and resumed divisions . The fraction of arrested cells was significantly greater in the strain with shortened TelVII-L compared to the control strain ( Figure 2B and Figure S2 ) indicating that shortening of a single telomere aggravated the effect of telomerase inactivation on cell cycle progression . The state of the TelVII-L in clonal populations was analyzed by Southern blotting ( Figure 2C ) . The terminal VII-L fragments with typical smeary appearance were detected in the expected size range for the control ( no arrest ) clones . In contrast , most of the clones that had undergone transient arrest completely lost the VII-L signal in this range . Instead , much larger fragments hybridized with VII-L-specific probe ( Figure 2C ) , suggesting that VII-L end has been rearranged . These larger fragments grouped in two size classes after digestion with either PacI or MfeI , and were remarkably consistent with Y′ element translocation ( see schematic in Figure 2C ) . Grouping of the fragments in two size classes could be well explained by translocation of either long ( Y′-L ) or short ( Y′-S ) version of the Y′ elements , which differ by a 1 . 5 kb insertion/deletion [24] . Most of the clones showed the presence of both Y′ classes . This heterogeneity is likely created due to independent acquisition of Y′ elements by TelVII-L in different cells ( 2- or 4-cell stage arrest ) , or even by two sister TelVII-L chromatids within a G2-arrested cell . Thus , we were able to isolate subclones descended from single recombination events ( group A in Figure S2 ) by sequential micromanipulation of the cells as they came out of the arrest . Those transiently arrested clones that did not show VII-L end rearrangement ( Figure 2C and Figure S2 ) could have either repaired it by addition of the terminal TG1–3 repeats [22] or arrested due to shortening of one of the native telomeres . To verify the hypothesis of Y′ translocation and the kinetics of this repair process , we designed primer pairs to amplify the putative junction region between the VII-L end and the Y′ element ( Figure 3A , Table S1 ) . We analyzed the presence of the junction PCR product at 0 , 10 , and 50 PDs after inactivation of telomerase in the strain bearing a critically short telomere ( 16 Rap1-bs ) and in the control strain ( 0 Rap1-bs ) ( Refer to Figure 1B ) . We detected the junction-specific PCR product at VII-L ( Figure 1A ) as early as 10 PD in liquid culture after inactivation of telomerase and telomere shortening . The intensity and heterogeneity of the junction PCR product increased dramatically by 50 PD ( Figure 3B ) . Moreover , the junction PCR product was more intense at 10 PD in the strain with short telomere compared to that in the control strain . When the same PCR approach to detect Y′ translocation was performed on the native VI-R end , the appearance of recombined telomere followed the same kinetics in the “0” and “16 Rap1-bs” strains ( see further ) . To assess the relationship between telomere length and Y′ acquisition in a more quantitative fashion , we analyzed the frequency of Y′ translocation on TelVII-L by Southern blotting in the random clones isolated from “0” and “16 Rap1-bs” strains . This analysis revealed significantly greater fraction of clones with Y′ element translocated on TelVII-L in the “16 Rap1-bs” ( 10/18 ) as compared to that in “0 Rap1-bs” ( 1/19 ) strain when the clones were isolated at 18 PD after Cre induction ( Fisher's exact test P value 0 . 001; Figure S2 ) . The same tendency was observed when the clones were isolated at 12 PD after Cre induction , but the frequency of Y′ translocation at this earlier time was too low for statistical evaluation ( 0/24 in “0 Rap1-bs” and 3/24 in “16 Rap1-bs” strain ) . In addition , the fraction of “16 Rap1-bs” clones with Y′ translocated on TelVII-L was significantly greater when the clones were isolated at 18 as compared to 12 PD after Cre induction ( Fisher's exact test P value 0 . 006; Figure S2 ) . The frequency of Y′ translocation among “0 Rap1-bs” clones was still low at these time points for statistical evaluation . We concluded that Y′ element is preferentially translocated on short telomeres; and the fraction of cells with translocated Y′ element grows with time after telomerase inactivation . Cloning and sequencing of the junction PCR products revealed that they are in fact composed of the joint VII-L and Y′ element sequences , thereby confirming Y′ translocation on the VII-L end . Remarkably , all clones contained the TG1–3 repeats at the breakpoint between the VII-L and Y′ sequences , consistent with our assumption that Y′ translocation was initiated by VII-L terminal repeats , which recombined with internal TG1–3 tracts located between the subtelomeric X and Y′ elements of the chromosome donor . Sequence analysis of the Y′ segments of the junction clones revealed that the choice of the donor for Y′ translocation was not random: the chromosome arms VI-L and VII-R were used most frequently as the donors ( Table 1 ) . To characterize recombination breakpoint we searched for the point of sequence divergence within TG1–3 repeats at the VII-L and Y′ junction . The exact pattern of irregular repeats newly synthesized by yeast telomerase differs between the molecules [34] . When telomere repeat sequences from clonal populations are aligned , only very distal 40–100 nt-long portions maintained by telomerase are divergent , whereas proximal regions are identical in sequence [35] . The divergent distal portion is quickly lost after telomerase inactivation . We found that TG1–3 repeats adjacent to the VII-L end shared 47±18 ( SD ) nt of identity before they diverged ( Figures 4 and S3A ) , indicating that VII-L telomeres were at least that long at the time they engaged in recombination . Only in a few clones , the TG1–3 sequence past the divergence point continued without interruption by repeats found proximal to the acquired Y′ element ( Figure S3B ) . In most of the clones , however , the repeats of the VII-L and Y′ origins were separated by divergent TG1–3 repeats ( Figure 4 ) . These intervening TG1–3 tracts varied in length and sometimes were quite long ( e . g . 255 bp in the clone H10 ) . We speculate that they might have resulted from abortive DNA repair synthesis due to rejection of homeologous heteroduplexes formed by TG1–3 repeats followed by template switch events [36] . Otherwise , the divergent regions at the junctions could be generated via recombination between two terminal TG1–3 repeat tracts , or even by residual telomerase activity during the first few PDs after Cre induction . To get insight into the mechanism of short telomere repair , we examined its genetic requirements . To this end , we generated a set of isogenic mutants by deleting RAD52 , RAD51 , and RAD59 genes in a strain with the VII-L telomere modified for inducible shortening and TLC1 allele with a tetracycline-regulatable promoter ( tetO2-TLC1 ) . Upon addition of doxycycline ( Dox ) , expression of TLC1 is tightly repressed and telomeres shorten progressively with each generation ( [26] and Figure S4 ) . We first examined the effect of gene deletions on the efficiency of Y′ translocation by semi-quantitative PCR assay . Telomerase was inactivated by addition of Dox and abrupt shortening of the VII-L was then induced following the scheme in Figure 5A . We analyzed junction PCR products in clonal populations of HR-proficient or mutant cells ( Figure 5B , C and Figure S5 ) . As expected , robust junction PCR products indicating Y′ translocation events were detected in the clonal populations of HR-proficient cells ( Figure 5B ) . Weaker product was also detectable in the mixed population of cells grown in liquid culture ( Bulk ) . RAD52 deletion nearly completely abolished Y′ translocation as judged by severe reduction of the amplified VII-L/Y′ junctions in either mixed or clonal populations ( Figure 5B ) . Thus , Rad52 is essential for recombination between TG1–3 repeats that leads to Y′ translocation . Surprisingly , deletion of the RAD51 had little effect on the efficiency of Y′ translocation ( Figure 5C , top panel ) , demonstrating that Rad51 may not be essential for recombination between TG1–3 repeats . In contrast , RAD59 deletion reduced both the amount and the heterogeneity of amplified VII-L/Y′ junctions ( Figure 5C , midpanel ) suggesting lower frequency of TG1–3 repeat recombination in the absence of Rad59 . These results indicate that Rad51 filament assembly may not be required at the 3′ overhang of the short telomere , which pairing with an internal tract of the TG1–3 repeats could depend only on Rad52 strand annealing activity which is stimulated by Rad59 . While Y′ translocation is initiated by TG1–3 repeat recombination , its completion likely depends on break induced replication ( BIR ) , a pathway used to repair a DSB when homology is limited to its one side [37] . Deletion of POL32 , encoding a nonessential subunit of Polδ required for processive DNA synthesis during BIR [38] , severely reduced the efficiency of Y′ translocation as evidenced by drastically reduced junction PCR product ( Figure 5C , bottom panel ) . Notably , the PCR at the VII-L terminus , which served as a loading control , failed for two transiently arrested pol32Δ clones ( clones c and d ) implying disappearance of the primer ( s ) site from the VII-L terminus . Therefore , we verified the integrity of the VII-L terminus in pol32Δ clones by Southern blot and found that it was indeed rearranged in both clones in a similar manner , which is different from Y′ translocation ( Figure S6 ) . We failed to detect Y′ translocation in any of the pol32Δ clones that we have analyzed by Southern blot . The junction PCR product that is reduced but still detectable in pol32Δ clones could have been generated on single strand extension products that were terminated before reaching chromosome end . Thus , we concluded that Pol32-dependent BIR is essential for the completion of Y′ translocation . To quantify the Y′ translocation efficiencies in the aforementioned deletion mutants , we employed real-time qPCR using two pairs of primers to amplify either the total amount of VII-L or the VII-L/Y′ junctions ( Figure 5D ) . For this analysis we used DNA extracted from random clones isolated at ∼16 PD after Cre induction which were also analyzed by Southern blotting with VII-L-specific probe ( Figure S7 ) . We performed clonal analysis because it provides an unbiased snapshot of the repair frequencies at any given time , whereas bulk liquid cultures of telomerase-negative cells are strongly affected by selection for best-growing clones . We estimated that at the time of analysis ( ∼40 PD after Cre ) on average 43 . 5% of VII-L ends in wild-type clones acquired Y′ element . The fraction of “repaired” VII-L was reduced to 18 . 2 and 6 . 6% in rad51Δ and rad59Δ , respectively , whereas it was at the background level in both rad52Δ and pol32Δ clones ( Figure 5D ) . We inferred from these results that Rad52 predominantly cooperates with Rad59 to initiate Pol32-dependent BIR which leads to Y′ translocation . Nevertheless , Rad51 also appears to contribute in this process since it reduces the efficiency of Y′ translocations by at least two fold . To show that Y′ translocation is not a unique phenomenon that takes place at the modified VII-L end , but is common for eroded X-only telomeres , we used the same PCR approach to detect Y′ translocation on the native VI-R end ( Figure S8A ) , which normally has only X element in the subtelomere region ( W303 genome sequencing project , contig 00111 ) . PCR product encompassing the junction between the VI-R end and the Y′ element was already detectable at 10 PD and increased further by 50 PD after telomerase inactivation ( Figure S8B ) . Thus , erosion of the native telomeres in the absence of telomerase also leads to Y′ element translocations . Sequence analysis of the cloned PCR fragments confirmed that the VI-R/Y′ junction regions were predominantly amplified . However , we also identified a few clones resulted from mispriming on the X- and Y′-containing chromosomes V-R and XIII-L , which explains the background amplification before inactivation of telomerase . As expected , all clones contained TG1–3 repeat tracts at the junction between the VI-R end and Y′ element . Thus , similar to modified VII-L , native chromosome VI-R end also acquired Y′ element as a result of recombination between the eroded terminal and internal TG1–3 repeats . The extent of TG1–3 sequence identity at the VI-R side of the junction was limited to 60±45 ( mean ±SD ) nt ( Figure 6 ) , the minimum length of the native telomeres at the time they recombined . The divergent repeat sequence was shorter on average ( or even absent ) compared to that at the junction of modified VII-L ( Figure 6 ) . Notably , among the chromosome ends that served as Y′ donors we identified a greater variety of ends ( Table 1 ) , indicating that native telomere VI-R was less selective with respect to a Y′ donor . Of note , not all junctions between the X and Y′ elements contain perfect TG1–3 repeats , but they all tend to be G-rich and are all bound by Rap1 ( Table1 and Figure S9 ) . Since the native VI-R/Y′ junctions showed simpler composition indicative of possibly greater efficiency of Y′ translocation , we attempted to detect them during the senescence time course by Southern blot directly in the bulk liquid cultures . To this end , we performed standard senescence assay in liquid with est2Δ spore clones . The samples were collected for DNA extraction daily at the time of culture dilution . XhoI-digested DNA was then Southern blotted and probed for the native X-only XV-L end using subtelomeric probe . We were surprised to find that two high-molecular-weight bands characteristic of Y′L and Y'S translocations were readily detectable early on during the outgrowth of the est2Δ cultures ( Figure 7A , B; top panels ) . The same result was obtained with the native VI-R end ( not shown ) . As expected , the timing of the onset of Y′ translocations correlated with the initial XV-L telomere length , which differed among the spore clones . The abundance of cells with Y′ translocated on XV-L in est2Δ cultures during presenescence was consistent with the notion of greater efficiency of the Y′ element translocation on the native X-only telomere than on the modified subtelomereless VII-L end . We next asked whether the RAD59 deletion , which reduced the frequency of Y′ translocation on the modified VII-L , would also compromise ( or delay the onset of ) Y′ translocations on the native XV-L as cells progress into senescence . Using the approach described above , we detected a substantial delay in the onset of Y′ translocations in the est2 rad59Δ relative to est2Δ clones ( Figure 7 ) . In the two representative est2Δ rad59Δ clones that are shown in Figure 7 ( bottom panels ) , Y′ translocations were not detectable until the cells with very short XV-L telomere nearly disappeared from the population . In contrast , the cells with unrecombined short XV-L telomere and the cells which have already undergone Y′ translocation coexisted in the cultures of Rad59-proficient est2Δ clones long before they reached growth nadir . This result clearly points to the role of Rad59 in promoting Y′ translocation on the short native telomere . Note that in clone rad59Δ-1 , type II pattern is seen after 8 days without telomerase . We usually observe that in liquid cultures , 75% of the clones are type I while 25% display a mixed pattern of type I and II ( see Figure S11B ) . The finding of short telomere repair by Y′ translocation raised a question whether this process can delay the onset of replicative senescence . Since Y′ element translocation is facilitated by Rad59 at X-only telomeres , we assayed the effect of RAD59 deletion on the onset of proliferative decline in the absence of telomerase . To this end we performed standard senescence assays using multiple est2Δ and est2Δ rad59Δ clones . Clones lacking Rad59 lost proliferative capacity slightly earlier and exhibited fivefold lower cell densities at the nadir of growth ( Figure 8A ) . This observation indicates that Rad59-facilitated repair of critically short telomeres contributes to sustain cell proliferation particularly when a population of telomerase-negative cells approaches growth nadir . Consistent with this notion , we found by ChIP-qPCR that Rad59 associates with telomeres during presenescence . Although Rad59 association varies considerably among the telomeres , it may peak abruptly ( up to 25-fold increase ) at certain X-only telomeres when culture approaches growth nadir ( Figure S10A ) . On average , Rad59 association with terminal and internal ( between X and Y′ elements ) TG1–3 repeats increases many fold as telomerase-negative cells progress into senescence ( Figure S10B ) , highlighting the role of Rad59 in the conversion of X-only into Y′ telomeres . We next reasoned that Y′ element translocation could be the initial step of type I telomere pattern formation at X-only telomeres . This premise seemingly contradicts established genetic requirements for survivor formation since type I survivors are predominantly obtained in cells lacking Rad59 [12] , [13] , [29] . The latter is due to the fact that Rad59 deletion greatly impedes type II . However , this does not exclude a possibility that Rad59 also facilitates transition to type I , particularly at X-only telomeres , although it is not strictly required . To reveal such a role of Rad59 , we had to prevent the dominant type II pathway . This was performed by deleting SAE2 whose deletion strongly inhibits type II formation ( Figure S11 ) . We therefore compared the efficiency of type I survivor formation in est2Δ sae2Δ and est2Δ sae2Δ rad59Δ mutants ( Figure 8B ) . We found that the triple mutant clones spent longer time in crisis suggesting reduced efficiency of type I survivor formation . The involvement of RAD59 in Y′ translocation raised a question whether the other genes of the type II survivor pathway may also contribute to Y′ acquisition . To this end , we compared the kinetics of Y′ acquisition by the native telomere XV-L between est2Δ and sgs1Δ est2Δ cells . We chose to delete SGS1 , another gene required for type II survivors to arise [13] , [15] , [16] , [19] , because unlike the genes encoding the subunits of MRX complex it does not cause short telomeres , which would complicate comparative analysis of Y′ translocation kinetics since short telomeres acquire Y′ faster . We found that although est2Δ and sgs1Δ est2Δ clones started to senesce with XV-L telomere of comparable size , there was a substantial delay in Y′ translocation on XV-L in sgs1Δ est2Δ compared to est2Δ cells ( Figure 9 ) . Therefore , Sgs1 also appear to promote Y′ element acquisition . As expected , XV-L telomere converted to type I pattern by the time sgs1Δ est2Δ cells generated survivors . Remarkably , recombination between Y′ elements increases in sgs1Δ mutants [39] , thus the inhibitory effect of SGS1 deletion on Y′ translocation further highlights the mechanistic difference between largely RAD51-independent Y′ acquisition and RAD51-dependent Y′ amplification , the two steps of type I survivor formation . Since we found that short telomere repair is associated with transient arrest , we asked whether the checkpoint function is required for Y′ translocation . To address it , we deleted MEC1 , which is required for G2/M arrest in telomerase-deficient cells [40] and also mediates type II recombination [41] . As expected , mec1Δ sml1Δ mutant clones exhibited flatter senescence profiles indicative of defective cell cycle arrest in response to telomere shortening and delayed senescence as reported previously [8] , and yet Y′ translocation was not affected in any of the three clones analyzed ( Figure 9 ) . We concluded that checkpoint function is not required for Y′ acquisition by X-only telomeres . This is in contrast to deleting RAD59 and SGS1 , which both substantially delay Y′ acquisition . Many studies from several laboratories have characterized the genetic pathways contributing to survivor formation [42] . In this study we demonstrated that a single telomere that was experimentally shortened in the absence of telomerase can acquire during presenescence Y′ element along with the terminal TG1–3 repeats from other chromosome ends which terminal repeats are still sufficiently long . Sequencing of the Y′ translocation junctions clearly evidenced that Y′ translocation was initiated by recombination between the short terminal TG1–3 repeats of the TelVII-L and the internal TG1–3 tracts located between the X and Y′ elements on certain native chromosome ends thereby confirming the model of Lundblad and Blackburn [10] . In addition , our results suggest that there is a continuous repair of the shortest telomeres that delays erosion into the subtelomere and allow the cell to escape DNA damage checkpoint activation . We showed that Y′ translocation was entirely RAD52-dependent , and was further promoted by RAD59 . In contrast , RAD51 deletion had rather modest effect on the efficiency of Y′ translocation ( two fold reduction ) , and Y′ translocations on VII-L end were detectable in rad51Δ clones . Therefore , Rad51-ssDNA filament formation and strand invasion do not seem to be obligatory for TG1–3 repeat recombination; instead , it could be accomplished by Rad52 strand annealing activity that is stimulated by Rad59 . This mechanism bears strong similarities with the “short tract homology” recombination that has been described in the other context [43] . It remains possible that Rad51 affects Y′ translocations indirectly via its involvement in the recombination among the Y′ elements themselves [44] . Another question that rises with respect to Rad51-independent recombination between TG1–3 repeats is how the G-strand overhang anneals to the internal TG1–3 repeats , which are normally double-stranded . Possibly , this single-strand annealing occurs during subtelomere replication when the single strand regions are exposed [45] , particularly when the replication forks are posed at the internal TG1–3 sequences [46] . In this study , we showed that such recombination events also occurred at the native telomeres VI-R and XV-L indicating that these rearrangements are physiological in nature . We found that modified subtelomereless VII-L and native X-only telomeres behave surprisingly different with respect to the efficiency of Y′ acquisition . Low efficiency of Y′ translocation on the modified VII-L end might be explained by its poor clustering with the other telomeres due to absence of subtelomeric elements . Indeed , intranuclear localization of the truncated TelVII-L is insensitive to the absence of either Sir3 or Yku [47] , whereas these proteins participate in targeting native telomeres to the nuclear periphery [48] , [49] . Poor clustering and failure to localize to nuclear compartments which favour recombination could be responsible for low efficiency of Y′ translocation and may explain why single experimentally shortened telomere accelerates senescence [8] , [28] . Our results suggest that the Rad59-facilitated recombination between the terminal and internal TG1–3 repeats could be responsible for initial spreading of certain Y′ elements on X-only telomeres . We demonstrated that disappearance of the bands corresponding to X-only telomeres from Southern blots as telomerase-negative cultures progress into crisis is indeed a consequence of Y′ element acquisition that is promoted by Rad59 . One can , therefore , envisage that the clones that arise via Y′ translocations are the precursors of type I survivors . The homogenization of the subtelomeric sequences due to preferential translocation of a certain class of Y′ elements can further promote perhaps more efficient Rad51-dependent BIR initiated within Y′ elements resulting in their amplification that is observed in type I survivors [10] . Remarkably , the strains that harbour Y′ elements at all chromosome ends due to previous history as a telomerase-defective survivor form survivors more readily when rendered telomerase-negative again [50] . This observation suggests that spreading of the Y′ elements to all ends could be one factor that limits the rate of type I survivor formation . Consistent with this proposition , we found that Rad59 accelerates type I survivor formation when type II survivor pathway is inhibited . The initial step of Y′ acquisition involves heteroduplex formation by TG1–3 repeats that contain mismatches and is likely recognized by mismatch-repair proteins [51] , [52] . It is therefore possible that the homeologous heteroduplexes formed during Rad59-dependent single-strand annealing are often rejected , and this may lead to reiterative rounds of repair synthesis which could effectively provide a way of TG1–3 tract expansion in the absence of telomerase . Indeed , we have identified a few clones with substantially long TG1–3 tracts at the breakpoint of recombination , which could have resulted from reiterative repair synthesis . We could speculate that once very long internal tracts are generated , they can be excised as circles via intramolecular recombination events ( perhaps in a few steps ) [14] , [53] . The ultimate escape mechanism would be achieved via conversion to type II survivors , which are thought to amplify their terminal repeats via rolling circle replication . This could explain the apparent contradiction between the fact that circular DNA molecules are efficiently generated only when telomeric repeat tracts are abnormally elongated [54] and that only short telomeres engage in type II recombination [14] . Interestingly , we found that Rap1 was strongly enriched at the internal TG1–3 tracts located between the X and Y′ elements of the donor telomeres ( Figure S9 and Table 1 , see also [55] ) . Rap1 at these internal TG1–3 tracts was not relocalized upon critical shortening of telomeres [55] . It is conceivable that these internal Rap1 binding sites are also bound by the Sir proteins [56] . Notably , there is no homology between the modified TelVII-L and the Y′ donor chromosome end outside of the TG1–3 repeat tracts . Nevertheless , the apparent efficiency of Rad51-independent single strand annealing that relies exclusively on short homeologous TG1–3 repeat sequences is rather high , which might be due to spatial clustering of the telomeres in the yeast nucleus [57] . Indeed , for double-strand break repair , it has been recently shown that proximity of the donor sequence promotes homologous recombination [58] . The choice of the Y′ donor does not appear random . Whether this preference reflects the proximity between the chromosome ends in the nucleus or is influenced by other factors is currently unknown . Of note , among all chromosome ends , the VI-L end harbours one of the longest ( 139 bp-long ) internal TG1–3 tract between the X and Y′ elements; and VII-R end is located at approximately the same distance from the centromere as the experimental VII-L end on the opposite arm of the same chromosome , which is consistent with Rabl configuration [59] . It has been also reported that Rap1 had the intrinsic ability to locally distort telomeric double-stranded DNA provoking local conformational changes characteristic of single strand [60] . Therefore , subtelomeric Rap1 binding could favour homologous recombination by creating local structures that are amenable to annealing with single-stranded overhang . In addition , Rap1 contains four putative SIMs , and thus , it may potentially recruit SUMOylated Rad52 and Rad59 to TG1–3 tracts . Although our study focuses on short telomere repair by Y′ translocation , the BIR events leading to only terminal TG1–3 tract extension are also possible in telomerase-negative yeast . These events are readily detectable by Southern blot in survivors [14] , [22] since they often result in large increase of terminal TG1–3 tract length , but this is not the case in pre-senescent cells . Direct sequencing of the terminal repeats showed that they do occasionally get extended in pre-senescent cells , although there is a controversy of whether the short or long tracts are preferentially extended [21] , [22] . In any case , recombination between terminal repeats cannot indefinitely sustain proliferation of telomerase-negative cells , whereas Y′ translocation leads toward type I telomere formation , which likely requires additional changes such as chromatin structure alteration [19] . In summary , we have characterized a repair mechanism that acts upon short X-only telomeres in budding yeast lacking telomerase during presenescence . This repair mechanism does not require Rad51 and depends on annealing between short homeologous sequences which is stimulated by Rad59 . Unlike a single unrepairable DSB , a single critically short telomere is not immediately lethal for a cell in the absence of telomerase as long as there is a reserve of long telomeres on other chromosome ends . Remarkably , Rad51 independence of the short telomere rescue pathway points to yet another problem caused by telomerase inactivation which resolution depends on Rad51-dependent HR , since deletion of RAD51 is known to cause early loss of viability of telomerase-negative cells . All yeast strains used in this study were from the W303 background ( see genotypes in Table S2 ) . Strains used to analyze telomere rearrangement in the absence of telomerase were derivatives of the YAB892 and YAB893 , which have 0 and 16 Rap1-binding sites , respectively , flanked by loxP sites at the modified telomere VII-L [31] . To obtain Tet-Off TLC1 derivatives , these strains were crossed to the tetO2-TLC1 strain . The inducible EST2 deletion derivatives ( double Cre-loxP ) were generated by first transforming the cells with the pDS381 plasmid [33] carrying EST2 gene linked to the ADE2 marker and the loxP-flanked ARS/CEN region , and then replacing the endogenous EST2 locus with KANr cassette . All other gene disruptions were carried out by PCR-based methods resulting in the replacement of targeted loci with the TRP1 marker . To induce genome-integrated GALp-CRE for simultaneous TelVII-L shortening and loss of the plasmid-borne EST2 , overnight cultures growing in SC medium lacking lysine and adenine and containing 2% raffinose were diluted ( 1∶20 ) into complete YEP medium containing 2% galactose . After 24 hours of induction the cells were diluted into YPD to prevent genome damage by Cre at non-specific sites . To inhibit telomerase in the Tet-off TLC1 strains , the cultures grown in SC medium lacking lysine and containing 2% raffinose were supplemented with doxycycline ( 10 µg/µl ) , 12 hours before induction of TelVII-L shortening by switching cells to galactose for the next 24 hours as described above . The doxycycline concentration was maintained constant throughout the experiments in all media . To determine the ability of individual telomerase-negative cells to form microcolonies , the double Cre-loxP cells were micromanipulated onto a grid of YPD agar at 36 h after induction of Cre with galactose in liquid culture . Alternatively , the Tet-off TLC1 derivatives were micromanipulated onto the YPD agar freshly supplemented with Dox ( 20 µg/µl ) at 24 h after Cre induction and 48 h after repression of TLC1 with Dox . The microcolony formation was monitored by counting the number of cells in each grid position at 2 , 4 , and 8 h after micromanipulation . The plates were incubated for additional 4 days to allow formation of visible colonies . The colonies which exhibited growth delay at the time or soon after micromanipulation were chosen for VII-L end analysis . In addition , the colonies with deeply nibbled edges and typically smaller size were included regardless of the growth delay . The clones that exhibited robust expansion and formed large colonies with smooth edges were used as controls . To determine the state of the VII-L end before and after induction of Cre expression , genomic DNA was digested simultaneously with MfeI and PacI . The resulting fragments were separated by 0 . 9% agarose gel electrophoresis , transferred on Hybond N+ , and hybridized with 32P-labeled 252 bp-long probe that was generated by PCR ( see Table S1 for primers sequences ) using YAB892 genomic DNA as a template . The rearrangement of the VII-L end in clonal populations recovered from transient arrest was analyzed similarly , except for two aliquots of DNA were digested separately with either MfeI or PacI . To determine the length of native telomeres , XhoI-digested yeast DNA was subjected to 0 . 8% agarose gel electrophoresis and hybridized with a 32P-labeled ( TG1–3 ) n probe . The probes were labeled by random priming using Klenow fragment exo- , and all hybridizations were performed in Church buffer at 58°C . To amplify the regions encompassing putative Y′ translocation junctions at either modified VII-L or native VI-R ends , the chromosome end-specific primers were designed to anneal ∼500 bp away from terminal TG1–3 repeats , and the Y′ element-specific primer sites were chosen in the centromere-proximal region that is conserved among all Y′ elements ( Table S1 ) . The Y′ sequence alignments are available from Ed Louis at http://www2 . le . ac . uk/colleges/medbiopsych/research/gact/resources/yeast-telomeres . Analytical PCR was performed using Phusion DNA Polymerase ( Thermo Scientific ) in 1xHF buffer and 1 ng/µl genomic DNA purified via phenol-chlorophorm extraction . Primers were used at 500 nM , and the annealing temperature was set at 3°C above the Tm of the least stable primer . For cloning , PCR across the junction was performed using Taq DNA polymerase to produce fragments with 3′ A overhangs , and the purified product was inserted into the pCR2 . 1 vector via one-step TA cloning ( Invitrogen ) . The inserts were sequenced from both ends using M13F-20 and M13-26REV primers by Sanger method ( at Beckman Coulter ) . As a rule , the read going through the TG-rich strand failed at the junction , so the entire sequence was assembled from both reads . Rad59 was 13xMyc epitope–tagged at the C-terminus using one-step PCR . ChIP was performed as previously described [61] . Briefly , chromatin was cross-linked with 1% formaldehyde and sonicated to an average 200- to 500-bp DNA fragment size . After clarifying centrifugation , soluble chromatin was incubated with mouse anti-Myc tag monoclonal antibodies ( 9E10 ) and immunocomplexes were bound to magnetic Dynabeads Protein G ( Novex ) . Following successive washes in standard solutions , Rad59-Myc bound chromatin was eluted from beads and incubated at 68°C to reverse crosslinks . DNA purified from the immunoprecipitates and inputs was quantified by real-time qPCR using chromosome end-specific primers listed in Table S1 . The enrichment of the telomere-specific sequences bound by Rad59 was normalized to input and an unaffected GAL2 locus . Rap1 ChIP experiments ( duplicates ) were performed with anti-Rap1 antibodies kindly provided by David Shore ( University of Geneva ) . Rap1 ChIP and input DNA samples were hybridized to Nimblegen S . cerevisiae high density tiling arrays that were designed by us in collaboration with Frédéric Devaux ( Ecole Normale de Paris ) and Nimblegen to cover the entire genome . They contain 50 nt-long oligonucleotides separated by ∼15 nt-long gaps . The chip covers both strands of S . cerevisiae genome . Hybridizations and data analysis were performed by Nimblegen ( Roche NimbleGen ) . Rap1 peaks were visualized with the signalMap software ( Nimblegen ) or with the Integrative Genomics Viewer ( Broad Institute ) . The genome-wide Rap1 binding profiles were consistent with the recent published studies [55] .
In humans , telomerase is expressed in the germline and stem , but is repressed in somatic cells , which limits replicative lifespan of the latter . To unleash cell proliferation , telomerase is reactivated in most human cancers , but some cancer cells employ alternative lengthening of telomeres ( ALT ) based on homologous recombination ( HR ) to escape senescence . Recombination-based telomere maintenance similar to ALT was originally discovered in budding yeast deficient in telomerase activity . Two types of telomere arrangement that depend on two genetically distinct HR pathways ( RAD51- and RAD59-dependent ) were identified in post-senescent survivors , but the transition to telomere maintenance by HR is poorly understood . Here , we show that one of the earliest steps of short telomere rearrangement in telomerase-negative yeast is directly related to the “short telomere rescue pathway” proposed 20 years ago by Lundblad and Blackburn , which culminates in the acquisition of subtelomeric Y′ element by shortened telomere . We found that this telomere rearrangement depends on Rad52 strand annealing activity stimulated by Rad59 , thus it is distinct from Rad51-dependent Y′ amplification process observed in type I survivors . We show that continuous repair of critically short telomeres in telomerase-negative cells delays the onset of senescence and prepares the ground for telomere maintenance by HR .
You are an expert at summarizing long articles. Proceed to summarize the following text: Biogenesis of ribosomes is an essential cellular process conserved across all eukaryotes and is known to require >170 genes for the assembly , modification , and trafficking of ribosome components through multiple cellular compartments . Despite intensive study , this pathway likely involves many additional genes . Here , we employ network-guided genetics—an approach for associating candidate genes with biological processes that capitalizes on recent advances in functional genomic and proteomic studies—to computationally identify additional ribosomal biogenesis genes . We experimentally evaluated >100 candidate yeast genes in a battery of assays , confirming involvement of at least 15 new genes , including previously uncharacterized genes ( YDL063C , YIL091C , YOR287C , YOR006C/TSR3 , YOL022C/TSR4 ) . We associate the new genes with specific aspects of ribosomal subunit maturation , ribosomal particle association , and ribosomal subunit nuclear export , and we identify genes specifically required for the processing of 5S , 7S , 20S , 27S , and 35S rRNAs . These results reveal new connections between ribosome biogenesis and mRNA splicing and add >10% new genes—most with human orthologs—to the biogenesis pathway , significantly extending our understanding of a universally conserved eukaryotic process . In eukaryotic cells , the synthesis of ribosomes is a complex process involving several hundred genes whose functions span transcription of precursor ribosomal ribonucleic acids ( pre-rRNAs ) , processing of pre-rRNAs , assembly of ribosomal proteins ( r-proteins ) with pre-rRNAs , and nuclear export of the ribosomal particles [1]–[6] . Ribosome biogenesis is an essential process , with mutations of ribosome biogenesis genes either causing lethality or increasing susceptibility to cancer—e . g . , bone marrow failure and leukemia [7] or breast cancer [8] . This pathway has been extensively studied over the past 30–40 y , and a broad picture of the major events is known for the yeast Saccharomyces cerevisiae . First , 35S polycistronic pre-rRNA is transcribed from the ribosomal deoxyribonucleic acid ( rDNA ) repeat by RNA polymerase I in the nucleolus . During transcription , the small-subunit processome and some small-subunit r-proteins assemble onto the 35S pre-rRNA to form a 90S particle . The 35S pre-rRNA is cleaved to release the pre-40S particle , which contains a 20S pre-rRNA . The pre-60S complex assembles on the rest of the transcript , and both subunits are further processed in the nucleus and independently exported through the nuclear pore complex ( NPC ) to the cytoplasm , where they undergo further maturation—e . g . , cleavage of 20S pre-rRNA to 18S rRNA . The mature small subunit contains 32 proteins and 18S rRNA , while the large subunit contains 46 proteins and three rRNAs: 5 . 8S , 25S , both derived from the 35S precursor , and 5S , which is transcribed separately by RNA polymerase III . Ribosome biogenesis is a temporally and spatially dynamic process requiring coordination of many trans-acting factors at different stages along the pathway , including at least 170 protein factors that act to modify and cleave pre-rRNAs and help to assemble and export ribosomal particles [5] , [9] . Many of these protein factors were first identified by yeast genetics . Later , biochemical purifications coupled with mass spectrometric analysis greatly expanded the number of known factors [10]–[16] . In addition , a large-scale effort using oligonucleotide microarrays identified 115 mutants that exhibited pre-rRNA processing defects , and 10 new genes were confirmed to affect pre-rRNA processing [17] . Despite these intensive studies , new ribosome biogenesis genes are still emerging , and recent computational analysis suggests that over 200 genes constitute the ribosome biogenesis regulon [18] , indicating that the genes in this fundamental cellular pathway have not been completely identified . We asked if recent functional genomic and proteomic studies could be applied in a predictive fashion to identify additional ribosomal biogenesis genes . In particular , functional networks of genes have been reconstructed , incorporating literally millions of experimental observations into probabilistic networks indicating genes likely to work together in cells . The emerging technique of network-guided genetics ( e . g . , [19] , [20] ) leverages such networks to computationally associate candidate genes with a biological process of interest , much as a genetic screen might do . We used such a probabilistic gene network [21] to predict the genes most likely to participate in yeast ribosome biogenesis based on connectivity to known ribosomal biogenesis genes , and we present here experimental confirmation of at least 15 new genes affecting ribosome biogenesis . Beyond providing new insights into ribosome biogenesis , this study therefore also represents one of the most extensive experimental studies to date of the principle of network-guided genetics , which we demonstrate to be a powerful approach for rational discovery of candidate genes , applicable to diverse biological processes . In general , we expect genes of ribosome biogenesis to be coordinately expressed , to physically or genetically interact with each other , to show common subcellular localization , and so on . Many such associations have been observed in high-throughput experiments in yeast , but these data suffer from false-positive and false-negative observations . Nonetheless , the appropriate analyses of such data should rationally prioritize candidate ribosome biogenesis genes . We therefore constructed a computational predictor of ribosome biogenesis genes based on analysis of functional genomics , proteomics , and comparative genomics datasets that had been combined into a probabilistic gene network [21] covering about 95% of yeast proteome ( Figure 1A ) . This network employs a probabilistic scoring scheme to quantitatively integrate heterogeneous functional genomic and proteomic datasets , including mRNA-expression data across different conditions , protein-protein interaction datasets derived from literature curation , high-throughput yeast two-hybrid assay , affinity purification coupled with mass spectrometry , genetic interaction data , and in silico interaction datasets [21] . We calculated the naïve Bayesian probability that each yeast gene will belong to the ribosome biogenesis pathway based on gene connectivity information in the gene network—i . e . , “guilt-by-association” [22] , [23] with known ribosome biogenesis genes . Ribosome biogenesis genes were highly connected and predictable in this gene network , as shown by a plot of cross-validated true-positive versus false-positive prediction rates ( ROC plot; Figure 1B ) . From the top-scoring genes , 212 candidates were manually selected based on expert knowledge for experimental validation ( Table S1 ) . The synthesis of ribosomes is essential for cell growth and survival , and most genes involved in ribosome biogenesis are either essential or required for normal growth rates . In our list of candidate ribosome biogenesis genes , 50 genes are essential , and 162 genes are nonessential under standard laboratory culture conditions [24] . We thus performed growth assays for each strain with a deletion of one of the 162 nonessential genes under three temperature conditions: 20°C , 30°C , and 37°C ( Figure S1 ) . Of these , 51 mutants with constitutive or conditional slow-growth phenotypes were identified . These mutants and 50 mutants carrying conditional essential alleles were investigated further ( Figure 1A ) . For each of the selected 101 mutants , we tested for gross ribosome biogenesis defects by measuring the proportions of free 40S , 60S , and 80S subunits , as well as polysomes , in the mutant strains . After cleavage of the pre-40S particle from the 35S transcript , the syntheses of 40S and 60S subunits are largely independent [6] . Depletion of the factors required for the synthesis of one subunit usually does not significantly affect synthesis of the other subunit [25] , resulting in a change in the ratio of 40S to 60S , which is most evident in the free subunit pools in the cell . In addition , a reduction in the amount of 60S subunits can lead to a translation initiation defect , with 40S subunits awaiting 60S subunits to form 80S ribosomes . These stalled 40S subunits are observable as halfmer polysomes in a polysome profile [26] . Polysome profiles are generated by separating the ribosomal subunits and different-sized polysomes through a continuous sucrose density gradient and monitoring the absorbance of nucleic acids along the sucrose gradient [27] . We analyzed polysome profiles for the 50 mutants carrying conditional alleles controlled by either a tetracycline-regulatable ( tetO7 ) promoter [28] or a GAL1 promoter and for the 51 nonessential gene deletion mutants with conditional growth defects . Including controls , over 150 polysome profiles were generated . In order to compare different profiles and perform multivariate analyses such as clustering , we computationally aligned each profile to a reference wild-type profile by using a correlation-optimized warping ( COW ) algorithm [29] , which corrects for peak shifts of ribosome subunits and polysomes due to minor variations in sucrose density gradients . Similar polysome profiles were grouped together using hierarchical clustering [30] . From the clustergram , the signals corresponding to the ribosomal subunits , monosomes , polysomes , and halfmer polysomes were clearly identifiable ( Figure 2A ) . Importantly , nearly half of the tested mutants showed clear ribosome biogenesis defects by this analysis . This is a much higher ratio than the ∼1/30 expected by chance , indicating the strong enrichment for true ribosome biogenesis genes provided by the network-guided genetics . Several sets of mutants exhibited grossly similar biogenesis defects , detectable as coherent groups in the clustergram . Most of the profiles with high 40S to 60S ratios and halfmer peaks were in clusters 1 and 2 , which represent 60S biogenesis defects ( Figure 2C ) . Cluster 3 represents profiles from mutants showing protein translation defects ( Figure 2D ) , some of which also affected the ratio of 40S to 60S ribosomal subunits when compared to wild-type strains ( Figure 2B ) . It is noteworthy that the translation-initiation factor mutants , including fun12Δ , tetO7-TIF35 , tetO7-TIF34 , tetO7-RPG1 , and tetO7-DED1 , did not display the same defects , indicating that the observed ribosome biogenesis defects are not simply a general effect of inhibition of translation . The profiles with low 40S to 60S ratios were in cluster 4 , which suggests 40S biogenesis defects ( Figure 2E ) . The polysome profiles from three mutants ( ypr044cΔ , tif4631Δ , and snu66Δ ) were not clustered with 60S biogenesis clusters 1 and 2 , although they showed halfmer polysomes ( Figure 2A , 2C ) . Some mutants showed only subtle defects , and their profiles were interspersed among wild-type-like profiles during clustering ( Figure S2 ) . The polysome profiles provided initial suggestions about the function of these genes in ribosome biogenesis and translation . We further investigated 43 mutants that exhibited altered 40S to 60S ratios compared to wild-type strains ( Table S1 and Figure 1A ) . Most ribosome biogenesis factors associate with pre-ribosomal particles [3] . In order to distinguish factors associated with pre-40S particles from factors associated with pre-60S particles , we applied both a classical immunoblot approach and a novel mass-spectrometry-based approach in order to assess sedimentation patterns of potential ribosome biogenesis factors in sucrose density gradients ( Figure 1A ) . Most mutants defective for ribosome assembly display altered pre-rRNA processing [9] . The effects on pre-rRNA processing can be a direct consequence of a mutation in an enzymatic processing activity , or they can be indirect . Regardless of whether the effect is direct or indirect , the observed pre-rRNA processing defects provide valuable diagnostics for characterizing the ribosome biogenesis defects and thus the putative activity of a ribosome biogenesis candidate gene; we therefore examined pre-rRNA processing defects in each of the 43 candidate genes confirmed by polysome profiling to affect ribosome biogenesis . Several specific pre-rRNA processing events are critical to biogenesis: The 35S pre-rRNA undergoes extensive modification as well as sequential multiple endo- and exo-nuclease cleavages to give rise to the mature 18S , 5 . 8S , and 25S rRNAs [2] . The 35S pre-rRNA is first cleaved at sites A0 , A1 , and A2 to yield 20S and 27SA2 species ( Figure 5B ) , and the 20S pre-rRNA is further processed in the cytoplasm to form the mature 18S rRNA after cleavage at the D position . The 27SA2 pre-rRNA is processed by two different routes . The majority is cleaved at site A3 , followed by exonuclease digestion to site B1S to form 27SBS , while a small amount of 27SA2 undergoes endonucleolytic cleavage at B1L to generate 27SBL . Both 27SB species are further processed at sites C1 and C2 to yield the mature 25S species and 7S species , which mature to 5 . 8S by 3′-exonuclease digestion to E ( Figure 5B ) . To examine the detailed effects of the candidate ribosome biogenesis genes on pre-rRNA processing , we used Northern blotting with oligonucleotide probes ( Figure 5A ) to monitor the levels of 9 different pre-rRNA and rRNA species in each of the 43 mutant strains . In order to quantitatively analyze the change of each RNA species in a mutant relative to the wild-type strain under corresponding conditions , Northern blots ( Figure 5C–5E ) were quantified , and the logarithm of the intensity ratio of each RNA species from a mutant strain relative to that from its corresponding wild-type strain was calculated and used for hierarchical clustering analysis ( Figure 5F ) . We observed a dramatic increase ( red signal in Figure 5F ) or decrease ( green signal in Figure 5F ) of at least one pre-rRNA species for all of the mutants except eap1Δ and trf5Δ ( Figure 5F ) . The mutants with 60S biogenesis defects in polysome profile analyses clustered into two groups in Northern blotting analyses ( Figure 5F , green labels ) , and many 40S mutants in polysome profile analyses also clustered together ( Figure 5F , red labels ) , showing general correlation between polysome profile defects and pre-rRNA processing defects . In conjunction with the polysome profile and co-sedimentation data , these defects strongly suggest function for the candidate genes in or upstream of the implicated processing steps . We thus employed the observed defects to classify the candidate genes according to their potential general roles . As a last major characterization of the candidate ribosome biogenesis genes , we investigated their possible roles in ribosome nuclear export . Nuclear export of the ribosomal subunits through NPCs depends upon the RanGTPase cycle and receptor proteins that mediate the interaction between the ribosomal subunit and the NPC . The receptors can bind to adapter proteins or to the subunits directly . In the case of the 60S subunit in yeast , export depends upon the adapter protein Nmd3p and its receptor Crm1p ( Xpo1 in human ) , as well as the heterodimer of Mex67p/Mtr2p [63] and the specialized receptor Arx1p [64] , [65] . Export of the 40S subunit also requires Crm1p , and although it has been suggested that Ltv1p acts as a Crm1p-dependent adapter , Ltv1p is not essential , indicating that additional adapters and/or receptors remain to be identified [5] , [66] . To test whether the ribosome biogenesis candidates affect ribosome transport , we assayed ribosome export in the mutants by using Rps2-GFP and Rpl25-GFP as reporters for the small and large ribosomal subunits , respectively [10] , [67] , while monitoring the nucleolus with Sik1-mRFP [34] . In wild-type control strains cultured under various conditions , both small and large ribosomal subunits localized primarily in the cytoplasm ( Figure 7A–7B , first row , and Figure S3 ) . Upon depletion of Yrb2p , a known factor involved in small-subunit export [45] , ribosomal small subunits accumulated in the nucleus ( Figure 7A ) , while the large subunits were unaffected ( Figure S4 ) . In mutants defective in the synthesis of small subunits , including tetO7-BFR2 , bud22Δ , bud23Δ , tetO7-YDR339C , ygr081cΔ , GAL1-ENP2 , GAL1-NOP9 , GAL1-SGD1 , and GAL1-KRE33 , we observed significant accumulation of the small subunit reporter in the nucleus and/or nucleolus ( Figure 7A ) , whereas the large subunits were unaffected ( Figure S4 ) . The defective nuclear export of 40S subunits upon depletion of Kre33p is consistent with previous observation of a temperature sensitive mutant kre33-1 [10] . Because the pre-40S contains the 20S pre-rRNA as it is exported to the cytoplasm , a bona fide block in subunit export is expected to result in increased levels of 20S rRNA . This was in fact observed for the bud23Δ and ygr081cΔ mutants ( Figure 5F ) , which suggests that they act late in the biogenesis and export pathway , whereas the other genes are involved in early ribosome biogenesis . Recently , Bud23p has also been shown to methylate G1575 of 18S rRNA [68] . We note , however , that defective pre-rRNA processing and/or ribosome assembly may also lead to the inefficient transport of ribosomes to the cytoplasm [69] or accumulation of reporter proteins in the nucleus . In mutants with defective synthesis of large ribosomal subunits , including tetO7-AFG2 , tetO7-BCP1 , puf6Δ , tetO7-YDR412W , and tif4631Δ , strong accumulation of the large ribosomal subunits in the nucleolus and nucleus was observed ( Figure 7B ) , but not of the small subunits ( Figure S5 ) . Surprisingly , deletion of LSM6 or LSM7 inhibited the transport of pre-60S subunits to the cytoplasm ( Figure 7B ) but not the small subunits ( Figure S5 ) . Therefore , the accumulation of 20S upon deletion of LSM6 or LSM7 suggests that they act in 20S processing in the cytoplasm . In total , we identified 17 genes that affected export of either the ribosomal small or large subunits . As expected , many genes for ribosome biogenesis are essential . However , a large number of nonessential genes are clearly involved in ribosome biogenesis , some of which show strong constitutive or conditional phenotypes ( Figure S6 ) . For example , deletion of PUF6 , SAC3 , or SNU66 resulted in strong defects at 20°C but only minor defects at the optimal growth temperature of 30°C . In contrast , the polysome profile of yor006cΔ showed 40S biogenesis defects at 30°C but no defects at 20°C . Several nonessential genes , including YIL096C , YCR016W , YJL122W , YNL022C , BUD20 , and NOP13 , form a tight cluster with known ribosome biogenesis genes in the gene network , and their encoded proteins co-sedimented with either 40S or 60S fractions , supporting them as being components of pre-ribosomes ( unpublished data ) . However , deletion mutants for those genes did not show growth defects at 20°C , 30°C , or 37°C ( Figure S1 ) , nor were polysome profiles of the deletion mutants different from wild-type cells ( unpublished data ) . However , lack of a mutant phenotype does not imply that these candidate genes are not part of the ribosome biogenesis pathway . In fact , Yjl122wp ( Alb1p ) was recently confirmed to interact directly with the known ribosome biogenesis factor Arx1p , although the deletion mutant had no observable phenotype [70] . It is therefore still likely that the remaining candidate genes participate in ribosome biogenesis but that we failed to identify a conditional phenotype or that these genes are functionally redundant with other genes . In the latter case , synthetic interaction assays might prove a useful strategy for deciphering the genes' functions . Indeed , we observed one such example: mutants with either deletion of TRF5 or depletion of Pap2p did not exhibit defects in polysome profile analyses at 30°C , but depletion of Pap2p in the trf5Δ mutant caused strong 60S biogenesis defects evident in polysome profile analysis ( Figure 8 ) , which suggests that TRF5 and its paralog PAP2 are required for efficient ribosome biogenesis , presumably by facilitating the removal of aberrant pre-rRNA molecules [71] . Thus , many of the remaining nonessential mutants without conditional phenotypes may still be involved in ribosome biogenesis . Gene network-based predictions based on binary associations between genes intrinsically help to identify genes that participate in multiple cellular processes . Correspondingly , several genes we identified have been reported to have other functions . For example , BCP1 is required for the export of Mss4p [72] , Sgd1p interacts with Plc1p and is involved in osmoregulation [73] , and a recent study showed that Mtr2p , known as an mRNA export receptor [74] , is directly involved in ribosomal large-subunit export [63] . Similarly , we identified Sac3p , which localized to the NPC and is involved in mRNA export [75] as a ribosome biogenesis factor based on polysome profile and Northern blot analyses of the deletion mutant ( Figures 2E , 5E ) . In addition , Sac3p co-sedimented with 40S fractions , suggesting its possible association with ribosomes ( Figure 3A ) . It is known that Sac3p can mediate protein export [76] , but we did not observe export defects for either ribosomal subunit in the sac3Δ mutant ( unpublished data ) . Thus , SAC3 joins MTR2 and MEX67 as genes participating in both the ribosome biogenesis and mRNA export pathways . Recently , the splicing factor Prp43p was confirmed to be a ribosome biogenesis factor by several groups , which suggests coordination of ribosome biogenesis and mRNA splicing [77]–[79] . We observed that four genes associated with mRNA splicing—LSM6 , LSM7 , PRP4 , and SNU66—also play roles in ribosome biogenesis . Although we do not exclude the possibility of indirect roles of PRP4 in ribosome biogenesis , deletion of SNU66 ( a component of the tri-snRNP ) not only delays 35S processing but also affects processing of the 5S rRNA precursor ( Figure 5E ) . Thus , these data provide further evidence for shared components between these processes , which supports a general connection between ribosome biogenesis and mRNA splicing . Whether this connection is direct or indirect generally remains to be established , although the specificity of the rRNA processing defect and the observed genetic interactions ( Figure 5G ) suggest a direct role for SNU66 in 5S processing . In conclusion , we applied the emerging technique of network-guided genetics to computationally predict and experimentally validate at least 15 previously unreported ribosome biogenesis genes ( TIF4631 , SNU66 , YDL063C , JIP5 , TOP1 , SGD1 , BCP1 , YOR287C , BUD22 , YIL091C , YOR006C/TSR3 , YOL022C/TSR4 , SAC3 , NEW1 , FUN12 ) ( Table 1 ) , most of which have human orthologs and thus represent evolutionarily conserved components of this essential core cellular process . Selecting candidates with a network-guided genetics approach therefore proved to be a powerful approach for identifying new genes in a pathway , even in such a well-studied cellular process as ribosome biogenesis , with ∼40% of the tested genes in the polysome profile analyses being shown to participate in this pathway . Although considerable effort has been spent predicting and validating gene functions from diverse functional genomics and proteomics data [17] , [80] , to our knowledge this is one of the most extensive experimental tests of predictions from network-guided genetics . These results add >10% new genes to the ribosome biogenesis pathway , significantly extending our understanding of a universally conserved eukaryotic process . Haploid MATa deletion mutants [81] were obtained from Research Genetics . TetO7-promoter mutants [28] and TAP-tagged strains [31] were acquired from Open Biosystems . All commercial strains in this paper were verified by PCR , and four strains found to be incorrect in commercial collections ( ypr045cΔ , tetO7-SGD1 , Kre33-TAP , and tetO7-KRE33 ) were recreated . GAL1-promoter mutants were constructed in strain BY4741 ( Text S1 ) . Haploid deletion mutants were cultured to OD600 0 . 3–0 . 5 in YPD ( 1% yeast extract , 2% peptone , 2% dextrose ) at the conditional temperature ( 20°C , 30°C , or 37°C ) . TetO7-promoter mutants were cultured in YPD and then diluted into YPD with 10 ug/ml doxycycline ( Fisher Scientific ) for 9–20 h to OD600 0 . 3–0 . 5 . GAL1-promoter mutants were cultured in YPGal ( 1% yeast extract , 2% peptone , 2% galactose ) and then diluted into YPD for 12–20 h to OD600 0 . 3–0 . 5 . Strains carrying pRS416 and pRS413 derived plasmids were cultured in synthetic complete media minus uracil and histidine supplemented with 2% dextrose to OD600 0 . 3–0 . 5 . Detailed culture information for each individual strain is described in Table S1 . Yeast cells were cultured at various conditions to OD600 0 . 3–0 . 5 . Two hundred µg/ml cycloheximide ( Sigma ) was added to each culture . Cell lysate preparation and sucrose density gradient sedimentation were performed as previously described ( Text S1 ) [65] . Each mutant's polysome profile was aligned to the wild-type reference polysome profile using COW implemented in MATLAB [29] . Aligned polysome profiles were hierarchically clustered using Cluster and Treeview software [30] . TAP-tagged strains were cultured in YPD at 30°C to OD600 0 . 3–0 . 5 , and subsequent steps were performed in the same manner as for the polysome profile analyses . Fractions from the sucrose density gradient were collected , and 25 µl of each fraction was deposited onto a nitrocellulose membrane using a 96-well dot-blot system ( Schleicher & Schuell ) . The membrane was probed for the TAP-tagged proteins with the rabbit peroxidase anti-peroxidase soluble complex ( Rockland Immunochemicals ) , using Luminol ( Santa Cruz Biotechnology ) as the substrate for detection . The total intensity of each dot was quantified with Quantity One 1-D Analysis software ( Bio-Rad ) . The wild-type strain BY4741 was cultured in YPD at 30°C to OD600 0 . 3–0 . 5 and then lysed and fractionated on a sucrose density gradient in the same manner as for the polysome profile analyses . Proteins from each fraction were precipitated with 10% cold trichloroacetic acid , washed with cold 100% acetone , resuspended in 100 mM Tris buffer ( pH 8 . 0 ) , and digested with proteomic-grade trypsin ( Sigma ) for 24 h at 37°C . Each digested peptide mixture was separated by a strong cation-exchange column , followed by a reverse-phase C18 column . Peptides were analyzed online with an electrospray ionization ion-trap mass spectrometer ( ThermoFinnigan DecaXPplus ) , and proteins were identified at a 5% false-detection rate by using PeptideProphet and ProteinProphet software [82] . For each sucrose gradient fraction , the number of MS/MS spectra associated with a given protein was divided by the sum of the spectral counts across all proteins in that fraction to estimate the relative abundance of each protein within each fraction . The resulting relative abundance profiles were subjected to hierarchical clustering using the Cluster and Treeview programs . Raw mass-spectrometry data are deposited in the Open Proteomics Database as accession opd00106_YEAST . RNA was extracted by the hot acidic phenol method . The high- and low-molecular-weight RNA species were separated by 1% agarose-formaldehyde gel ( NorthernMax , Ambion ) and 8% polyacrylamide-TBE-urea gel , respectively . RNAs were transferred onto Zeta-Probe GT membrane ( Bio-Rad ) by capillary transfer for agarose gel or semi-dry electroblotting for polyacrylamide gel . After UV cross-linking of the RNAs to the membrane , 5′-P32-labeled oligonucleotide probes were sequentially hybridized , and the hybridization signals were detected by phosphorimaging and quantified using Quantity One ( Bio-Rad ) . The logarithm ratio of total intensity of each RNA species from a mutant to that from the corresponding wild-type was calculated and used for hierarchical clustering . Wild-type strains or mutants were transformed with either pAJ907 ( RPL25-GFP CEN LEU2 ) or pAJ1486 ( RPS2-GFP CEN LEU2 ) , and each strain was also transformed with pRS411-SIK1-mRFP ( SIK1-mRFP CEN MET15 ) . Strains were cultured in synthetic complete media minus leucine and methionine , supplemented with 2% dextrose or 2% galactose . Essential gene expression was inactivated in the same way as for the polysome profile analyses . Cells were fixed with 4% formaldehyde ( Pierce ) for 30 min and then washed twice with PBS ( pH 7 . 2 ) . DAPI ( Vector Laboratories ) was used to stain DNA , and images were acquired using a Nikon E800 microscope and a Photometrics CoolSNAP ES CCD camera . The GFP median intensities within the three different compartments ( cytoplasm , nucleus , and nucleolus ) for each cell were determined by custom image-processing software implemented in MATLAB ( Text S1 ) . Then the relative ratio of GFP median intensity in the nucleus or nucleolus to that in the cytoplasm for each cell was calculated . For each strain , the median of this ratio for a population of cells was used as an index for the enrichment of ribosomal subunits in either the nucleus or nucleolus . To compare this enrichment in mutants to that in their corresponding wild-type strains , the index of each strain was normalized to the index of the corresponding wild-type strain .
Ribosomes are the extremely complex cellular machines responsible for constructing new proteins . In eukaryotic cells , such as yeast , each ribosome contains more than 80 protein or RNA components . These complex machines must themselves be assembled by an even more complex machinery spanning multiple cellular compartments and involving perhaps 200 components in an ordered series of processing events , resulting in delivery of the two halves of the mature ribosome , the 40S and 60S components , to the cytoplasm . The ribosome biogenesis machinery has been only partially characterized , and many lines of evidence suggest that there are additional components that are still unknown . We employed an emerging computational technique called network-guided genetics to identify new candidate genes for this pathway . We then tested the candidates in a battery of experimental assays to determine what roles the genes might play in the biogenesis of ribosomes . This approach proved an efficient route to the discovery of new genes involved in ribosome biogenesis , significantly extending our understanding of a universally conserved eukaryotic process .
You are an expert at summarizing long articles. Proceed to summarize the following text: Multipartite viruses replicate through a puzzling evolutionary strategy . Their genome is segmented into two or more parts , and encapsidated in separate particles that appear to propagate independently . Completing the replication cycle , however , requires the full genome , so that a systemic infection of a host requires the concurrent presence of several particles . This represents an apparent evolutionary drawback of multipartitism , while its advantages remain unclear . A transition from monopartite to multipartite viral forms has been described in vitro under conditions of high multiplicity of infection , suggesting that cooperation between defective mutants is a plausible evolutionary pathway towards multipartitism . However , it is unknown how the putative advantages that multipartitism might enjoy at the microscopic level affect its epidemiology , or if an explicit advantange is needed to explain its ecological persistence . In order to disentangle which mechanisms might contribute to the rise and fixation of multipartitism , we here investigate the interaction between viral spreading dynamics and host population structure . We set up a compartmental model of the spread of a virus in its different forms and explore its epidemiology using both analytical and numerical techniques . We uncover that the impact of host contact structure on spreading dynamics entails a rich phenomenology of ecological relationships that includes cooperation , competition , and commensality . Furthermore , we find out that multipartitism might rise to fixation even in the absence of explicit microscopic advantages . Multipartitism allows the virus to colonize environments that could not be invaded by the monopartite form , while homogeneous contacts between hosts facilitate its spread . We conjecture that these features might have led to an increase in the diversity and prevalence of multipartite viral forms concomitantly with the expansion of agricultural practices . Viruses transport their genetic material inside a protein shell , the capsid , surrounded in some species by a lipid membrane . In most viral species , each viral particle contains all the genetic material needed to carry out replication inside a host cell , and generate a progeny of viral particles . A prominent exception to this behavior is found in multipartite viruses . These viruses , first described in the 1960s [1] , have a genome segmented in two or more parts . According to current evidence , the segments are encapsidated separately and , apparently , propagate independently [2 , 3] . As of today , there is no mainstream theory able to explain the adaptive advantage of such a strange lifestyle [4] . The main puzzle regarding multipartite viruses is how the simultaneous presence of multiple segments , which imposes severe constraints on the number of viral particles that have to reach a susceptible host , is balanced by other adaptive advantages of multipartitism , whether microscopic or ecological [5 , 6] . Despite this apparent paradox , multipartitism is widespread in the Virosphere , as up to 40% of all known viral families are multipartite [7] . A large majority of them infects either plants or fungi , with only four known examples of species infecting exclusively animals [5] . Evolutionary pathways leading to multipartitism are likely to be multiple , since this strategy is present in RNA and DNA viruses , and in the latter case an origin to a single ancestral virus cannot be traced . Beyond its virological interest , multipartitism has a particularly negative effect on agricultural production , as several multipartite viruses are pathogenic , and routinely cripple crop yield [8] . Cultivars themselves may have directly played a role in the rise of multipartitism , as an evolutionary radiation in the diversity of viral species , many being just centuries old , was likely promoted by an intensification of agricultural practices [9–11] . It has been put forward that multipartite species might be at an advantage in the face of environmental changes , since they likely adapt faster due to new combinations of segments promoted by their genomic architecture [5] . It is known that changes in land cover offer multiple opportunities for novel interactions between plants and pathogens [12–15] . Studies on the impact of agriculture in viral ecology have uncovered a surprisingly negative association between plant diversity and family-level diversity of plant-associated viruses , and a higher prevalence of viruses in cultivated areas [16] . The emergence of defective variants out of the wild-type ( wt ) form ( i . e . , the one containing the full genome [17] ) has been both posited and observed in controlled environments , arising from replication errors and thriving under conditions that ensure high multiplicity of infection ( MOI ) [18 , 19] . Specifically , it has been shown in vitro that two defective forms spontaneously generated by foot-and-mouth disease virus ( FMDV has an unsegmented genome formed by ssRNA of positive polarity ) can complement each other and quickly substitute the wt form [20] . This strategy has been formally explored in models of competitive dynamics between the wt and a number of complementing segments [21] , which implemented different advantages that could compensate for the cost of an increased MOI . The model mimicked the experimental setting where , in particular , host-cell availability corresponded to that of a well-mixed system . Two of the advantages implemented had been theoretically proposed in the past , though , as of yet , have not received empirical support ( a faster replicative ability [22] and a slower accumulation of deleterious mutations in shorter segments [23 , 24] ) while , in the case of FMDV , it was shown that capsids containing shorter genomes enjoyed a larger average lifetime between infection events [25] . This differential degradation , dependent on genome length , was sufficient to compensate for co-infection requirements in multipartite forms with two , to up to four , segments [21] , but cannot explain the emergence of multipartite viruses with many segments , such as nanoviruses or babuviruses [5] . Hence , the evolutionary pathway explored in that work would be applicable to a subset of all currently described multipartite viral species . What is missing from this picture is investigating how the interaction between viral dynamics and host ecology shapes the rise and persistence of multipartite viral forms at the host population level . We also wish to quantify the impact of different host contact structures in driving the success of multipartitism . We tackle this problem by building a compartmental model for studying the competition between monopartite and multipartite variants in terms of their ability to spread and persist on a structured host population . As the generation of functional defective mutants from the wt occurs at a much longer time scale than the spread of the virus in the population , we set up a model that already contains both the wt and a cohort of defective forms , potentially complementary . This allows us to study the competition dynamics between the different forms causing them to coexist in , or take over the ecological niche . Using both analytical calculations and numerical tools , we investigate the outcome of a random emergence of mutants , and derive the conditions that make multipartitism a fitness-enhancing strategy , allowing the virus to adapt to a wider range of hosts and environments . Since no apparent structural feature discriminates multipartite viruses from monopartite ones—they are found exhibiting different capsid structures , genome sizes and types [26]—we include in the model as few virological features as possible , and investigate how multipartitism impacts on the spreading potential of the virus . We do , however , account for key viral mechanisms that can drive the resilience of multipartitism in an ecological context . The first one is the already mentioned differential degradation , i . e . , the different average lifetime of defective viral particles with respect to wt’s , or formally equivalent advantages of faster replication or elimination of deleterious mutations through sex . A second biological mechanism is the mode of transmission of multipartite viruses between hosts . Most known multipartite viruses are spread by vectors ( mainly insects ) , which typically pick up very few viral particles from an infected plant [27] . The transmission process between hosts typically acts as a population bottleneck for the virus , entailing a loss of genetic diversity and , if severe enough , the systematic purge of deleterious forms [28 , 29] . Thanks to our parsimonious modeling setup , any of the aforementioned mechanisms can be seen as effectively impacting the chances of the wt or defective particles to reach the target hosts , leading to a difference in transmissibility . A single model parameter , therefore , by tuning this relative transmissibility , embraces a number of different biological processes . In this sense , the results of our model can be extended to other systems as long as the specific mechanisms involved in their spreading fitness can be cast in the form of changes in transmissibility . Remarkably , we find out that even in the absence of an explicit microscopic advantage , ecological dynamics might cause the fixation of the multipartite form due to the stochastic extinction ( analogous to random drift ) of the monopartite virus . Alongside the biological properties of the virus , the model implements the structure of contacts among susceptible hosts through which the viruses can spread . Often , in our context , this means the contact network induced by vector movements among plants . Its topology may be diverse , depending on plant distribution and vector behavior , with two limit cases being the distribution of plant species in the wild ( see , e . g . [30] and references therein ) and huge modern agriculturally homogeneous regions [31] . These different architectures are implemented by tuning the distribution of contact rates among hosts , with limiting cases being fixed contact rate , and power law-distributed contact rate . This feature is the key tool to uncover how the structure of contacts among hosts shapes the endemicity and prevalence of the different viral forms . We remark that compartmental models of interacting diseases have been studied in the past [32–38] . Those models , however , assume that the disease agents involved are fully-fledged pathogens that can spread on their own , and cannot describe asymmetric viral associations [39] , of which multipartitism is an example . Thanks to that , a completely new phenomenology emerges , driven by a complex evolutionary dynamics , and involving a wide range of ecological interactions: competition , symbiosis , commensality . We consider a large population of N hosts susceptible to a virus that may circulate in its wild-type ( wt ) form together with v defective variants that are potentially complementing , i . e . , when simultaneously present in the host , they are able to complete the virus infective cycle even in the absence of the wt . As previously done [21] , we take advantage of the timescale separation between the random emergence of defective mutants due to errors during replication of the complete genome , and the competition between forms driven by their spread in the host population . This allows us to effectively model the random emergence of mutants as an initial small , yet nonzero , prevalence in the host population . As customary in compartmental models , we consider that hosts are either free of the virus , and thus susceptible ( S ) , or infected by a certain combination of the viral forms , translating into various infectious compartments ( Fig 1 ) . The main assumption is that a host can be infected only by a combination that guarantees the presence of the full genome . Without it , there is no completion of the viral cycle , and thus no systemic infection is possible . Moreover , we assume that host cells replicate all , and only , the viral forms they are infected by ( Fig 1A ) . These assumptions determine the set of existing compartments . Two infectious compartments are present regardless of the value of v . They are ⌜wt⌟ and ⌜all⌟ , and correspond to plants infected by the wt only , and by the wt together with all the v variants , respectively . If v = 1 , no other compartments exist . If v > 1 , ⌜seg⌟ identifies plants infected by all the v defective and complementing variants , without the wt . In addition , there are 2v − 1 other compartments containing wt plus a combination of some ( not all ) of the defective variants . We name them according to which of the latter they are infected by . For instance , ⌜1⌟ contains wt plus variant 1 , and ⌜3 , 5⌟ contains wt plus variants 3 and 5 . If defective variants are not present , ⌜wt⌟ behaves like a standard Susceptible–Infected–Susceptible ( SIS ) model , with probability of transmission upon contact equal to λ . We implement enhanced transmissibility of defective variants by assuming they spread with a probability ρλ . ρ = 1 thus means that wt and segments are epidemiologically equivalent , while any value larger than 1 causes the defective variants to transmit more easily than the wt . For a graphic representation of the spreading routes and differential transmissibility see Fig 1D . In the general case , an agent in a given compartment may transmit some ( or all ) of the viral species it hosts to the one it is in contact with , with a probability depending on the initial compartments of the two agents , and on the final compartment . For instance , a host in compartment ⌜1⌟ , upon contact with a susceptible one , may transmit both wt and 1 , turning the susceptible into a ⌜1⌟ . It may instead transmit wt alone , turning the susceptible into a ⌜wt⌟ . The defective variant , however , cannot be transmitted alone , as it requires wt , as previously stated . A schematic representation of the compartmental model for a bipartite virus ( v = 2 ) is depicted in Fig 1C . Our assumption of constant host population ( of size N ) holds for strictly constant size , as well as populations that are at equilibrium , i . e . , the number of births equals the number of deaths , or at least any growth pattern occurs at time scales much larger than the spread of the virus . This assumption is connected to the spreading model , as the recovery process of the Susceptible-Infectious-Susceptible model can be regarded in two ways . It can be seen as proper recovery , with the host clearing the virus but acquiring no immunity to reinfection . It can also be interpreted as the virus killing the host , and a new ( susceptible ) host filling its ecological space . In the absence of specific evidence [40] , we make the simplest assumption for transmission: the different types of viral particles are transmitted independently , so that the probability of concurrent transmission of two variants ( and wt ) is simply the product of the probabilities of the single events . We also assume that co-infection by wt and variants does not alter the infectious period , allowing us to model recovery at a rate μ for all infected hosts . In Methods and S1 Text we expand our analysis to account for nonindependent transmission , heterogeneous recovery rates , and the case when different variants compete for a limited carrying capacity within the host , due , for instance , to a limited number of viral particles a host cell can make per unit time . We show that all these additional features do not impact the qualitative behavior of our model , in agreement with what was previously found in [21] . When the virus is introduced into a susceptible population , it can either die out quickly and leave the system disease-free ( disease-free state , dfs ) , or reach endemicity . There are four possible endemic states , depending of which variants circulate . We equivalently use the term equilibria , as they are the stable equilibrium points of the spreading dynamics . The first one is wt , in which only the wt is prevalent , and the defective variants have died out . This case maps into an effective SIS model for the compartment ⌜wt⌟ . In the second one , hj , any defective segment can circulate alongside the wt because , roughly speaking , the transmissibility of the latter is so high that any defective variant can hijack it , with no need to complement the genome with other variants . In this case , we will likely see the circulation of a number of variants lower than v , as segments can go extinct without hampering the circulation of the remaining ones . The third endemic state , seg , witnesses the presence of all the v segmented variants without wt , and in this case complementation is essential . This state is an SIS model for the compartment ⌜seg⌟ . Finally , the state all exhibits circulation of the wt plus all the variants v . Borrowing some terminology from physics , we can then define different epidemic phases . Phases are regions of the space of model parameters . Inside each phase , the macroscopic behavior of viral spread is qualitatively the same . Specifically , we can define a phase in terms of which endemic states it allows . The parameter surfaces separating different phases are called phase transitions , the most important in epidemiology being the epidemic threshold . Below the epidemic threshold , only the dfs exists . Above it , the pathogen can circulate . There , we identify five other phases: wt-phase allows only wt; contingent-phase allows wt , hj and all; mix-phase allows wt , seg and all; seg-phase allows only seg; finally all-phase admits all the possible endemic states . Table 1 provides a schematic representation of the relationship between phases and endemic states . In the following , we analytically derive the critical surfaces that separate the different phases in the space of the parameters . This means that , given specific values of the parameters , the possible outcome of the spread can be predicted , thus characterizing the conditions leading to the persistence of multipartitism , and its nature . Then , using numerical simulations , we study the equilibrium prevalences of the endemic states , and their probability of occurring , for a representative set of parameter values . Firstly , however , we need to set up the theoretical modeling framework in terms of reaction-diffusion equations . For a generic v , we order the compartments by increasing number of viral species they contain , starting from ⌜wt⌟ , and ending with ⌜seg⌟ , ⌜all⌟ . For instance , for v = 3 , this would be ⌜wt⌟ , ⌜1⌟ , ⌜2⌟ , ⌜3⌟ , ⌜12⌟ , ⌜13⌟ , ⌜23⌟ , ⌜seg⌟ , ⌜all⌟ . Within the framework of heterogeneous mean field [41–43] , we divide hosts in classes according to their contact potential ( degree in the language of networks ) , so that if two hosts have degree k , h , respectively , their contact rate will be the product kh ( in the absence of degree-degree correlations ) . We assume hosts with the same degree are equivalent , and consider the prevalence per degree class . To this end , we define the variable x ν k as the prevalence of compartment with index ν and degree class k , i . e . , the fraction of the host population which has that degree , and finds itself in that compartment . In terms of x ν k , the equations describing the evolution of the disease are x˙νk=−μxνk+k〈 k 〉∑β[Γνβ ( 1−∑σxσk ) +∑σΛνβσxσk] ( ∑hhpγ ( h ) xβh ) , ( 1 ) where pγ ( k ) is the probability of a host having degree equal to k . We consider the homogeneous case , where all hosts have the same degree , so that pγ ( k ) = δk , 1 ( with no loss of generality we set it to 1 ) , and a highly heterogeneous case , where pγ ( k ) = Cγ k−γ is a power-law with exponent γ , and normalization constant Cγ . We denote 〈km〉 as the m-th moment of the degree distribution , computed as 〈km〉 = ∑k pγ ( k ) km , as usual . The term 〈k〉 appearing in Eq 1 is then the expected degree . The Greek indices β , ν , σ , run on all the infectious compartments defined before . The susceptible compartment is not included , as the number of susceptible hosts is completely determined by the other compartments , thanks to the assumption of constant population size . Γνβ is the rate of the transition ⌜β⌟⌜S⌟ → β⌜ν⌟ , i . e . , a transition affecting the prevalence of compartment ⌜ν⌟ through a contact between a host in compartment ⌜β⌟ and a Susceptible . Λνβσ encodes transmission rates among infected individuals , and specifically a transmission from ⌜β⌟ to⌜σ⌟ , that leads to the change of the prevalence of ⌜ν⌟ . The entries of Γνβ , Λνβσ are functions of λ , ρ and v . Eq ( 1 ) thus links the change in the number of hosts with a given degree , and in a compartment ( x ˙ ν k ) , to one reaction and two diffusion processes . The first term , μ x ν k , represents the decrease due to hosts recovering back to the susceptible state . The second one , with coupling constant Γ , contains the probability of a host , with degree k , being susceptible ( 1 - ∑ σ x σ k ) , and being infected by a host in compartment β , and degree h . The last term , with coupling constant Λ , has the same structure , but the target compartment is a generic infectious compartment σ , instead of the susceptible one . Both infection terms contain the term khpγ ( h ) /〈k〉 , which is the probability a host of degree k establishes a contact with a host of degree h , given a network with no degree-degree correlations [44] . The analytical approach to computing the critical surfaces consists in studying the linear stability of the different equilibria of Eq ( 1 ) . Instead , in order to compute the prevalence values and occurrence probability of these equilibria , we have to resort to stochastic spreading simulations . The extensive calculations are reported in Methods and S1 Text , as well as the explanation of the numerical simulations . The five phases are completely determined by three surfaces with tractable analytical expressions . They are T1 , above which the wt can spread on its own ( epidemic threshold for the compartment ⌜wt⌟ while alone ) ; T2 , above which segments circulate by hijacking the wt , and Ts , which is the epidemic threshold for the compartment ⌜seg⌟ circulating alone . The expressions we find are T 1 = { λ = μ ^ } ; ( 2 ) T 2 = { λ = 1 + ρ ρ μ ^ 1 + μ ^ } ; ( 3 ) T s = { λ = μ ^ 1 / v ρ } . ( 4 ) μ ^ is an effective recovery-rate embodying both the actual recovery rate , and the topology of the contacts: μ ^ = μ 〈 k 〉 / 〈 k 2 〉 . This entails an important scaling: recovery rate and topology never impact the critical points on their own , but always jointly as μ ^ . This fact was well-known in the case of the epidemic threshold , Eq ( 2 ) [41] . Here , we rigorously prove that it extends also to all other critical points . Given that homogeneous contact networks have 〈k〉 ∼ 〈k2〉 , the heterogeneous ( power-law-like ) network recovers the homogeneous case when γ → ∞ . Hence , the smaller the exponent γ , the more heterogeneous the contact network is , i . e . , hosts with a large number of connections become more likely . These hosts can reach a significant part of the population , and when infectious , they act as superspreades . They are responsible for causing μ ^ to go to zero ( μ ^ → 0 ) in the limit of large population size ( N → ∞ ) , when the exponent of the degree distribution respects 2 < γ < 3 . This implies not only that T1 goes to zero , as it is well-known [41] , but that T2 and Ts do it as well . However , while T2/T1 remains finite as μ ^ goes to 0 ( T1 and T2 go to zero at the same speed ) , we find that Ts/T1 → ∞ . This entails that Ts goes to zero more slowly , and increasingly so for higher v . The study of Eqs ( 2 ) – ( 4 ) reveals four regimes . For low or zero differential transmissibility ( ρ < μ ^ - ( v - 1 ) / v - ( 1 - μ ^ 1 / v ) ) , as λ increases , one crosses the wt-phase , then the contingent-phase and finally the all-phase ( see Fig 2A ) . For intermediate values of differential transmissibility ( μ ^ - ( v - 1 ) / v - ( 1 - μ ^ 1 / v ) < ρ < μ ^ - ( v - 1 ) / v ) , the mix-phase substitutes the contingent-phase . This can be seen in Fig 2B and 2C . Then , when μ ^ - ( v - 1 ) / v < ρ < μ ^ - 1 , increasing λ causes the system to be in the seg-phase , followed by the mix-phase and later by the all-phase ( see Fig 2B , 2C and 2D ) . Finally , for very high differential transmissibility ρ > μ ^ - 1 , the wt no longer spreads and the only possible phase is the seg-phase ( see Fig 2B and 2C ) . For any possible value of the parameters , Eqs ( 2 ) – ( 4 ) tell us which endemic states are possible , i . e . , which prevalences are higher than zero . They provide , however , no information about the values of such prevalences , which are , in principle , the solutions of the algebraic system obtained by setting x ˙ ν = 0 in Eq ( 1 ) . A closed-form solution of this system does not exist for heterogeneous networks . In the homogeneous case , while a complete analytical derivation of the endemic states is not possible , we can obtain two important results . Firstly , we notice that the total prevalence of the wt , i . e . , the fraction of hosts infected by it ( z = ∑ν ≠ ⌜seg⌟ xν ) , obeys an SIS dynamics ( see S1 Text ) with transmissibility λ , and can thus be computed as zwt = 1 − μ/λ . Secondly , when the whole set of segments circulates without wt ( as compartment ⌜seg⌟ ) , again the virus spreads as an SIS , this time with transmissibility ( ρλ ) v , and its endemic value can be predicted in the same fashion: z s e g = 1 - ρ ( ρ λ ) v . Interestingly , for high ρ , and a transmissibility λ > ρ−v/ ( v − 1 ) , it turns out that zseg > zwt: the prevalence of the multipartite form is higher than that of the wt . In order to fully characterize the endemic states , we resort now to stochastic spreading simulations ( see Methods ) , focusing on the bipartite case ( v = 2 ) . A higher number of variants ( v > 2 ) would not change the qualitatively picture; it would simply increase the possible values for the prevalence of hj by increasing the number of possible segments that survive through hijacking . We choose six points in the parameter space that lie in different phases ( see Fig 2E ) , and for those values we carry out the simulations . We firstly focus on homogeneous host population structures . The results are shown in Fig 3A . We characterize the endemic states in terms of their type ( see Table 1 ) , and plot their total prevalence , and the prevalence of the defective variants . In the points lying on the x-axis ( labeled by wt ) the defective variants have gone extinct , and the wt behaves like an SIS ( states wt ) . The points lying on the diagonal have witnessed the extinction of the wt , and the defective variants are circulating together in the ⌜seg⌟ compartment ( states seg ) . Their values match the theoretical prediction ( dashed vertical lines ) . The solid vertical lines in Fig 3A are the theoretical predictions of wt prevalence . They match all equilibria of type both wt and hj , as in those cases the total prevalence coincides with the prevalence of the wt . The states all , whose total prevalence cannot be predicted analytically , have the highest prevalence . This picture further confirms the relationship between the theoretically predicted phases and the allowed endemic states ( Fig 2D ) . Previously we have stated that the critical surfaces are not sensitive to recovery rate and topology separately , but only to the parameter μ ^ encoding both at the same time . Specifically , two populations with different recovery rate and contact heterogeneity , but with the same μ ^ , are indistinguishable from the point of view of their critical behavior . The endemic prevalences , however , break this symmetry , as one can see from Fig 3B , where we focus on P3 ( Fig 2D ) and get to μ ^ = 0 . 25 both with one homogeneous ( as in Fig 3B ) , and two heterogeneous population structures ( with exponents γ = 3 . 5 and γ = 3 . 2 ) . All the three configurations show all the equilibria , as expected by the critical behavior , but in the heterogeneous case the prevalence is consistently lower for each equilibrium . Up to now , we have identified the phases ( allowed endemic states ) and computed the prevalence of such states . We now focus on the probability of occurrence of each state . For each of the usual points in Fig 2E , we show the probability of reaching each equilibrium in Fig 3C . This is achieved by counting the number of stochastic realizations that , starting from similar initial conditions , lead to that specific equilibrium ( branching ratio of that equilibrium ) . Clearly , points P1 and P6 have only one endemic state , which then has a probability equal to one of being reached . For the other points , which have more than one possible endemic scenario , these probabilities are more informative , as they tell us the chances of the different viral forms taking over the population . We remark , however , that while both the critical behavior and the prevalence of the endemic states are inherent properties of the system that do not depend on the specific initial conditions chosen , this is not true for the probabilities of occurrence , which are clearly influenced by the initial infection status of the population . Given that , however , we computed them by seeding only one host in the ⌜all⌟ compartment to a susceptible population , we can say that our predictions are—at least qualitatively—reliable in an invasion scenario , in which the viral form is introduced by just one ( or few ) individuals . Using the analytical characterization of the endemic phases and the numerical study of the equilibria , we now can investigate under which conditions the interplay between spreading dynamics and topology of contacts leads to the rise and persistence of multipartitism . We can also determine the nature of such emergence , in terms of a commensal relationship with the wt , or a true competitive advantage at the ecological level . For the sake of simplicity , we start by considering no differential transmissibility ( ρ = 1 ) : wt and segments have the same transmission probability . The relevant figures are Fig 2A and 2B , points P1 , P2 and P3 in Figs 2E and 3 . In this scenario , three phases are possible , and one crosses them all by increasing the transmissibility λ . The first one ( λ just above T1 ) is the wt-phase , in which only the wt can circulate , and whenever a defective segment is produced , it quickly goes extinct . By increasing λ , we then encounter the contingent-phase . This phase predates the appearance of true multipartitism , as defective segments can hijack the wt to circulate . These segments cannot persist on their own , but the highly prevalent wt allows them to complete the replication cycle . At this stage , any defective segment is a commensal of wt , as the persistence of the former depends on the latter , while wt’s fitness remains unchanged . The emergence of multipartitism in this context is a contingent process: segments circulate simply because they are allowed to , causing no change to the overall fitness of the virus . Furthermore , there is no selective pressure towards complementation , as a combination of segments reconstructing the full genome without the presence of the wt ( compartment ⌜seg⌟ ) would not be able to persist . This is confirmed by the functional form of T2 in Eq ( 4 ) , which features no dependence on the number of complementing variants v: the survival of each mutant is independent of the presence of others , as effective replication and diffusion is wt-mediated . In other words , complementation would not make the variants fitter to the environment . A further increase in λ takes us to the all-phase . Here , in addition to the commensal relationship between wt and segments , complementing variants are able to circulate on their own , without wt: the equilibrium seg emerges . Selection then imposes a bias on those segments that together reconstruct the genome , as they represent a new effective spreading configuration , and increase the overall viral fitness . They are thus advantaged with respect to purely commensal segments . This fitness-enhancing effect is quite straightforward: let us suppose that , due to viral or host population bottlenecks , or another stochastic event , wt prevalence goes down drastically , to the point where it is cleared from the system . In the contingent-phase this would lead to complete viral extinction , as all the segments would die out , too , as their persistence is linked to wt’s . In the all-phase , on the other hand , the virus is more resilient , as it can still circulate thanks to complementation . This time selective pressure toward complementation is well visible in the expression of Ts in Eq ( 4 ) , which depends exponentially on the number of variants v . This fact is in qualitative agreement with results in [21] , where it was shown that the larger the number of segments , the harder to reach the persistence of the segmented variants within a host . Even if the multipartite genome does not enjoy any microscopic advantage , it can rise to fixation if the monopartite virus undergoes stochastic extinction . Though fluctuations would also affect the multipartite form , and stochastic extinction of the monopartite form is not very likely , similar scenarios are relevant in virus evolution [45 , 46] and cannot be discarded a priori . The analysis of the prevalences ( Fig 3A ) confirms the evolutionary drivers behind the different phases , and adds information regarding crossed effects between viral types . Moreover , it allows us to uncover an evolutionary potential for multipartitism even in the absence of an explicit microscopic advantage . Let us focus on the contingent-phase ( point P2 in Fig 3A ) , and the all state . The total prevalence of the latter state is higher than wt’s and hj’s in the same phase , implying that some hosts are infected by complementing segments without wt ( compartment ⌜seg⌟ ) , that is by a bona fide multipartite virus . Given that the multipartite form is not endemic in this phase—the contingent-phase relies on the wt for viral persistence— , this excess prevalence of the all state is a by-product of overall viral prevalence , rather than its driver: an extinction of the wt would quickly drive segmented variants to extinction . It is , however , an important one , as while it may not increase fitness in that specific environment , it permits the independent replication of the set of complementing variants; these are then able to invade other environments in which the wt could not persist , as we will see in the following . Let us examine the effect of a heterogeneous contact network on the phases and equilibria above . As we have explained , in the phase space , topology is encoded in the parameter μ ^ . A low epidemic threshold is a well known feature of heterogeneous networks [41 , 42 , 44] . Specifically , power-law networks with γ < 3 exhibit a vanishing threshold as they grow larger , as the emergence of highly connected hubs ensures the persistence of the disease at any value of transmissibility , that is 〈k〉/〈k2〉 → 0 ( and as a result , μ ^ → 0 ) as the number of potential hosts grows , N → ∞ . In our case this translates into T1 , which is the epidemic threshold , going to zero for μ ^ → 0 . Also T2 , Ts → 0 . Further information is obtained when comparing their limit behaviors . As μ ^ becomes smaller , T2/T1 increases but remains finite , while Ts/T1 → ∞ . This implies that , the higher the heterogeneity of the network is , the more difficult it becomes for multipartitism to persist . Specifically , reaching the all-phase from the wt-phase would require an infinite relative increase ( in the limit μ ^ → 0 ) in transmissibility . Even when heterogeneity is not severe enough so as to cause the threshold to vanish , i . e . , when γ > 3 , heterogeneity makes it harder to sustain multipartitism , as both T2/T1 and Ts/T1 are decreasing functions of μ ^ . Heterogeneity also modifies endemic prevalences and the branching ratios of equilibria . Let us examine point P3 ( all-phase in Fig 2E ) : when the network is homogeneous , the highest branching ratio corresponds to all , and the equilibria containing segments together ( hj ) happens 20% of the time . When the network is heterogeneous , this fraction decreases , and wt quickly overtakes all in being the most probable outcome . Summarizing , homogeneous contact patterns favor the emergence and persistence of multipartitism , while heterogeneous contacts hamper it . Qualitatively , this is the result of the complex interaction between the bottlenecks induced by between-host transmission and the presence of superspreaders , i . e . , hosts that can potentially infect a large fraction of the population thanks to their high number of contacts . Combining this mechanism with a low MOI—and hence low λ—predicts an evolutionary radiation of multipartite viral forms linked to the rise and intensification of agricultural practices . In crops and cultivars , contacts among hosts are much more homogeneous ( and often closer ) than in wild settings , tremendously alleviating the requirements imposed by co-infection . Multipartite viruses adapted to the patchy distribution of wild hosts could have found it easy to propagate in regular , monospecific host populations which in all cases have closely related wild forms from which they departed through artificial selection [47] . Up to this point , we have assumed that all viral forms have the same transmissibility and , still , fixation of the multipartite form cannot be fully discarded . Any additional advantage , however minor , of multipartitism will contribute to its ecological success , as we now discuss . We now set out to study the impact of enhanced transmissibility of defective variants , encoded in the parameter ρ being larger than one ( ρ > 1 ) . As ρ increases , the endemic state seg becomes more prevalent and more likely with respect to wt ( see Fig 3C and 3D ) . Specifically , the value of the transmissibility for which zseg > zwt decreases as ρ increases , facilitating the predominance of the multipartite form ( in Fig 3A point P3 has zwt < zseg , while P4 and P5 have zseg > zwt ) . Most importantly , ρ > 1 causes two new phases to emerge ( Fig 3B , 3C and 3D ) , and both facilitate the rise of multipartitism by eliminating hj from their possible equilibria . One is the mix-phase , in which the virus circulates either as wt or as a multipartite . The mix-phase also presents an all endemic state that results from the interaction between the two former equilibria . Unlike in the all-phase , however , here the all equilibrium no longer indicates commensal relation . The second emerging phase is the seg-phase , in which only the complemented multipartite virus is able to circulate , while the monopartite version quickly goes extinct ( yellow , and point P6 in Figs 2 and 3 ) . This phase is of paramount importance because it lies in a parameter region where , without developing multipartitism , the virus would not be endemic . In addition , by prescribing the exclusive presence of the multipartite form , it allows to explain the phenomenology observed in nature , as the simultaneous presence of monopartite and defective-complementing forms of the same virus has been observed only in vitro [20 , 48–50] . In vivo , viral species circulate as either pure monopartite ( endemic state wt ) , or pure multipartite ( seg ) . Furthermore , although in vitro several defective viral forms are generated and detected , and propagate along the wt ( which would correspond to an in vitro hj ) , this equilibrium is rarely found in wild plants . There is , however , an association between fully-fledged viruses and defective viral forms formally equivalent to the hj equilibrium: virus and viral satellites [51] . Often , in addition , satellites modify the aetiology of viral infections [52] , such that the transmissibility and the recovery rate might be affected by its presence in no particular direction , a phenomenon we do not consider in our model . There are other classes of hyperparasites that depend on a functional virus for replication ( e . g . virophages [53] or viroid-like satellites [54] ) whose ecological dynamics could , with appropriate modifications , be described in the framework discussed here . Interestingly , it has been proposed that virus-satellite associations , a typically unrelated tandem from a phylogenetic viewpoint , might evolve towards full co-dependence , and therefore be a possible , alternative evolutionary pathway to multipartitism [5] . Though our knowledge of existing viral forms is still incomplete and likely biased [16 , 55] , our results indicate that endemic states mixing monopartite and multipartite cognate forms ( hj , all ) need values of transmissibility difficult to sustain: endemicity could be achieved with lower values of transmissibility if the virus propagated only as a wild-type , while high values entail a cost that is usually compensated by decreasing infectivity [56] . Albeit rare , however , these endemic states might act also as a stepping stone towards multipartitism even if they are only transiently present , as in the following example . Consider a purely monopartite virus endemic in a plant population , in a specific environment , with parameters μ ^ A , λA as in point A in the inset of Fig 2C . μ ^ A is a combination of the recovery rate of the disease ( characteristic of the host-virus interaction ) , and the between-plant contact network , driven by plant distribution and vector movements . A second population , occupying an adjacent geographic area , may have a different parameter ( μ ^ B > μ ^ A ) , due to a different contact topology . As the inset of Fig 2C shows , the virus is able to colonize the second population only through an evolutionary process that increases its transmissibility up to at least λB′ , so that point B′ is above the epidemic threshold ( green path in the figure ) . It is reasonable to assume that the larger the increase in transmissibility required , the less likely this process is , given that the required mutation ( s ) are less likely and possibly more costly to maintain . The emergence of multipartitism decreases the evolutionary distance between the two states , increasing adaptability ( magenta path in the figure ) . Random mutations , in fact , need to increase transmissibility from λA ( wt-phase ) to λB < λB′ ( all-phase ) , where a complementing , multipartite version of the virus can emerge . Invasion of the second population is now possible , because the new viral forms effectively lowers the epidemic threshold in μ ^ B , thanks to the emergence of the seg-phase ( point B ) . This simplistic example not only shows that multipartitism can emerge as a fitness-enhancing feature , but also that coexistence of monopartite and multipartite forms is a key stage in the evolutionary process , albeit possibly transient and short-lived . In addition to outlining the adaptive potential of multipartitism , this example elucidates the hampering effect of network heterogeneity . By increasing the distance between the wt-phase and the all-phase , network heterogeneity reduces the ratio λB′/λB , making multipartitism less advantageous . In conclusion , while making viral persistence overall easier , network heterogeneity curbs the potential of multipartitism as an effective adaptation strategy . Multipartitism represents an example of a complex and as-of-today puzzling viral strategy . We have developed a framework that , starting from few key biological features , models the interaction between monopartite and multipartite forms , driven by the spreading dynamics on a host population . Despite assuming that multipartitism emerged from complementation between defective viral forms generated by the wt virus , as it has been observed in vitro , our results can be extended to other situations with relative ease . Most importantly , in addition , we have described how the structure of contacts among hosts drives the rise and persistence of the different viral forms . We have analytically characterized the parameter regions leading to viral persistence , in the form of wt only , of wt and defective segments , or segments only . We have also defined the different types of relationships between wt and segments , and specifically the presence or absence of selective pressure towards complementation , i . e . , to witnessing the circulation of defective variants that may cooperatively reconstruct the whole genome . We have corroborated these findings through stochastic numerical simulations aimed at computing the prevalence of the different endemic states , and their probability of occurrence . As a result , we have been able to identify under which ecological conditions would multipartitism be a successful adaptive strategy , in the presence or absence of microscopic advantages , to new external conditions and environments characterized by variations in the topology of contacts between hosts . Defective particles generated through replication errors would start circulating by hijacking the wt . Subsequently , a complementing set of variants might form . Once that situation is achieved , even a small advantage in transmissibility ( ρ > 1 ) would give an advantage to the multipartite form , which could anyway replace the monopartite form if chance causes the stochastic extinction of the latter . This sequence of events represents a plausible , parsimonious evolutionary pathway to the rise and persistence of multipartite viruses , and clarifies in which manner multipartitism might be an effective adaptive strategy at the ecological level . We have also uncovered that while heterogeneous contact patterns among hosts favor viral persistence in general , they give a higher advantage to monopartite forms , by limiting the evolutionary and adaptative potential of multipartitism . Our model clearly lacks specific biological features that characterize different viruses , but that is a strength rather than a weakness , as it can be applied to a wide variety of settings with appropriate minimal modifications . We nonetheless explore additional realistic features in S1 Text , as nonhomogeneous recovery rates and nonindependent viral transmission . Finally , it is worth discussing the effect of the interaction between the microscopic advantage and stochastic effects on multipartite fixation . The effect of the microscopic advantage , as quantified by our parameter ρ , becomes apparent in our current results , in terms of a much larger region of parameter space compatible with the fixation of the multipartite form ( compare , for instance Fig 2B and 2C ) . Assuming a microscopic advantage therefore leads to a competitive advantage of multipartitism . A quantitative estimate of this competitive advantage , however , would require accounting for stochastic effects , an endeavor that goes beyond the current approach . Despite not being able to formulate quantitative predictions , we are convinced that our framework provides an interesting qualitative picture of coexistence or substitution of different genomic architectures in a wide range of ecological environments . In this sense , we have uncovered evidence that the topology of contacts along which viruses spread may contribute to explaining why multipartite viruses preferentially infect plants . Our results lead us to conjecture that multipartite diversity and prevalence should have significantly increased together with the expansion of agriculture . Our goal is to derive the critical surfaces of Eqs ( 2 ) – ( 4 ) from the equation driving the dynamics of the system , namely Eq ( 1 ) . We do that by starting from a simpler scenario , and incrementally adding features , up to the full model . Specifically , the first step consists in solving the model with no differential degradation ( ρ = 1 ) , no contact heterogeneity , and with only one segmented variant ( v = 1 ) . In the second step we generalize the result to a generic v , and in the third one we allow for heterogeneous contacts . In the last step we add differential degradation . A numerical validation of the critical surfaces is carried out in S1 Text . ⌜wt⌟ and ⌜all⌟ are the only infectious compartments , with prevalence x1 and x2 , respectively . With neither differential degradation nor contact heterogeneity , Eq ( 1 ) reduces to { x ˙ 1 = λ ( 1 − x 1 − x 2 ) x 1 + λ ( 1 − λ ) ( 1 − x 1 − x 2 ) x 2 − λ x 1 x 2 − μ x 1 x ˙ 2 = λ 2 ( 1 − x 1 − x 2 ) x 2 + λ x 1 x 2 − μ x 2 . By summing these equations , we find that the equation for the total prevalence ( z = d e f x 1 + x 2 ) is z ˙ = λ ( 1 - z ) z - μ z . This also follows from noticing that for v = 1 the total prevalence is also the total wt prevalence ( see S1 Text ) . This means that the total prevalence behaves as a standard SIS , for which we know the epidemic threshold T1 = {λ = μ} , and the equilibrium above it . In addition , we know that just above T1 we are in the wt-phase . Hence , we have ( zwt = 1 − μ/λ , x2 , eq = 0 ) . Studying the stability of this equilibrium gives us T2 . Since the equation for z decouples from x1 and x2 , it is convenient to study the system in ( z , x2 ) . Studying the sign of the eigenvalues of the Jacobian matrix reduces to studying ∂ x ˙ 2 / ∂ x 2 < 0 calculated in the equilibrium . This gives T2 = {λ = 2μ/ ( 1 + μ ) } . The details of the calculation are reported in the S1 Text . We now generalize the previous result to an arbitrary v , while still assuming that all hosts have the same contact rate , that we can set to one with no loss of generality . Eq ( 1 ) simplifies to x ˙ ν = ∑ β σ Λ ν β σ x β x σ + ∑ β Γ ν β x β ( 1 − ∑ σ x σ ) − μ x ν , ( 5 ) whose Jacobian matrix is J ν β = ∂ x ˙ ν ∂ x β = ∑ σ ( Λ ν ( β σ ) - Γ ν σ ) x σ + Γ ν β ( 1 - ∑ σ x σ ) - μ δ ν β , ( 6 ) where Λν ( βσ ) = Λνβσ + Λνσβ . Firstly , we note that for v > 1 the total prevalence , now defined as z = ∑ν xν , no longer behaves like an SIS , due to the presence of the compartment ⌜seg⌟ . Indeed , one can show that , when summing over ν in Eq ( 1 ) , the terms with Λ cancel out , as they pertain to interaction exclusively among infectious compartments , which by definition cannot change the total prevalence , and so all the contributions must cancel out . This is not the case however for the terms with Γ , so that the final equation is z ˙ = ( 1 - z ) ∑ β ( Γ x ) β - μ z , which does not decouple from xν . Interestingly , despite this breaking of the SIS symmetry , which was crucial to solve the v = 1 model , we can still prove that the values of T1 , T2 found for v = 1 generalize to an arbitrary number of variants . We start from the first critical surface ( T1 ) . We compute the Jacobian matrix of Eq ( 6 ) in the dfs , i . e . , xβ = 0∀β . We get J ( dfs ) = Γ − μ . We now argue that Γ , and therefore the Jacobian matrix , is upper triangular , thanks to the specific ordering of the compartments that we introduced . Γνβ is the rate at which a susceptible becomes a ⌜ν⌟ , upon contact with a ⌜β⌟ . For this to happen ( Γνβ > 0 ) , ⌜β⌟ must contain at least all the viral species ⌜ν⌟ contains . Hence , either β = ν ( diagonal term ) , or β > ν . By the same reasoning , the diagonal terms are Γββ = λφ ( β ) , where φ ( β ) is the number of viral species in ⌜β⌟ , e . g . φ ( ⌜wt⌟ ) = 1 , φ ( ⌜all⌟ ) = v+ 1 . From these considerations , the spectrum of J ( dfs ) is {λφ ( β ) −μ;∀β} . Keeping in mind that λ < 1 , we recover the first critical point: T1 = {λ = μ} . Just above T1 , ⌜wt⌟ is the only compartment with prevalence different from zero , hence it behaves like a standard SIS . Thanks to that we can compute Eq ( 6 ) in the wt-phase , and its spectrum . From that we find that the second critical surface is the same as for v = 1 . The details of the calculation are in S1 Text . We now build on the previous results , by adding heterogeneous contact rates . We work in the widely-used degree-block approximation [41–44 , 57] , assuming the contacts among agents are represented by an annealed network in which we assign each node a degree sampled from a power-law distribution with exponent γ: pγ ( k ) = Cγ k−γ , where Cγ is the normalization factor . As customary , we assume γ > 2 , so that the average degree is defined in arbitrary large populations . In the framework of annealed networks it makes sense to interpret k as a discrete number; one could also interpret it as a ( continuous ) coupling potential ( either choice does not change the result found ) . We now directly compute the Jacobian of Eq ( 1 ) , reported in Eq . ( S . 16 ) of S1 Text . The Jacobian is a matrix acting on a space which is the tensor product of the space of compartments , spanned by the Greek indices , and the space of degrees , spanned by the Latin indices . We can study its spectral properties on each space separately , using the previous results for the space of compartments . The full derivation is reported in the S1 Text . Differential degradation ρ > 1 changes the matrices Γ , Λ , as reported in the S1 Text . The derivation is then similar to the case with ρ = 1 . Our model assumes that the transmission probability of one variant does not depend on the coinfecting variants . In reality , however , the number of viral particles a cell can produce in time is limited , and they are known to often spread in superinfection units . In S1 Text we investigate these aspects using a simple assumptions . We show that despite altering the specific values of the critical surfaces , they do not impact the qualitative behavior of the model . Data in Fig 3 are produced through stochastic spreading simulations . Starting with a population of N = 6000 , we infected the hosts with wt and both segments ( ⌜all⌟ for v = 2 ) , and let the virus spread . We used an adaptation of the Gillespie algorithm [58 , 59] , to model both contacts among hosts , and contagion and recovery events . For each parameter configuration , we carried out 5000 simulations and kept only those reaching an endemic state other than the disease-free state , in order to discard instances of stochastic extinction , and focus only on the metastable equilibria which represent the attractors of the equations . We then used those simulations to compute prevalences and occurrence probabilities .
Viruses typically consist of some genetic material wrapped up in a single particle , the capsid . Multipartite viruses follow another lifestyle . Their genome is made up of several segments , each packed in independent particles . However , since the completion of the viral cycle requires the full genome , these particles need to coinfect each host . This imposes strong constraints on the minimum number of independently transmitted particles , making the rise and persistence of multipartitism an evolutionary puzzle . By using analytical and numerical tools , we study the ecological interaction between monopartite and multipartite forms , in terms of their ability to spread on , and take over , a host population . We reveal that this interaction can take various forms ( competition , cooperation , commensality ) , depending on the underlying structure of contacts among hosts . We also find that , in some situations , multipartitism represents an effective adaptive strategy , allowing the virus to colonize environments in which the monopartite form cannot thrive . Finally , we uncover that contact structures typical of farmed plants favor multipartitism , suggesting a correlation between the intensification of agricultural practices and an increase in the diversity and prevalence of multipartite viral species .
You are an expert at summarizing long articles. Proceed to summarize the following text: Mutation signatures in cancer genomes reflect endogenous and exogenous mutational processes , offering insights into tumour etiology , features for prognostic and biologic stratification and vulnerabilities to be exploited therapeutically . We present a novel machine learning formalism for improved signature inference , based on multi-modal correlated topic models ( MMCTM ) which can at once infer signatures from both single nucleotide and structural variation counts derived from cancer genome sequencing data . We exemplify the utility of our approach on two hormone driven , DNA repair deficient cancers: breast and ovary ( n = 755 samples total ) . We show how introducing correlated structure both within and between modes of mutation can increase accuracy of signature discovery , particularly in the context of sparse data . Our study emphasizes the importance of integrating multiple mutation modes for signature discovery and patient stratification , and provides a statistical modeling framework to incorporate additional features of interest for future studies . Patterns of mutation in cancer genomes reflect both endogenous and exogenous mutagenic processes [1] , allowing inference of causative mechanisms , prognostic associations [2] , and clinically actionable [3–6] vulnerabilities in tumors . Many mutational processes leave distinct genomic “footprints” , measurable via nucleotide substitution patterns [1] , localised mutation densities , and patterns of structural variation ( SV ) . As such , each mutagenic source ( whether exogenous or endogenous ) changes DNA in a characteristic manner , at genomic locations with preferred chemical and structural characteristics . Exogenous insults such as ultra-violet radiation and tobacco smoke-associated mutagens ( e . g . benzo[a]pyrene ) induce single nucleotide variants ( SNVs ) with characteristic C→T ( at CC or TC dinucleotides ) [7] and C→A mutation patterns [8] , respectively; endogenous APOBEC activity mediates enzymatic 5-methylcytosine deamination , resulting in C→T substitution patterns at TC dinucleotides [7] . Cancer cells can also acquire endogenous mutator phenotypes , accumulating mutations [7] due to DNA repair deficiencies . Defective DNA repair processes induce both point mutations and structural variations [9] , and include several mechanistic classes such as mismatch repair deficiency ( MMRD ) , homologous recombination deficiency ( HRD ) , microhomology mediated end-joining , and breakage fusion bridge processes . Defective DNA repair has been exploited in therapeutic regimes , including immune checkpoint blockade for mismatch repair deficiency [6] , and synthetic lethal approaches for HRD [4 , 5] , underscoring their clinical importance . Both point mutation signatures [10] and structural variation signatures [11] have been studied extensively as independent features of cancer genomes , mostly through non-negative matrix factorization ( NMF ) approaches [1 , 3 , 12–15] . As increasing numbers of whole genomes are generated from tumors in international consortia and focused investigator research , the need for robust signature inference methods is acute . Additional computational methods have been proposed [16–19] , however no approaches jointly infer signatures from both point mutation and structural variations . We contend that systematic , integrative analysis of point mutation and structural variation processes enhances ability to exploit signatures for subgroup discovery , prognostic and therapeutic stratification , clinical prediction , and driver gene association . Latent Dirichlet allocation ( LDA ) [20] , a popular and effective approach for natural language document analysis , is well suited to the task of mutation signature inference . Although LDA was designed to extract topics from documents , these concepts can be mapped to mutation signatures and somatic mutation catalogues derived from tissue samples , respectively . In this paper we introduce the correlated topic model ( CTM ) [21] , an extension of LDA which incorporates signature correlation , and a multi-modal correlated topic model ( mf-CTM . dt in Salomatin et al . [22] , hereafter referred to as MMCTM ) . A modality is a particular kind of data , and in this report SNV and SV counts are two distinct modalities . The MMCTM thereby jointly infers signatures from multiple mutation types , such as SNVs and SVs . Signature correlations can arise through a mutational process generating multiple signatures , as with the HRD-associated SNV and SV signatures . C→T substitutions caused by APOBEC cytidine deaminases have also been shown to cluster around SV breakpoints [12] . Correlations between mechanistically independent signatures can also occur; for example , COSMIC SNV signatures 1 and 5 are both correlated with age of diagnosis in some cancer types [23] . We set out to investigate whether statistical modeling that could encode correlations between signatures could enhance accuracy in signature analysis . We show how integrating SNV and SV signature probability correlation improves mutation signature inference relative to NMF and standard topic modeling methods . By incorporating statistical correlation and multiple modalities , more information is provided to the model , improving inference further , while still maintaining distinct signatures for each modality . Motivated by the need to better understand mutation signatures in the context of DNA repair deficiency , we analysed breast and ovarian tumour genomes . We applied the MMCTM to SNV and SV somatic mutations derived from whole genomes ( breast [13] and ovarian [2]; 755 samples total ) , performing joint statistical inference of signatures . Our results reveal correlated topic models as an important analytic advance over standard approaches . Rigorous benchmarking over mutation signatures inferred from previously published mutation corpora was used to establish metrics for comparison . We show systematically how correlation integration improves inference , especially in the context of sparse mutation counts , and where SNVs and SVs are considered jointly . In addition , we report novel strata using MMCTM-derived signatures , including patient groups exhibiting combined whole genome SNV and SV signature profiles from breast and ovary cancers . We automatically recovered BRCA1-like and BRCA2-like homologous recombination repair deficient breast and ovarian cancers , where the tumors bearing the well known SNV HRD signature were reproducibly split on the basis of SVs . In aggregate , our study reveals the importance of simultaneously considering multiple classes of genomic disruption as a route to expanding mutation signature discovery , and their downstream impact on novel stratification across human cancers . We developed a suite of probabilistic correlated topic models ( Fig 1 ) to evaluate their utility in signature discovery . We describe the models here briefly and refer to S1 Text for more detailed descriptions . Topic models represent mutation signatures as discrete distributions over unique mutation categories ( e . g . C→T substitutions at TCT trinucleotides ) . Each sample is then represented as a discrete distribution over signatures . How the sample-signature distributions are generated differ between LDA ( Fig 1a and 1d ) and the correlated topic models . In LDA , this variable is drawn from a Dirichlet distribution [20] . With the correlated topic models , however , it comes from the transformation of a variable that is distributed according to a multivariate Gaussian distribution [21] ( Fig 1b , 1c and 1d ) . By using the multivariate Gaussian , the covariance of signature probabilities across samples can be captured . The multi-modal extension of the CTM ( i . e MMCTM ) encodes mutation counts and signatures for different modalities ( e . g . SNVs and SVs ) independently , except for the sample-signature probabilities which are all modeled using the same Gaussian distribution , allowing for cross-modality correlations . We also developed a set of “independent” feature models based on the method introduced by Shiraishi et al . [16]—independent-feature LDA , CTM , and MMCTM ( ILDA , ICTM , IMMCTM , S1 Fig , S1 Table , S1 Text ) . These models can treat each mutation feature ( e . g . substitution type , flanking nucleotide ) independently . That is , one feature for the mutation itself ( say , C→T ) , and features for each piece of contextual information ( e . g . 5′ A and 3′ G ) . Using this scheme , we drastically reduced the number of feature values: assuming 6 SNV types , and 2 flanking nucleotides the number of feature values is reduced from 6 * 4 * 4 = 96 to 6 + 4 + 4 = 14 [16] . We studied mutation signatures in 560 breast [13] and 195 ovarian [2 , 24] cancer genomes ( S2 Table ) . Each dataset was analyzed separately to avoid biases from differences in sample sequencing , data-processing or annotation . We constructed SNV features using the 6 types of pyrimidine-centric substitutions ( C→A , C→G , C→T , T→A , T→C , T→G ) , and their flanking nucleotides . For example , a C→T substitution with an upstream A and downstream G is represented as the item “A[C→T]G” . We defined SV features by rearrangement type ( deletion , tandem duplication , inversion , foldback inversion ( FBI ) , translocation ) , number of homologous nucleotides around the breakpoints ( 0–1 , 2–5 , >5 ) , and breakpoint distance ( <10kbp , 10–100kbp , 100kbp–1Mbp , 1–10Mbp , >10Mbp , except for translocations ) . Foldback inversions are inverted duplications caused by breakage-fusion-bridge cycles . We then computed counts of mutations , categorized as described above . The resulting count matrices were provided as input to LDA , CTM , MMCTM , and NMF ( S1 Table ) . We compared NMF to the LDA , CTM , and MMCTM topic models . As NMF is commonly applied to normalized mutation counts , we also compared output from this alternative NMF procedure ( NMF-NORM ) . Each method was run on input mutation counts constructed in an identical manner ( e . g . for SNVS , 96 counts for each sample ) , and methods were compared using three different benchmarks: i ) average per-mutation predictive log-likelihood; ii ) logistic regression prediction accuracy of HRD labels; and iii ) the mean absolute error of inferred solutions compared to a synthetic reference dataset . For log-likelihood comparisons , we performed 5-fold cross validation , repeated 10 times , on the 560 breast cancer dataset . In each comparison , we fit SNV and SV signatures to four folds , leaving out a test fold ( 112 samples ) . We split mutation counts from each test fold sample into two parts , inferred sample-signature probabilities with one portion , and computed average per-mutation predictive log-likelihood values with the other portion . By evaluating each method on data different than those used for parameter estimation , we alleviated the risk of over-fitting parameters . This evaluation procedure only required estimated mutation signatures and sample-signature probabilities from each method , and did not depend on other model details , e . g . signature correlation structure . The average per-mutation predictive log-likelihood is an established comparison metric used in the topic modeling literature [25–27] , and is also not directly optimized by any method here ( unlike e . g . reconstruction error which is directly minimized by NMF ) . Although a likelihood-based metric may seem more applicable to the probabilistic models , NMF can be interpreted as maximum likelihood estimation of the “signature” and “activity” matrices under certain conditions ( e . g . using Euclidean distance for the cost function maps NMF to a Gaussian emission model ) [17 , 28] . We first compared performance as a function of the number of signatures , fitting models over a range of 2–12 SNV and SV signatures ( Fig 2a , S1 Dataset ) . For SNV signatures , LDA , CTM , and MMCTM performed similarly , and were consistently higher than the NMF methods across the full range of signature numbers . For SV signatures , the probabilistic topic models’ performance was consistently higher than the NMF models , and improved until a plateau was reached with an inflection point at 5 . Within the topic models , the CTM and MMCTM showed better performance than LDA . NMF-NORM performance degraded with >5 signatures , and NMF performance degraded with >6 signatures . Correlated topic models performed better than their non-correlated analogues at inferring SV signatures , possibly due to relatively low input counts for SV features . To explore this further , we compared performance over a range of mutation count fractions ( Fig 2b , S2 Dataset ) . When subsetting SNV counts , LDA , CTM , and MMCTM performed roughly equally until only 1% of mutation counts were retained , at which point LDA performance became worse than the CTM and MMCTM . With fewer SV counts , the MMCTM performed better than the CTM , and both outperformed LDA . Importantly , correlated topic models were the least affected by reducing mutation counts , whereas NMF-NORM exhibited the worst performance decline , indicating that correlated models were in general more robust to data sparsity . Further , fixing the MMCTM covariance matrix during inference reduced it’s performance with fewer counts ( Fig 2c , S2 Dataset ) , underlining the benefit of modeling signature correlations . We next compared the ability of these methods to provide informative , low-dimensional representations of samples , using signatures to stratify patients ( Fig 3 , S3 Dataset ) . We trained each method 10 times with random initializations on the full breast cancer dataset . We then trained a logistic regression classifier with the per-sample signature probabilities from each run as input features , and published labels from HRDetect [3] . HRD prediction accuracy scores were computed using 5-fold cross-validation . When the classifier was trained on only SNV signature probabilities , LDA , CTM , and MMCTM performed equally well . NMF and NMF-NORM generally performed worse . With SVs , the MMCTM signature probabilities provided the best accuracy , followed by the CTM and LDA . When the classifier was trained on both SNV and SV signature probabilities , the CTM and MMCTM performed better than other methods , further supporting the advantage of correlated models . We then tested each method on a simulated dataset based on SNV and SV counts from 560 breast cancers [13] ( Fig 4 , S2 Fig , S4 Dataset ) . Briefly , we used NMF to fit signature probabilities to a set of distinct SNV and SV signatures previously identified in this dataset ( COSMIC 1 , 2 , 3 , 13; RS 1 , 2 , 3 , 5; see Methods ) [13] . We note that using NMF-based signatures and estimated signature probabilities likely biased results in favour of NMF . Using the signatures , estimated signature probabilities , and mutation counts per sample , we generated 20 new sets of counts ( 560 synthetic tumour samples each ) by sampling from a Poisson distribution . We then repeated the experiment by generating synthetic datasets with only 1% and 10% of the original SNVs and SVs . Signatures and signature probabilities were estimated using each method , selecting the best solution from 500 restarts , and the mean absolute error ( MAE ) was calculated between estimated and reference values . While all methods generally performed well at recovering SNV signatures ( all median MAE <0 . 01 , except for LDA in COSMIC 2 with 1% counts ) , NMF-NORM performed worst at inferring SV signatures ( adjusted t-test p-values <0 . 05 , S2 Fig ) . The relatively low MAE even with reduced mutation counts also indicated that these methods are able to detect similar signatures as with a full set of mutations . Considering signature probabilities with full counts ( Fig 4 ) , NMF performed best for COSMIC 1 ( except v . s . NMF-NORM ) , COSMIC 3 , COSMIC 13 , and the SV signatures , except v . s . CTM in RS 3 ( adjusted t-test p-values <0 . 05 ) . NMF-NORM was worst for COSMIC 2 , 3 , and 13 ( adjusted t-test p-values <0 . 05 ) . However , with 1% of the original SNV counts , the MMCTM did better than other methods for COSMIC 1 , 3 , & 13 , and both the MMCTM and CTM did best for COSMIC 2 ( adjusted t-test p-values <0 . 05 ) . With 10% SV counts , the MMCTM did best for RS 2 and 5 . The CTM and MMCTM both did better than other methods for RS 1 and 3 ( adjusted t-test p-values <0 . 05 ) . The performances of the independent-feature models ( ILDA , ICTM , IMMCTM ) were also robust to low mutation counts , as previously described [16] , and they typically worked well for SV signature estimation . However , they are generally worse than the MMCTM at inferring SNV signatures , and were not considered for subsequent analysis ( S3 and S4 Figs ) . Overall , correlated topic models produced superior predictive mutation signature distributions and low-dimensional representations of samples . This was especially true when each sample had few mutations , as for SVs . We also found similar patterns in log-likelihood comparisons using the smaller ovarian cancer dataset ( S4 Fig ) , except we detected no major differences between the CTM and MMCTM . Performance of probabilistic topic models was stable across a range of topic hyperparameter values ( S3d Fig ) , and across random restarts compared to NMF ( S5 Fig ) , although randomization schemes differ across these two classes of methods . We next analysed mutations from the 560 breast cancer genomes [13] with the MMCTM for stratification analysis ( S6a Fig ) . We simultaneously fit 6 SNV and 7 SV signatures to counts of SNVs and SVs ( Fig 5a and 5b , S7 Fig , S5 Dataset , see Methods for signature count selection ) . We found SNV signatures similar to those previously identified with proposed etiologies ( S8 Fig ) , including the age-related ( Age , COSMIC 1 ) , APOBEC ( APOBEC-1 & APOBEC-2 , COSMIC 2 & 13 ) , MMRD ( COSMIC 20 ) , and HRD ( COSMIC 3 ) signatures . Additionally we found an SNV signature of unknown etiology , UNK ( COSMIC 17 ) . We identified SV signatures including small , medium , and large tandem duplications ( S-Dup , M-Dup , L-Dup ) , deletions ( Del ) , intrachromosomal SVs ( Intra-Chr & L-Intra-Chr ) , and translocations ( Tr ) . Some signatures were more likely to co-occur in the same tumour , possibly reflecting common etiology . According to the MMCTM model , the two APOBEC signatures were positively correlated ( Pearson’s r = 0 . 34 ) ( Fig 5d , S6 Dataset ) , and the HRD SNV signature was positively correlated with the S-Dup signature ( r = 0 . 3 ) , as expected . The Age signature was positively correlated with Intra-Chr ( r = 0 . 66 ) , L-Intra-Chr ( r = 0 . 53 ) , and Tr ( r = 0 . 38 ) SV signatures . We next performed unsupervised clustering over tumours on joint per-tumour SNV and SV signature probabilities ( Fig 5c , S6b and S9 Figs , S7 and S8 Dataset , see Methods ) . The resulting 7 groups included two ( clusters 1 & 2 , n = 164 & 147 ) enriched for the Age signature ( see S10a Fig , S9 Dataset for significant cluster-signature associations ) . Cluster 1 was enriched for the Tr signature , and both clusters 1 & 2 were enriched for Intra-Chr and L-Intra-Chr . While the Age signature was most correlated with patient age at diagnosis ( r = 0 . 23 , adjusted p-value << 0 . 0001 ) , Intra-Chr was second most correlated ( r = 0 . 20 , adjusted p-value << 0 . 0001 ) . Cluster 1 was associated with Luminal A cancers with relatively fewer SNVs , and contained tumours from generally older patients ( see Fig 5e , S10 Dataset for significant cluster-annotation associations ) . This implies that older patients may be more likely to have accumulated SVs in their cancers’ etiology as function of background rates , indicating a putative SV-related age signature for breast cancer . We also observed clusters with BRCA1/BRCA2 mutations and methylation ( clusters 3 & 4 , n = 79 & 71 ) , as previously described [13] . These tumours typically exhibited an HRD phenotype , and had elevated probability of the HRD SNV signature . Cluster 3 was associated with the S-Dup & M-Dup SV signatures , and more BRCA1 , RB1 , and PTEN driver mutations than expected by chance . As expected , cluster 3 patients were predominantly from the Basal PAM50 class . Cluster 4 was associated with the Del signature , and BRCA2 mutation . In contrast to cluster 1 , patients in cluster 3 also tended to be younger than patients in other clusters . The majority ( 87% ) of BRCA1/2 samples fell into clusters 3 & 4 , although BRCA1/2 mutant tumours that fell outside these clusters often had evidence of HRD , albeit with increased probability of unrelated signatures ( e . g . L-Dup in cluster 6 ) . Of patients predicted by HRDetect [3] to harbour HRD , 97% fell within the BRCA1/2 ( clusters 3 & 4 ) groups , demonstrating that the MMCTM output provides a substrate upon which known biological clusters are recovered , with further stratification as a result of SNV and SV integration . Cluster 5 ( n = 62 ) was enriched for the APOBEC-1 , APOBEC-2 , Intra-Chr , and L-Intra-Chr signatures , and was also enriched for HER2-positive tumours , relating Her2-amplification and APOBEC deamination processes for approximately 11% of breast cancers , as previously reported [29] . Cluster 6 ( n = 29 ) was the only group enriched for L-Dup , and also contained older patients than expected by chance . Cluster 7 ( n = 8 ) was associated with defective DNA mismatch repair ( MMRD ) , and the MMRD SNV signature , consistent with previous reports [30] . A recent analysis of ovarian tumours revealed a novel high-grade serous ovarian carcinoma ( HGSC ) sub-group with relatively worse prognosis , characterized by increased frequency of foldback inversions ( FBI ) [2] . Their analysis combined NMF-based SNV signature analysis with ad-hoc SV and copy number variant ( CNV ) features . Here we expanded on some of their findings using the MMCTM on a merged data set consisting of 133 samples from Wang et al . [2] and 62 samples from the International Cancer Genome Consortium ( ICGC ) ovarian cancer whole genome dataset [31] . We fit 6 SNV and 7 SV signatures to mutation counts from the 195 ovarian cancer genomes ( Fig 6a and 6b , S11 Fig , S5 Dataset , see Methods for signature count selection ) , including endometrioid carcinomas ( ENOC ) , clear cell carcinomas ( CCOC ) , granulosa cell tumours ( GCT ) , and HGSC ( S2 Table ) . Amongst the resultant SNV signatures were the previously described Age ( COSMIC 1 ) , APOBEC ( COSMIC 13 ) , HRD ( COSMIC 3 ) , MMRD-1 ( COSMIC 20 ) , MMRD-2 ( COSMIC 26 ) , and POLE ( COSMIC 10 ) signatures ( S8 Fig , see also for a comparison to the breast SNV signatures ) . The SVs included signatures for small , medium , and large tandem duplications ( S-Dup , M-Dup , L-Dup ) ; deletions ( Del ) ; FBI , inversions , and deletions ( FBI/Inv/Del ) ; intrachromosomal SVs ( Intra-Chr ) ; and translocations ( Tr ) . The association of deletions with FBI can be understood in terms of the underlying cause of FBI: breakage-fusion-bridge cycles . After the loss of a telomere , sister chromatids fuse and are then pulled apart during mitosis , producing one chromosome with a foldback inversion and another with a terminal deletion . We clustered the tumours according to their joint standardized SNV and SV signature probabilities , which resulted in 11 groups ( Fig 6c , S12 Fig , S7 and S8 Dataset ) . While the original study identified one HRD signature group [2] , our analysis here produced two major HRD clusters ( 1 & 4 , n = 34 & 23 ) , roughly defined by tumours with S-Dup and M-Dup ( see S10 Fig , S9 Dataset for cluster-signature associations ) coupled with loss of BRCA1 ( see Fig 6d , S10 Dataset for cluster-annotation associations ) , and small deletions ( Del ) coupled with loss of BRCA2 , respectively . The association of BRCA1/2 status with tandem duplication and deletion SV signatures has been reported in breast cancer tumours [13] , and was reflected in our analysis of the 560 breast cancer dataset ( Fig 5 , described above ) , providing strong evidence for BRCA1-like and BRCA2-like HRD sub-strata crossing tumour types . Cluster 2 ( n = 32 ) , 5 ( n = 20 ) , 7 ( n = 14 ) , and 9 ( n = 8 ) were all enriched for the FBI/Inv/Del signature . Cluster 9 also included all microsatellite instable ( MSI ) ENOC tumours , and was also associated with MMRD-1 , Age , and Del signatures , along with higher numbers of SNVs , and KMT2B and RPL22 mutations . Cluster 3 ( n = 25 ) contained mainly CCOC and ENOC tumours enriched for the Age , L-Dup , and Tr signatures . Cluster 6 ( n = 16 ) included tumours highly enriched for APOBEC signature probability . Cluster 7 ( n = 14 ) was associated with the HRD SNV signature as well as Del and FBI/Inv/Del . Cluster 8 ( n = 11 ) was only enriched for the Intra-Chr signature . Cluster 10 ( n = 6 ) was similar to the BRCA1 cluster ( 1 ) , but was more strongly associated with the M-Dup signature . Another small cluster of mainly HGSC tumours ( 11 , n = 6 ) , was associated with higher probability of the L-Dup signature , and CDK12 mutations , an association supported by a previous study [32] . By inspecting the signature correlations output by the MMCTM model ( Fig 6g , S6 Dataset ) we saw that the HRD SNV signature was positively correlated with the S-Dup ( r = 0 . 12 ) signature , as may be expected from the underlying biology of these signatures . The Age signature is positively correlated with the L-Dup ( r = 0 . 45 ) and FBI/Inv/Del ( r = 0 . 37 ) signatures . MMRD-1 is positively correlated with the S-Dup ( r = 0 . 26 ) , and Del ( r = 0 . 53 ) SV signatures . HGSC patient groups , defined by their standardized mutation signature probabilities , differed in survival rates . We defined 5 HGSC groups ( see Methods ) , representing BRCA1-mutant ( clusters 1 , 10; n = 36 ) , BRCA2-mutant ( cluster 4 , n = 19 ) , FBI ( clusters 2 , 5 , 7; n = 50 ) , Intra-Chr ( cluster 8 , n = 8 ) , and CDK12-like tandem duplicator tumours ( cluster 11 , n = 5 ) . We compared overall-survival amongst the HGSC super-clusters using the Kaplan-Meier method ( Fig 6e and 6f ) . The BRCA2/deletion cluster had the highest survival rate , while the CDK12/tandem duplicator group had the worst . Comparing the HGSC clusters in a pairwise fashion , the CDK12 group had worse survival than the BRCA1 and BRCA2 groups ( adjusted log-rank p-value < 0 . 05 ) . The FBI group had worse survival than the BRCA2 group ( adjusted log-rank p-value < 0 . 05 ) . The BRCA1/tandem-duplication group had an intermediate survival rate , but the survival curve was not significantly different than those of the FBI or BRCA2 groups ( adjusted log-rank p > 0 . 05 ) . While FBI was previously identified as a marker for poor prognosis [2] , activity of a mutational process linked with loss of CDK12 and producing 100kbp–1Mbp tandem duplications could indicate even worse outcomes . Overall , the MMCTM analysis represented a refinement of signature-based prognostic stratification in HGSC indicating BRCA2-like HRD as the best performing group of patients , followed by BRCA1-like HRD , Intra-Chr , FBI , and CDK12-like tandem duplicators . To evaluate the reproducibility of signatures inferred using the MMCTM , we applied the method to the two independent HGSC datasets included in our ovarian cancer analysis above . Specifically , 59 samples previously published by our group , and 62 samples from ICGC . Each HGSC group contained signatures that were similar between both groups , including HRD associated SNV and SV signatures . Both groups also showed a segregation of BRCA1- and BRCA2-like cases based on per-sample signature probabilities ( S13 and S14 Figs ) . We also compared SNV signatures inferred in the ovarian and breast datasets to each other and to the COSMIC signatures ( S8 Fig ) . In both datasets we found signatures similar to those previously reported to occur in ovary and breast cancers [2 , 10 , 13] , including the APOBEC; HRD; and age-associated signatures , demonstrating the ability of the MMCTM to capture established signatures . Through integrated statistical inference and analysis of SNV and SV mutation signatures , our results reveal at once correlated signatures and patient stratification within DNA repair deficient tumours . Our findings have several implications for the field . The use of structural variations in signature analysis is less common than for point mutations , in part due to the relative paucity of whole-genome sequencing datasets . Here , we show the significant new value from their joint interpretation , and set the framework for their simultaneous consideration across a broad range of tumour types . Moreover , our results demonstrate that correlated statistical modeling improves signature inference in the context of sparse mutation counts . The HRD point mutational signature is well described , but automated association of tandem duplications within BRCA1-like and interstitial deletions within BRCA2-like cancers represents an important refinement , reproduced here in two independent cancer types , with data from two independent studies . Furthermore , we show in the ovarian cancer cohort how this has prognostic implication , superseding what could be derived from gene-based biomarkers ( i . e . if only BRCA1 and BRCA2 mutation status were considered ) . We have introduced a new formalism for mutation signature analysis in cancer genomes . Our approach models the correlation between signatures , which provides their performance increase . However , when no correlations exist between signature probabilities , this method will likely not provide much benefit . In these situations , a researcher may opt to use an alternative , such as NMF or LDA . Nevertheless , signature correlations exist in at least breast and ovarian cancer , as shown in this report , and we believe analysis of other cancer types will benefit from our approach . The topic models discussed in this manuscript produce signature probabilities , as opposed to activity estimates , which requires a subtle difference in interpretation . Signature probabilities are related to activities , but they indicate the probability of signatures generating a mutation , rather than the proportion of mutations generated by a signature . The topic models discussed output non-zero signature probabilities for each sample , due to their Bayesian formulation . Since every sample is unlikely to have experienced activity from every detected signature , one may wish to set a probability threshold to determine active signatures for downstream analysis . However , the optimal choice of probability threshold is a matter for future investigation . Correlated topic models are significantly more robust to reduced mutation burden , which can occur in a number of scenarios . We have already described that signature extraction from SVs , at the level detected in the breast and ovarian datasets analysed here , benefits from correlated signature modeling . Analysis of other low-count mutation types may also benefit , for example mutations called from exome or single-cell sequencing experiments . Importantly , the statistical framework of the MMCTM is flexible and extensible . While here we show the advantage of integrated SNV and SV analysis , the MMCTM can seamlessly integrate other count-based features such as copy number events , double strand breaks , and telomeric insertions . As the field develops , we suggest a robust and extensible framework will be required to encode and integrate multiple feature types of the genome as they relate to mutational processes . The advantage of our relatively simple SNV and SV integration is evident and motivates further advances through multi-modal statistical modelling leading to richer biological interpretations of endogenous and potentially exogenous processes . In conclusion , our findings reinforce the importance of an integrated , holistic view of multiple classes of genomic scarring to drive discovery and characterization of mutation processes across human cancers . Nucleotides flanking SNVs were extracted from human reference GRCh37 . The number of each type of SNV ( e . g . C→T ) with a particular flanking sequence was counted . SV calls were split according to type ( deletion , tandem duplication , inversion , foldback-inversion , translocation ) , the level of homology ( 0–1 , 2–5 , >5 bp ) , and breakpoint distance ( <10kbp , 10–100kbp , 100 kbp–1Mbp , 1–10Mbp , >10Mbp ) , then counted . Foldback inversion calls were not included in the breast cancer dataset . Breakpoint distance bins are those used in a previous study on SV signatures [13] . Breakpoint distance was not calculated for translocations , as the concept is not applicable for this class of SVs . SNV and SV counts per sample were computed from the mutations used for signature analysis . Additional ovary sample gene mutation annotations were computed from SNV and indel calls according to the original paper . For LDA and ILDA , parameters were inferred using mean-field Variational Bayes . For CTM , MMCTM , ICTM and IMMCTM , parameter inference was performed using mean-field variational EM . The MMCTM updates and derivations can be found in Salomatin et al . [22] . See S1 Text for detailed descriptions of the topic models . When using only a single mutation type , the MMCTM reduces to the CTM described by Blei and Lafferty [21] ( similarly for the IMMCTM and ICTM ) . Therefore , the CTM and ICTM parameters were inferred using the MMCTM and IMMCTM implementations , but with counts from a single mutation type . The CTM , ICTM , LDA , ILDA , and NMF methods were used to compute SNV or SV signatures separately . The probabilistic topic models were implemented similarly using the Julia language v0 . 6 . 3 [33] . NMF models were fit using the coordinate descent solver implementation in the Scikit-learn library [34] v0 . 19 . 1 . NMF was run on both raw and normalized mutation counts . Normalization was performed by dividing mutation counts by sample totals , for each mutation type . For log-likelihood-based comparisons , mutation counts were split according to a stratified 10 × 5 cross validation scheme; For each histotype , samples were split into 5 training and test sets . The splitting procedure was performed 10 times , resulting in 50 training and test sets . Each method was run on each training set and evaluated on each corresponding test set , using random initialization . Random initialization for the topic models involved generating random positive integer values for the variational signature-mutation dirichlet parameters . Evaluation was performed by randomly splitting the mutations in each test sample into observed and hidden sets . Signature probabilities for each test sample were estimated using the observed test mutation counts , then the per-mutation predictive log likelihood was computed using the hidden test mutation counts . Methods were tested over a range of 2–12 signatures , as well as over a range of count subsets . Multi-modal topic models were given the same number of signatures for SNVs and SVs . An additional , similar comparison was performed by fitting the MMCTM to this data with covariance fixed to the identity matrix . Count subset comparisons were performed by removing mutations from each genome , retaining only a given fraction . Mutations were randomly selected according to their type ( e . g . C ( C→T ) T ) and relative type proportions . These mutations were removed and the genome mutation counts updated . The updated mutation counts were then input to the compared methods . SNVs were subset to 1 , 5 , 10% , while keeping SVs at 100% . SVs were subset to 10 , 15 , 20% , while keeping SNVs at 100% . For the breast cancer dataset , the number of SNV and SV signatures was fixed at 5 , selected by observing the log-likelihood curves in the above benchmarking experiment ( S3 Fig ) with the objective of choosing a “fair” value . For the ovarian cancer dataset , the number of SNV and SV signatures was fixed to 6 and 5 , respectively . The stability of method solutions were also compared over 100 random restarts on 4/5 of the breast cancer dataset . Solutions were evaluated on the remaining 1/5 of the samples in the manner described earlier . Predictive log likelihoods were computed on test sets with signatures for SNVs and SVs separately . The likelihood computation involves the signatures fit with the training data , sample-signature probabilities estimated using the observed test counts , and the hidden test counts . The average per-mutation predictive log likelihood for a particular mutation type is given in Eq 1 . l = ∑ d D ∑ n N d log ( ∑ k K p ( X n d ∣ ϕ k ) p ( Z n d = k ∣ θ d ) ) ∑ d D N d ( 1 ) where D is the number of samples , Nd is the number of mutations in sample d , K is the number of signatures , X is the mutations in sample d , Z is the mutation-signature indicators , ϕk is the signature-mutation distribution , and θd is the sample-signature distribution . For comparisons involving the breast cancer dataset , foldback inversion counts were not provided to NMF as these SV types were not included in this dataset . When evaluating the NMF solutions , the outputs are normalized to produce valid probability distributions that can be used for the log-likelihood calculations . Since NMF does not take into account uncertainty during estimation , the sum of probabilities calculation above can occasionally produce zeros . To avoid taking log ( 0 ) , we add 10−16 to the sum of probabilities for NMF . Topic model signature-mutation and sample-signature distribution point-estimates were obtained by taking the mean of their variational posterior distributions . For the logistic regression classifier-based comparisons , each signature detection method was trained 10 times with 2–10 signatures , using the full 560 breast cancer dataset . For multi-modal methods , the same number of SNV and SV signatures was given . The sample-signature distributions were used as training data for the classifier along with previously published HRDetect-derived labels . HRDetect negative cases were subsampled for each method run to produce balanced datasets for training and evaluation , with 124 positive and negative labels each . Three types of tests were performed: using only SNV , only SV , or both SNV and SV sample-signature distributions . Stratified 5-fold cross-validation was performed for each test , resulting in 5 × 10 = 50 scores for each method , training data type , and setting of the number of signatures . The output score of cross validation is the mean accuracy of the logistic regression classifier . Parameter inference was performed using the Scikit-learn [34] v0 . 19 . 1 implementation with the liblinear solver and maximum 10 , 000 iterations . Simulated datasets were generated by first selecting COSMIC SNV signatures 1 , 2 , 3 , 13 , and breast cancer SV signatures [13] RS 1 , 2 , 3 , and 5 . These SNV signatures were reported as present in the breast cancer dataset [13] , and they are qualitatively distinct from each other . SV signatures largely defined by clustered breakpoints were excluded as that feature was not included in this analysis . Reference signature probabilities were estimated using NMF , the given signatures , and counts for the 560 breast cancer dataset . 10 synthetic datasets were generated , where for each mutation type in each sample , counts were generated by drawing from a Poisson distribution with rate equal to the number of mutations in the sample multiplied by the reference signature matrix and the sample’s signature probability vector . This approach is similar to that used in a previous study [18] . This procedure was repeated using the reference signatures , signature probabilities , and mutation counts subsetted to 1% SNVs and 10% SVs . Signatures and signature probabilities per dataset were then estimated by running each method 500 times with random restarts and choosing the best solution per method based on predictive log-likelihood . Topic model signature hyperparameters were set to 1 . 0 . Estimated signatures were then matched to the reference signatures , and the mean absolute differences between the reference and estimated values were computed . Signature matching was performed by finding the pairwise combination of estimated and reference signatures that gave the lowest mean absolute error . Then the matching procedure was repeated for the rest of the signatures , while ignoring previously assigned reference signatures . The number of signatures to estimate in the breast and ovarian datasets was selected by inspecting the log-likelihood curves from the benchmarking experiment , using the elbow curve method ( S15 Fig ) . The number of signatures to estimate in the two HGSC datasets was selected by fitting the MMCTM to approximately half the mutations in each sample , and computing the average per-mutation log-likelihood on the other half of the mutations . This differs from the benchmarking cross-validation scheme in that it takes in account all samples in the dataset . The model was initially fit to each dataset 1000 times for a limited number of iterations . α hyper-parameters were set to 0 . 1 . Each restart is run until the relative difference in predictive log likelihood on the training data was < 10−4 between iterations . The restart with the best mean rank of the SNV and SV predictive log likelihoods was selected for fitting to convergence with a tolerance of 10−5 . Samples were clustered using sample-signature probabilities for SNV and SV signatures together . Signature probabilities were converted to Z-scores for each signature across samples . By standardizing the probabilities , the inter-sample differences of low-prevalence signatures are given increased emphasis relative to higher-prevalence signatures . Hierarchical agglomerative clustering was performed using the Euclidean metric , and Ward linkage . Discrete clusters were formed using the R dynamicTreeCut package [35] v1 . 63 with method = “hybrid” , deepSplit = FALSE , and minClusterSize = 3 . Enrichment of a sample cluster’s signature probability was tested using an unequal variance one-sided t-test against the signature probabilities of other clusters . For the breast cancer dataset , cluster associations with ER , PR , HER2 , MMRD , and PAM50 status were performed with a two-tailed Fisher’s exact test . Differences in Age or the number of SNVs and SVs were tested with two-tailed unequal variance t-tests . Driver gene mutation and HRDetect prediction associations were computed using a blocked permutation test . The permutation tests were performed as follows: For each cluster , “new” clusters were generated by sampling tumour samples without replacement from the full dataset . New clusters maintained the same ER , PR , and HER2 status composition as the original cluster . The difference in proportions of samples with the annotation of interest between the new cluster and all other samples was computed . Two-tailed p-values were calculated using Eq 2: p = 1 + ∑ n N I ( a b s ( s ′ ) ≥ a b s ( s ) ) 1 + N ( 2 ) where N is the number of permutations ( generated clusters , here 10 , 000 ) , and s is the statistic of interest for the original cluster ( e . g . difference in proportions of samples with loss of TP53 ) , and s′ is the same statistic for a generated cluster . This procedure attempts to correct for correlations between the tested annotations and ER , PR , and HER2 status . Gene mutation status and MSI cluster associations in ovarian cancer were tested with the blocked permutation test described above , accounting for histotype rather than ER , PR , and HER2 status . Differences in SNV and SV counts were performed with two-tailed unequal variance t-tests . Due to the presence of a POLE mutant sample with a very high number of SNVs , t-tests for this statistic were performed on count ranks . The unequal variance t-test on ranked data is a robust alternative to Student’s t-test and the Mann-Whitney U test when assumptions are violated [36] . Cluster-signature and cluster-annotation p-values within each dataset were corrected using the Benjamini & Hochberg method [37] . HGSC samples grouped according to the hierarchical clustering were compared by estimating overall-survival Kaplan-Meier curves for each cluster , using the R survival package . Clusters 2 , 5 , and 7 were grouped as they were all enriched for the FBI/Inv/Del signature , and had no significant difference in survival outcome . We call this the “FBI” group . Similarly , cluster 10 was grouped with cluster 1 as it contained BRCA1 mutant patients with similar signature profiles . P-values were calculated using the log-rank test . Pairwise survival curve comparison p-values were adjusted using the Benjamini & Hochberg method [37] implemented in the R p . adjust function . Topic model code is available in a GitHub repository: https://github . com/shahcompbio/MultiModalMuSig . jl .
Over time DNA accumulates mutations from a variety of sources . Some mutations result from external mutagens , such as UV radiation , while others result from processes occurring within the cell itself . Each of these sources can impart characteristic patterns of mutations on the genome , known as mutation signatures , which can be detected using computational techniques . Loss of DNA repair mechanisms can leave specific mutation signatures in the genomes of cancer cells . To identify cancers with broken DNA-repair processes , accurate methods are needed for detecting mutation signatures and , in particular , their activities or probabilities within individual cancers . In this paper , we introduce a class of statistical modeling methods used for natural language processing , known as “topic models” , that outperform standard methods for signature analysis . We show that topic models that incorporate signature probability correlations across cancers perform best , while jointly analyzing multiple mutation types improves robustness to low mutation counts .
You are an expert at summarizing long articles. Proceed to summarize the following text: Interactions between an organism and its environment can significantly influence phenotypic evolution . A first step toward understanding this process is to characterize phenotypic diversity within and between populations . We explored the phenotypic variation in stress sensitivity and genomic expression in a large panel of Saccharomyces strains collected from diverse environments . We measured the sensitivity of 52 strains to 14 environmental conditions , compared genomic expression in 18 strains , and identified gene copy-number variations in six of these isolates . Our results demonstrate a large degree of phenotypic variation in stress sensitivity and gene expression . Analysis of these datasets reveals relationships between strains from similar niches , suggests common and unique features of yeast habitats , and implicates genes whose variable expression is linked to stress resistance . Using a simple metric to suggest cases of selection , we found that strains collected from oak exudates are phenotypically more similar than expected based on their genetic diversity , while sake and vineyard isolates display more diverse phenotypes than expected under a neutral model . We also show that the laboratory strain S288c is phenotypically distinct from all of the other strains studied here , in terms of stress sensitivity , gene expression , Ty copy number , mitochondrial content , and gene-dosage control . These results highlight the value of understanding the genetic basis of phenotypic variation and raise caution about using laboratory strains for comparative genomics . A major focus of genetic study is to elucidate the effects of genetic variation on phenotypic diversity . The evolution of phenotypes is often driven by environmental factors and the interactions between each organism and its environment . Recently , there has been a renewed interest in characterizing the diversity and ecology of organisms long used in the laboratory as models for biological study . Yeast , worms , flies , and mice have been studied on a molecular level for decades and have provided many insights into basic biology . However , most of our knowledge base exists for only a handful of domesticated lines . Little is known about the natural ecology of these organisms or the degree to which individuals of each species vary within and between natural populations . The budding yeast Saccharomyces cerevisiae exists in diverse niches across the world and can be found in natural habitats associated with fruits , tree soil , and insects , in connection with human societies ( namely through brewing and baking ) , and in facultative infections of immuno-compromised individuals [1] . These yeasts are transported by insect vectors and likely through association with human societies . Recent population-genetic studies have begun to explore the genetic diversity of S . cerevisiae strains [2]–[5] . These studies have demonstrated little geographic structure in natural yeast populations and relatively low sequence diversity , particularly within vineyard strains . It has been proposed that low sequence diversity in this species may be due to a more recent common ancestor compared to other yeasts [6] . Genomic comparisons also suggest low rates of outcrossing between strains [7] , which may limit the fixation of genetic differences under selection by reducing effective population sizes [8] . Although the genetic diversity of S . cerevisiae populations is emerging from large-scale sequencing projects , the phenotypic diversity within and between yeast populations has been less systematically studied . Myriad studies have characterized strain-specific differences in specific phenotypes to identify the genetic basis for phenotypes of interest ( for example , those related to wine making [9] , thermotolerance [10]–[12] , sporulation efficiency [13]–[16] , drug sensitivity [17]–[19] , and others [20]–[25] ) . The degree to which these phenotypes vary across diverse strains has not been systematically explored . Other genomic studies have investigated variation in genomic expression across strains , with the goal of investigating the mode and consequence of gene-expression evolution [26]–[30] . These studies demonstrated significant variation in gene expression between strains , and in some cases pointed to the genetic basis for those differences [27] , [31]–[35] . However , each study investigated only a few strains , typically vineyard strains . The broader phenotypic variation across diverse yeast strains and populations , particularly natural isolates , is largely uncharacterized . Here we investigated the variation in stress sensitivity and genomic expression in a large panel of Saccharomyces strains . We quantified the sensitivity of 52 strains collected from diverse niches to 14 environmental conditions and measured genomic expression in 18 of these strains growing in standard medium . We observe a large amount of phenotypic variation , both in terms of stress sensitivity and gene expression . Associations among phenotypes revealed relationships between environmental conditions and among yeast strains . One case in particular suggests that genetically diverse strains collected from oak soil have undergone selection for growth in a common niche . This study provides a representative description of expression variation and stress sensitivity within and across yeast populations , particularly non-laboratory strains , setting the stage for elucidating the genetic basis of this variation . Fay and Benavides conducted a population-genetic study of 81 Saccharomyces strains by analyzing ∼7 kb of coding and non-coding sequence from each isolate [2] . We characterized the phenotypic diversity of 52 of these strains , shown in Figure 1 . This set included natural isolates from European vineyards , yeasts collected from African palm-wine fermentations , commercial wine- and sake-producing strains , clinical yeasts , natural isolates collected from African and Asian fruit substrates , strains from oak-tree soil and exudates from the Northeastern United States , three common lab strains , and other isolates ( see Table S1 and [2] for references ) . We also characterized two haploid S . cerevisiae strains ( RM11-1a and YJM789 ) and three other Saccharomyces species ( S . paradoxus , S . mikatae , and S . bayanus ) for which whole-genome sequence is available [36] , [37] . Each strain was grown under 31 different conditions representing 14 unique environments , chosen to provoke diverse physiological responses . These environments varied in nutrient composition , growth temperature , and presence of toxic drugs , heavy metals , oxidizing agents , and osmotic/ionic stress . Cells were grown on solid medium in the presence of each environmental variable , and viability was scored relative to a no-stress control for each strain ( see Materials and Methods for details ) . The results reveal a tremendous amount of phenotypic diversity in environmental sensitivity ( Figure 2 ) . Although there were similarities between strains , no two strains were exactly alike in phenotypic profile . Each displayed a propensity for growth under at least one environment and sensitivity to one or more conditions . Some strains were generally tolerant to stressful environments across the board . For example , strain Y2 , originally collected from a Trinidadian rum distillery , and clinical isolates YJM454 and YJM440 were tolerant of most of these conditions , while the S . bayanus strain used in our study was sensitive to nearly all stresses tested . Several strains , including commercial sake-producing strains , showed a wide standard deviation of growth scores across the stresses , reflecting that they were either highly sensitive or highly resistant to different stresses . In contrast , most vineyard isolates grew moderately well in most of the environments examined ( see Discussion ) . Exploration of the range of strain sensitivities measured for each environment also suggested common and unique features of Saccharomyces' habitats . Collectively , this set of strains showed the greatest variation in copper sulfate tolerance , sodium chloride resistance , and freeze-thaw survival , implicating these as niche-specific features not generally experienced by yeast . In contrast , strains showed the least variation ( but some variability nonetheless ) for growth on non-fermentable acetate , in minimal medium lacking supplemental amino acids , and at 37°C . Presumably , defects in respiration , prototrophy , and growth at physiological temperature represent a significant selective disadvantage , regardless of the particular niche . Hierarchical clustering of the phenotype data revealed interesting relationships between groups of strains . In particular , several groups of strains displayed similar profiles of stress sensitivity across the environments tested ( Figure 2 ) . As a group , the sake-producing strains were extremely resistant to lithium chloride but sensitive to copper sulfate , calcium chloride , cadmium chloride , and SDS detergent ( p<0 . 005 based on 10 , 000 permutations , see Materials and Methods ) ; indeed , this group was slightly more sensitive to stress in general . Many of the vineyard strains shared specific phenotypes , including resistance to copper sulfate , as previously noted for other vineyard strains [26] , [38] , [29] . The group of laboratory strains was also highly resistant to copper sulfate as well as sodium and lithium chloride . In contrast , strains collected from oak soil were particularly sensitive to copper sulfate and sodium chloride but highly resistant to freeze-thaw stress ( p<0 . 005 , 10 , 000 permutations ) . The similarities in phenotypic profiles could arise through selection ( either directional or purifying ) due to shared selective pressures across strains living in the same environment . Alternatively , phenotypic similarity could result simply if the strains are genetically related due to a recent common ancestor . For example , many of the lab strains are closely related , since a large fraction of their genomes is derived from a common progenitor [39] , [40] . We wished to distinguish between these possibilities for other strain groups . Natural selection can be inferred by comparing the population genetic structure ( FST ) to an analogous measure of phenotypic structure ( QST ) [41] , [42] . A deviation from unity suggests that either divergent ( QST/FST>1 ) or purifying ( QST/FST<1 ) selection has occurred across populations . We wished to analyze each subpopulation separately , and therefore we devised a simple alternative approach to identify deviations from neutral phenotypic variation . We calculated the average pairwise phenotypic distance over the average pairwise genetic distance for pairs of strains collected from the same environment ( ‘sake’ , ‘vineyard’ , ‘oak’ , ‘clinical’ , ‘natural’ or ‘other fermentation’ ) . This ratio was compared to the ratio of distances calculated for pairs of strains between niche groups , generating the parameter P/G . A P/G ratio = 1 is expected under neutrality , where the phenotypic to genetic distance is equal for within-group versus between-group comparisons . In contrast , a value of P/G<1 suggests that the strains within the group are more similar in phenotype than would be expected under the neutral model , whereas a ratio >1 indicates that the strains are phenotypically more variable than expected based on their genetic relatedness . The results provide evidence of both selection and shared ancestry for different groups of strains . First , the P/G ratio did not deviate significantly from unity for strains in the ‘clinical’ , ‘natural’ , or ‘other fermentation’ groups ( average P/G = 1 . 02+/−0 . 22 ) , nor did it deviate significantly for randomized simulations ( data not shown ) . In contrast , P/G was 4 . 2 and 3 . 0 for sake strains and vineyard strains , respectively . Thus , the similarity in their phenotypes likely arises due to their recent divergence from a common ancestor . Interestingly , these P/G values were significantly higher than expected by chance ( p<0 . 0001 from 10 , 000 permutations ) , suggesting that the strains show more phenotypic variation than expected . This could arise if strains have experienced diversifying selection for disparate phenotypes , although it could also result if genetic distances are underrepresented or skewed due to limited sequence data . In contrast , strains collected from oak-tree exudates and soil are phenotypically more similar than would be expected under a neutral model . We observed a P/G ratio of 0 . 31 ( p = 0 . 0013 from 10 , 000 permutations ) , indicating that phenotypic variation within this group is lower than expected based on the strains' genetic relatedness . This suggests that the strains have undergone selection for growth in a common environment ( see Discussion ) . Consistent with this model , the S . paradoxus strain YPS125 , also collected from Northeastern oak flux [6] , is phenotypically more similar to S . cerevisiae strains collected from that environment ( pairwise R of 0 . 61 , 0 . 66 , and 0 . 77 to YPS1000 , YPS1009 , and YPS163 , respectively ) than to the other S . paradoxus strain in our collection ( R = 0 . 51 ) . At least some of the phenotypes shared by these strains are likely important for their ability to thrive in their niche ( see Discussion ) . Numerous studies have characterized differences in genomic expression between individual strains of yeast , typically vineyard and lab strains [13] , [26]–[31] , [34] , [43] . To more broadly survey the variation in genomic expression across populations , we measured whole-genome expression in 17 non-laboratory strains compared to that in the diploid S288c-derived strain DBY8268 , using 70mer oligonucleotide arrays designed against the S288c genome . The long oligos used to probe each gene minimize hybridization defects due to sequence differences from S288c . We verified this by hybridizing genomic DNA from 6 strains of varying genetic distance from S288c: indeed , fewer than 5% of the observed gene expression differences described below could be explained by defective hybridization to the arrays ( see Materials and Methods ) . Therefore the vast majority of measured expression differences are due to differences in transcript abundance . A striking number of yeast genes showed differential expression from the laboratory strain in at least one other strain ( Figure 3A ) . Of the ∼5 , 700 predicted S . cerevisiae open reading frames , 2680 ( ∼47% ) were statistically significantly altered in expression ( false discovery rate , FDR = 0 . 01 ) in at least one non-laboratory strain compared to S288c , with an average of 480 genes per strain . At an FDR of 0 . 05 , over 70% of genes were significantly altered in expression in at least one non-lab strain ( Table 1 ) . The number of expression differences is comparable to that observed by Brem et al . , who reported over half of yeast genes differentially expressed between the vineyard strain RM11-1a and S288c [27] . However , closer inspection revealed that many of these expression differences were common to all of the non-laboratory strains ( Figure 3A ) , revealing that these expression patterns were unique to S288c . This group was enriched for functionally related genes , including those involved in ergosterol synthesis , mitochondrial function , respiration , cell wall synthesis , transposition , and other functions ( Table 2 ) . Many of these functional groups were also reported by Brem et al . , who noted that multiple categories ( including ergosterol synthesis and mitochondrial function ) can be linked to a known polymorphism in the Hap1p transcription factor [44] . Indeed , the expression differences specific to S288c were enriched for targets of Hap1p ( p<10−11 , hypergeometric distribution ) as well as targets of Hap4p ( p<10−6 ) [45] , which regulates genes involved in respiration . Hence , many of the observed expression differences may result because of S288c-specific physiology ( see Discussion ) . For a more representative description of expression variation in non-laboratory strains , we sought to represent the expression differences in a way that was not obscured by S288c . First , we identified genes whose expression varied significantly from the oak strain YPS163 . Second , we identified transcripts whose abundance varied from the mean of all non-laboratory strains ( see Materials and Methods ) . Although the mean expression value of each gene is merely an arbitrary reference point , this data transformation serves to remove the effect of S288c from each array while maintaining the statistical power to identify expression differences . Roughly 1330 ( 23% ) of yeast genes varied in expression in at least one non-laboratory strain relative to the mean of all strains , while 953 ( 17% ) of genes varied significantly from YPS163 ( FDR = 0 . 01 ) . In both cases , two thirds of significant expression differences were specific to only one strain ( Figure 3B and 3C ) . The number of genes with statistically significant expression differences from the mean ranged from 30 ( in vineyard strain I14 ) to nearly 600 ( in clinical isolate YJM789 ) , with a median of 88 expression differences per strain . The number of expression differences did not correlate strongly with the genetic distances of the strains ( R2 = 0 . 16 ) . However , this is not surprising since many of the observed expression differences are likely linked in trans to the same genetic loci [27] , [31] , [34] , [35] , [43] . Consistent with this interpretation , we found that the genes affected in each strain were enriched for specific functional categories ( Table S4 ) , revealing that altered expression of pathways of genes was a common occurrence in our study . We noticed that some functional categories were repeatedly affected in different strains . To further explore this , we identified individual genes whose expression differed from the mean in at least 3 of the 17 non-laboratory strains . This group of 219 genes was strongly enriched for genes involved in amino acid metabolism ( p<10−14 ) , sulfur metabolism ( p<10−14 ) , and transposition ( p<10−47 ) , revealing that genes involved in these functions had a higher frequency of expression variation . Differential expression of some of these categories was also observed for a different set of vineyard strains [26] , [28] , and the genetic basis for differential expression of amino acid biosynthetic genes in one vineyard strain has recently been linked to a polymorphism in an amino acid sensory protein [35] . We also noted that the 1330 genes with statistically variable expression in at least one non-laboratory strain were enriched for genes that contained upstream TATA elements [46] ( p = 10−16 ) and genes with paralogs ( p = 10−6 ) but under-enriched for essential genes [47] ( p = 10−25 ) . The trends and statistical significance were similar using 953 genes that varied significantly from YPS163 . Thus , genes with specific functional and regulatory features are more likely to vary in expression under the conditions examined here , consistent with reports of other recent studies [30] , [43] , [48] , [49] ( see Discussion ) . Expression from transposable Ty elements was highly variable across strains . However , Ty copy number is known to vary widely in different genetic backgrounds [50] , [51] , suggesting that these and other observed expression differences could be due to copy number variations in particular strains . Indeed , numerous expression differences could be linked to known gene amplifications in S288c , such as ASP3 , ENA1 , CUP1 , and hexose transporters [52] , [51] . We quantified the contribution of increased copy number to the observed increases in gene expression relative to S288c in 6 of our strains . In general , ∼2–5% of expression differences could be wholly or partially explained by differences in gene copy number ( see Materials and Methods ) . YPS1009 was an exception to the trend , since nearly 20% of genes with higher expression could be attributed to increased copy number - most of these genes reside on Chromosome XII . In fact , more than 80% of genes on Chromosome XII met our criteria for increased copy number ( Figure S1A ) , indicating that the entire chromosome is duplicated in this strain . Another example of chromosomal aneuploidy is evident in strain K9 , for which Chromosome IX appears amplified ( Figure S1B ) . Whole-chromosome aneuploidy has been frequently observed in strains growing under severe selective pressure ( for example [53]–[56] . Interestingly , the majority of genes on these duplicated chromosomes do not show elevated transcript abundance in the respective strains . In fact , only ∼25% of genes with increased copy number in each strain showed elevated expression ( defined at FDR = 0 . 01 or as genes whose expression is >1 . 5× over S288c ) . This is in stark contrast to previous studies demonstrating little dosage compensation in S288c in response to gene amplification and chromosomal aneuploidy , leading to the conclusion that yeast does not have a mechanism for dosage compensation . [53] , [54] , [57] . Instead , our results suggest that some form of feedback control acts to normalize the dosage of most genes in non-laboratory yeast strains . The remaining quarter of amplified genes may be inherently exempt from this feedback mechanism . Alternatively , relaxed feedback may occur for specific amplifications if the resulting transcript increase provides a selective advantage to the strain in question . Indeed , 15–40% ( depending on the strain ) of genes lacking feedback control show at least 1 . 5× higher expression beyond what can be accounted for by gene amplification alone , indicating that the expression differences are affected by both gene dosage and regulatory variation . These genes are excellent candidates for future studies of adaptive changes . As observed for gene expression , we found that some genomic amplifications were common across all 6 strains compared to S288c . All strains showed decreased Ty1 copy number , ranging from 2–15× lower than S288c . This is consistent with previous studies that showed higher Ty1 copy number ( including active and partial Ty elements ) in S288c compared to wine strains and natural isolates [50] , [51] , [58] . Most strains also showed even lower Ty1 transcript abundance , beyond what could be explained by copy number variations . Thus , in addition to a higher Ty content , S288c also shows higher expression from Ty genes , perhaps reflecting elevated rates of retrotransposition under the conditions studied here . In contrast , all strains showed higher copy number of the mitochondrial genome compared to S288c , typically elevated 2–3× but nearly 7× higher in clinical strain YJM789 . The most likely explanation is that these strains harbor more mitochondria than S288c , a fact confirmed in vineyard strain RM11-1a by mitochondrial staining [25] . In addition to revealing phenotypic diversity within and between yeast populations , natural variation can also uncover new insights into the effects of each environment on cellular physiology . For example , we noted correlations between environments based on the distribution of strain-sensitivity scores . The most likely explanation is that these stresses have similar effects on cellular function , and thus strains display similar sensitivities to them . Resistance to sodium chloride and lithium chloride or tolerance of ethanol and elevated temperature were highly correlated ( R = 0 . 66 at p<0 . 0001 and R = 0 . 51 at p<0 . 0006 , respectively , based on 10 , 000 permutations ) , consistent with the known effects of these stress pairs on ion concentrations or membrane fluidity/protein structure , respectively . Other relationships were not previously known , including the correlation between sensitivity to SDS detergent and the heavy metal cadmium ( R = 0 . 64 , p<0 . 0001 ) and between ethanol and caffeine tolerance ( R = 0 . 59 , p<0 . 0001 ) . In contrast , resistance to freeze-thaw stress was anticorrelated to sodium chloride resistance ( R = −0 . 35 , p = 0 . 006 ) , suggesting antagonistic outcomes of the same underlying physiology . These relationships point to commonalities in the cellular consequences inflicted by these environments that will be the subject of future investigations of stress-defense mechanisms . We also conducted an associative study to identify gene expression patterns correlated with environmental sensitivity across the 17 non-laboratory strains ( see Materials and Methods for details ) . As basal expression differences could significantly contribute to the inherent ability of cells to survive a sudden dose of stress , the results point to genes whose expression is related to , and perhaps causes , the phenotypes in question . Among the top genes associated with copper sulfate resistance was the metallotheionein CUP1 , important for copper resistance and known to have undergone tandem duplications in copper-resistant strains [59] , [60] . Of the genes whose expression was correlated to sodium chloride tolerance , nearly 20% are known to function in Na+ homeostasis and/or osmolarity maintenance ( including RHR2 , COS3 , SIS2 identified through genetic studies [61]–[63] and JHD2 , SRO7 , YML079W , YOL159C , TPO4 , UTH1 implicated in high-throughput fitness experiments in S288c [64] ) . Thus , these and likely other genes whose expression is highly correlated with each stress-sensitivity profile play a functional role in surviving that condition . Other correlations were not expected . Ethanol and caffeine tolerance were both correlated to the expression of genes encoding transmembrane proteins ( p<0 . 003 , hypergeometric distribution ) , perhaps related to the effect of these drugs on membrane fluidity . Sensitivity to the cell-wall damaging drug Congo Red was significantly correlated to the expression of genes involved in mitochondrial function and translation , respiration , and ATP synthesis ( p<10−13 ) , revealing a link between mitochondria/respiration and the cell wall . Although these connections will require further characterization , they demonstrate the power of using natural diversity to uncover previously unknown relationships between stresses and cellular processes . This study demonstrates the vast amount of phenotypic variation in Saccharomyces strains collected from diverse natural habitats , used in industrial processes , and associated with human illness . Considering the phenotypic responses to the conditions studied here provides insights into the relationships between specific strains and their niches . For example , the wide variance in growth scores of sake-producing strains indicates that they are either highly resistant or sensitive to the different environments studied here , suggesting that they may be specialized for growth in the defined conditions of sake fermentation . In contrast , many of the vineyard isolates survived relatively well in most of the conditions tested . This may reflect their ability to thrive in more variable , natural environments and may also have facilitated their dispersal into new environments in a manner associated with human interactions [5] . Geographic dispersal might also explain the higher-than-expected phenotypic diversity of vineyard strains , which might be driven by diversifying selection ( suggested by our analysis ) due to unique pressures imposed after expansion into new environments . Although many of the phenotypic differences we observed are probably neutral , providing no benefit or disadvantage to the strains in question , some are likely to provide a selective advantage . Copper-sulfate resistance in European vineyard strains may have arisen through positive selection , since copper has long been used as an antimicrobial agent in vineyards and orchards [1] , [65] . Another example may apply to the oak strains studied here . Our simple metric comparing phenotypic to genetic diversity in strains collected from similar environments suggests that oak strains are phenotypically more similar than expected based on their genetic relationship . Formally , this could arise if multiple traits are evolving neutrally ( but slower than the genetic drift represented by the sequences used here ) since the strains diverged from a distant , common progenitor . However , the fact that S . paradoxus oak isolate YPS125 is phenotypically more similar to S . cerevisiae oak strains than the other S . paradoxus isolate in our analysis instead supports that these strains have undergone selection for growth in a common environment . One intriguing phenotype is freeze-thaw resistance , which may be important to survive the wintry niche from where these strains were collected . Consistent with this hypothesis , we have recently isolated numerous Sacharomycete strains ( including S . cerevisiae ) from Wisconsin oak exudates , of which 86% ( 19/22 ) are freeze-thaw tolerant ( DJK and APG , unpublished data ) . Ongoing studies in our lab are dissecting the genetic basis for this phenotypic difference . In addition to stress sensitivity , gene expression also varies significantly across yeast populations . More than a quarter of yeast genes varied in expression in at least one non-laboratory strain under the conditions studied here . Consistent with other recent reports [30] , [48] , [49] , [66] , we find that genes with specific structural or functional characteristics ( including nonessential genes and those with upstream TATA elements and paralogs ) show higher levels of expression variation across strains . This has previously been interpreted as a higher rate of regulatory divergence for genes with these features , either in response to selection [48] or mutation accumulation [49] . However , these features are also common to genes whose expression is highly variable within the S288c lab strain grown under different conditions ( [67] and data not shown ) , particularly those induced by stressful conditions [46] , [68] . It is also notable that genes with TATA elements show higher ‘noise’ in gene expression within cultures of the same strain [69] , [70] . Thus , an alternative , but not necessarily mutually exclusive , hypothesis is that the expression of these genes is more responsive to environmental or genetic perturbations , again consistent with previous studies [66] , [30] , [48] , [49] . We have conducted our experiments under ‘common garden’ lab conditions in attempt to minimize environmental contributions to expression phenotypes . However , because each strain may have evolved for growth in a unique environment , each may in fact respond differently to the same growth conditions used here . Indeed , this may explain the prevalence of metabolic genes in our set of genes showing variable expression in multiple strains , since many of these strains have not evolved for growth in highly artificial laboratory media . Emerging from our analysis is the fact that S288c is phenotypically distinct from the other non-laboratory strains studied here . This strain displays extreme resistance to specific stresses , harbors fewer mitochondria , contains more transposable elements , and shows unique expression of many genes compared to all other strains investigated ( a direct comparison of the number of differentially expressed genes in S288c is difficult due to the different statistical power in calling these genes ) . We have also found that this strain has an aberrant response to ethanol , since it is unable to acquire alcohol tolerance after a mild ethanol pretreatment , unlike natural strains [71] . It is likely that additional responses found in natural strains have been lost or altered in this domesticated line . The progenitor of S288c was originally isolated from a fallen fig in Merced , California , and sequence analysis indicates that S288c is genetically similar to other natural isolates [1]–[3] . A recent study by Ronald et al . counters the proposal that S288c has undergone accelerated divergence during its time in the laboratory [72] . Instead , our results suggest that the strain has evolved unique characteristics through inadvertent selection for specific traits ( such as growth on artificial media ) and population bottlenecks . Thus , the laboratory strain of yeast may not present an accurate depiction of natural yeast physiology . Indeed , no single strain can be used to accurately represent the species , a note especially important for comparing phenotypes across species . Complete exploration of an organism's biology necessitates the study of multiple genetic backgrounds to survey physiology across populations . Despite its limitations , the lab strain offers nearly a century of detailed characterization , along with powerful genetic and genomic tools . A useful approach is to complement studies on laboratory strains with investigations of natural variation . By characterizing stress sensitivity in a large set of strains , we have leveraged the power of natural diversity to uncover new relationships between stresses and to reveal previously unknown connections between genes , stresses , and cellular processes . These connections lead to hypotheses about stress defense mechanisms that can often be dissected using the valuable tools provided by the lab strain . Application of genomic techniques to characterize natural yeast strains will foster such studies while revealing additional insights into genetic and phenotypic variation in Saccharomyces . Strains used in this study and references are found in Table S1 . In addition to sequence data from [2] , an additional 5 , 305 bp of noncoding DNA was sequenced for 41 S . cerevisiae strains over 8 intergenic sequences ( GENBANK accession numbers EU845779 - EU846095 ) for a total of 13 , 016 bp over 13 loci . Phylogenetic analysis shown in Figure 1 was performed on the combined sequence set using the program MrBayes [73] . Evolutionary distances were estimated using the Jukes-Cantor ( JC ) model based on 2 , 056 bp noncoding sequence data present in all strains; results and significance were very similar when the distance was based on 9 , 334 bp of noncoding sequence excluding only pairwise-deletion data [74] . Strains with evolutionary distances equal to zero over this subsequence ( but clearly non-zero when all sequence was assayed ) were set to 0 . 00001 to facilitate permutation calculations . Paralogs were defined as genes with a BLAST E-value score <10−100 . Yeast strains were grown in YPD medium at 30°C to an optical density of ∼0 . 3 in 96-well plates . Three 10-fold serial dilutions were spotted onto YPD agar plates containing the appropriate stress , as well as a YPD plate for a no-stress control . Cells were also plated onto minimal medium [75] or YP-acetate . In the case of freeze-thaw stress , 200 µl cells was frozen in a dry ice/ethanol bath for two hours or left on ice as a control before spotting onto YPD plates . Cells were grown for 2–3 days at 30°C unless otherwise noted , and viability of each dilution was scored relative to the no-stress control for each strain . All experiments were done in at least duplicate over 2–3 doses of most stresses ( see Table S2 for raw data and stress doses ) . Final resistance scores were summed over the 3 serial dilutions then averaged over replicates and stress doses , providing a single score ranging from 0 ( no growth ) to 6 ( complete growth ) for each strain and each stress condition . For Figure 2 , strains were clustered based on phenotypes using the Pearson correlation and UPGMA clustering [76] . Correlations between stresses were calculated based on the Pearson correlation between strains , excluding 14 strains of highly similar genetic distance ( JC<0 . 0008 ) . Phenotypes specific to groups of strains collected from similar environments ( see Table S1 for groupings ) were calculated based on the median growth score of strains in that group . Significance was estimated by 10 , 000 permutations of strain-group labels , scoring the frequency of observing a median growth score equal to or greater than that observed . A parameter , P/G , was calculated to compare the similarity in phenotype to the similarity in genotype for strains within and between niche groups . The average pairwise phenotypic distance , taken as the Pearson distance ( 1 – Pearson correlation ) between phenotype vectors , was divided by the average pairwise JC distance for strains within a niche group . This value was divided by the same ratio calculated for all pairs of strains between niche groups ( see Table S1 for niche groupings ) . Significance was estimated based on 10 , 000 random permutations of strain-group labels . The distribution of P/G ratios from randomized trials was centered on 0 . 99; furthermore P/G was ∼1 . 0 for strains in the ‘clinical’ , ‘natural’ , and ‘other fermentation’ groups , reflecting either neutral drift for these groups or that these strains were inappropriately grouped together into somewhat amorphous categories . Seventeen strains ( including B1 , I14 , M22 , M8 , PR , RM11-1a , K1 , K9 , YJM308 , YJM789 , YJM269 , Y12 , SB , Y1 , Y10 , YPS1009 , and YPS163 ) were chosen for whole-genome expression analysis . Cells were grown 2–3 doublings in YPD medium to early log-phase in at least biological triplicate . Cell collection , RNA isolation , and microarray labeling and scanning were done as previously described [77] , using cyanine dyes ( Flownamics , Madison , WI ) and spotted DNA microarrays consisting of 70mer oligos representing each yeast ORF ( Qiagen ) . For all arrays , RNA collected from the denoted strain was compared directly to that collected from the diploid S288c lab strain DBY8268 , with inverse dye labeling used in replicates to control for dye-specific effects . At least three biological replicates were performed for all comparisons . Data were filtered ( retaining unflagged spots with R2>0 . 1 ) and normalized by regional mean-centering [78] . Genes with significant expression differences ( compared to the S288c control , strain YPS163 , or the mean expression across all strains ) were identified separately for each strain with a paired t-test ( or unpaired t-test in reference to YPS163 ) using the BioConductor package Limma v . 2 . 9 . 8 [79] and FDR correction [80] , taking p<0 . 01 as significant unless otherwise noted ( see Table S3 for limma output and Figure S2 for a comparison of the statistical power for each strain ) . All microarray data are available through the NIH Gene Expression Omnibus ( GEO ) database under accession number GSE10269 . Array-based comparative genomic hybridization ( aCGH ) was performed in duplicate on six strains ( K9 , M22 , RM11-1a , Y10 , YJM789 , and YPS1009 ) relative to the DBY8268 control as previously described [81] , using amino-allyl dUTP ( Ambion ) , Klenow exo-polymerase ( New England Biolabs ) , and random hexamers . Post-synthesis coupling to cyanine dyes ( Flownamics ) was performed using inverse dye labeling in replicate experiments . Technical variation in hybridization was defined as the mean+2 standard deviations ( a log2 value of 0 . 3 ) of all spot ratios , based on triplicate comparisons of DBY8268 to DBY8268 genomic DNA . For non-lab strains compared to DBY8268 , genes with negative aCGH ratios outside the range of technical variation on both duplicates were defined as those affected by copy number and/or hybridization defects . Transcript levels within 0 . 45 ( 3 standard deviations of technical variation ) of the aCGH ratio were identified as those largely explained by copy number and/or hybridization defects – on average , fewer than 5% of genes with statistically significant ( FDR = 0 . 01 ) differential expression compared to DBY8268 fell into this class . Genes with a positive aCGH ratio >0 . 7 in log2 space were defined as genes with increased copy number in each non-lab strain . All microarray data are available through the NIH Gene Expression Omnibus ( GEO ) database under accession number GSE10269 . A vector of relative phenotype scores was generated by dividing scores from Figure 2 by the score measured for DBY8268 . The Pearson correlation between this vector and the measured expression vector for each strain relative to DBY8268 was calculated for all genes in the dataset . Genes whose expression was correlated above or below what was expected by chance ( p<0 . 01 ) were defined based on 100 permutations of each of the ∼6 , 000 expression vectors .
Much attention has been given to the ways in which organisms evolve new phenotypes and the influence of the environment on this process . A major focus of study is defining the genetic basis for phenotypes important for organismal fitness . As a first step toward this goal , we surveyed phenotypic variation in diverse yeast strains collected from different environments by characterizing variations in stress resistance and genomic expression . We uncovered many phenotypic differences across yeast strains , both in stress tolerance and gene expression . The similarities and differences of the strains analyzed uncovered phenotypes shared by strains that live in similar environments , suggesting common features of yeast niches as well as mechanisms that different strains use to thrive in those conditions . We provide evidence that some characteristics of strains isolated from oak tree soil have been selected for , perhaps because of the shared selective pressures imposed by their environment . One theme emerging from our studies is that the laboratory strain of yeast , long used as a model for yeast physiology and basic biology , is aberrant compared to all other strains . This result raises caution about making general conclusions about yeast biology based on a single strain with a specific genetic makeup .
You are an expert at summarizing long articles. Proceed to summarize the following text: The NF-κB-like velvet domain protein VosA ( viability of spores ) binds to more than 1 , 500 promoter sequences in the filamentous fungus Aspergillus nidulans . VosA inhibits premature induction of the developmental activator gene brlA , which promotes asexual spore formation in response to environmental cues as light . VosA represses a novel genetic network controlled by the sclB gene . SclB function is antagonistic to VosA , because it induces the expression of early activator genes of asexual differentiation as flbC and flbD as well as brlA . The SclB controlled network promotes asexual development and spore viability , but is independent of the fungal light control . SclB interactions with the RcoA transcriptional repressor subunit suggest additional inhibitory functions on transcription . SclB links asexual spore formation to the synthesis of secondary metabolites including emericellamides , austinol as well as dehydroaustinol and activates the oxidative stress response of the fungus . The fungal VosA-SclB regulatory system of transcription includes a VosA control of the sclB promoter , common and opposite VosA and SclB control functions of fungal development and several additional regulatory genes . The relationship between VosA and SclB illustrates the presence of a convoluted surveillance apparatus of transcriptional control , which is required for accurate fungal development and the linkage to the appropriate secondary metabolism . Velvet domain transcription factors interconnect fungal developmental programs and secondary metabolism and affect a significant part of differential gene expression during development in comparison to vegetative growth [1] . The majority of the fungal target genes of velvet domain proteins , which bind to promoters of thousands of genes by their Rel homology-like domain , is yet elusive [2 , 3] . This fungal protein family is highly conserved in ascomycetes and basidiomycetes [4 , 5] . The velvet proteins VosA ( viability of spores A ) and VelB ( velvet-like B ) can form homodimers as well as the VosA-VelB heterodimer to repress or activate gene expression [2 , 6–9] . VosA represses brlA ( bristle A ) expression encoding a master regulator for the initiation of conidia formation , which are the asexual spores of the fungus . VosA-VelB later activates within conidia the gene encoding the transcription factor VadA ( VosA/VelB-activated developmental gene ) , which downregulates brlA expression to allow the maturation of viable conidia [7] . Full suppression of conidiation during vegetative growth of the hyphae require direct binding of VosA and a second brlA-repressor , NsdD ( never in sexual development D ) to the brlA promoter [2 , 8 , 9] . Growth of fungal filaments after the germination of spores is in the first hours not responsive to external signals , because developmental regulatory genes are not expressed . De-repression of brlA accompanies the achievement of developmental competence of fungal hyphae approximately 18 to 20 h post germination [8 , 10] . This derepression is characterized by delocalization of VosA and NsdD from the brlA promoter , which allows the Flb proteins ( fluffy low brlA ) FlbB , FlbC , FlbD and FlbE to activate brlA expression [8 , 9 , 11–15] . A second layer of conidiation repression during vegetative growth is carried out by SfgA ( suppressor of fluG ) , which negatively regulates expression of the flb genes . FluG ( fluffy G ) accumulates to a certain threshold during ongoing vegetative growth , which removes the repressive effects of SfgA upon conidiation [16 , 17] . The Flb proteins activate brlA in two distinct cascades: FlbB/FlbE→FlbD→brlA and FlbC→brlA [11–15 , 18 , 19] . The fifth Flb protein , FlbA , regulates development in an indirect manner by antagonizing a G-protein mediated repression of conidiation , and thereby represses vegetative growth [20–22] . The C2H2 transcription factor BrlA activates abaA ( abacus A ) in the mid phase of conidiation [23] . AbaA activates wetA ( wet-white A ) in the late phase of conidiation , which is necessary for the synthesis of conidiospore wall components [4 , 24 , 25] . VosA is involved in time tuning of conidiation: it represses brlA until developmental competence is achieved and is activated by AbaA and WetA downstream of BrlA during late asexual growth [4 , 26] . VosA regulates conidiospore viability during ongoing spore formation in Aspergilli through activation of genes which products are important for spore maturation [4 , 6 , 27–29] . VosA and VelB are important for trehalose biogenesis [4 , 27] . Trehalose is a storage compound , which supports conidiospore viability and germination [30–32] . Velvet domain proteins couple fungal differentiation programs to specific secondary metabolisms for sexual or asexual development and a fifth of the genome is differentially expressed during development in comparison to vegetative growth [1 , 33] . Velvet domain proteins are located at the interface between development and secondary metabolism control [33–36] . A . nidulans is able to produce several secondary metabolites ( SMs ) , such as penicillins , sterigmatocystin , benzaldehydes , emericellamides , orcinol and diorcinol , diindoles , austinol and dehydroaustinol [37–43] . SM genes are often clustered in fungal genomes . Those gene clusters are controlled by cluster-specific transcription factors and master regulators , which interconnect SM biosynthesis and developmental programs in response to environmental cues , such as light [33 , 41 , 44 , 45] . A key element of this interconnection is the velvet complex , comprising the velvet proteins VeA and VelB and the methyltransferase LaeA [27 , 33 , 46–50] . Velvet proteins regulate secondary metabolite gene clusters , as well as downstream master regulators , such as the well conserved MtfA ( Master transcription factor A ) [43 , 51 , 52] . Their regulatory versatility suggests a complex hierarchy of multiple control layers of genetic networks mutually controlled by distinct transcription factors . The zinc cluster ( C6 ) protein SclB acts as activator of a genetic network , which was characterized by genome-wide transcriptional analyses and which represents a novel downstream-target for inhibition of the velvet domain protein VosA in the fungal model organism A . nidulans . SclB interconnects the formation of asexual spores and the enzymatic as well as non-enzymatic responses upon oxidative stress to a distinct secondary metabolism . A ΔsclB strain was generated to analyze the differences in gene expression in the absence of sclB compared to A . nidulans wildtype . The complete sclB ORF in this ΔsclB strain was exchanged with a recyclable marker cassette leaving only a small six site as scar ( 100 nucleotides ) after recycling [72] . RNA of wildtype , ΔsclB and a sclB complemented ( sclB comp ) strain were extracted from submerged cultures grown for 24 h under constant agitation and sequenced to compare genome-wide transcriptional changes in the presence or absence of sclB . The reintroduction of the sclB ORF fully complemented all effects on transcription in the ΔsclB strain resulting in transcriptomes comparable to wildtype . 169 genes were significantly increased and 239 were significantly decreased in ΔsclB compared to wildtype with a threshold of at least two fold for upregulation or downregulation ( Log2 fold change ( FC ) of at least 1 ) ( S1 Table ) . Analyses employing the Aspergillus Genome Database ( AspGD ) [64] and the Fungal and Oomycete Genomics Resources Database ( FungiDB ) [73] were conducted to categorize these genes into functional groups ( Fig 2 ) . 13 genes were assigned to carbon metabolism , one to sulfur metabolism and 9 to other metabolic functions of the genes upregulated in ΔsclB compared to wildtype . Genes connected to secondary metabolism constitute the largest group ( 18 ) with an assigned function . Several genes related to the respiratory chain ( 6 ) or transmembrane transport ( 11 ) were also upregulated in ΔsclB compared to wildtype . Four genes were assigned to the response to oxidative stress and one is assigned to menadione induced stress . One gene of the group of upregulated genes in ΔsclB compared to wildtype is linked to development . The largest group among the genes downregulated in ΔsclB compared to wildtype with an assigned function is related to secondary metabolism ( 18 ) . Other large groups are constituted of genes connected to development ( 17 ) or transmembrane transport ( 15 ) . Several genes related to carbon metabolism ( 9 ) , sulfur metabolism ( 2 ) or amino acid biosynthesis ( 6 ) were found as well amongst the downregulated genes in ΔsclB compared to wildtype , as well as genes related to the response to oxidative stress ( 9 ) or to other stresses ( 6 ) . Members of eight different SM gene clusters were amongst the genes upregulated and 10 amongst the genes downregulated in ΔsclB compared to wildtype ( Table 1 and S1 Table ) . This equals approximately 25% of all predicted secondary metabolite gene clusters in A . nidulans ( Table 1 and S1 Table ) [74] . Genes encoding backbone enzymes of four of these clusters were upregulated ( AN3396 , AN3252 , AN6784 and AN1242 ) and six were downregulated ( AN6236 , AN9244 , AN8383 AN2064 , AN9226 and AN2924 ) . This equals approximately 14% of all backbone enzymes of secondary metabolite gene clusters in A . nidulans [74] . Taken together , a significant part of the transcriptome is differentially expressed when the ΔsclB strain was compared to wildtype , with even 1 . 5 times more genes with decreased than with increased transcription . Most differentially regulated genes , for which a function could be assigned , are related to secondary metabolism and genes related to development . Another large part of genes differently regulated in the absence of sclB compared to the wildtype situation are genes related to stress response , especially of the response towards oxidative stresses . The A . niger scl-2 mutant forms reduced numbers of conidiophores and structures similar to sclerotia [53] , whereas a deletion of the sclB orthologous gene in A . fumigatus ( Afu6g11110 ) did not result in any obvious phenotype when grown on minimal medium ( S2 Fig ) . Transcriptomic analyses of the ΔsclB strain compared to wildtype in A . nidulans suggested that SclB is involved in asexual development ( Fig 2 and S1 Table ) . The growth and differentiation of the ΔsclB mutant strain was examined during light and unlimited oxygen supply promoting asexual spore formation in comparison to cultivation in dark with limited oxygen supply supporting sexual development ( Fig 3A ) . A . nidulans wildtype forms high numbers of conidiophores carrying asexual spores in light and produces lower numbers of asexual spores in dark after a delay of several days [1] . The absence of sclB leads to a significantly decreased formation of conidiophores during asexual or sexual development , compared to wildtype ( Fig 3A and 3B ) . This phenotype of the A . nidulans ΔsclB strain was fully restored by re-introducing either the sclB ORF into ΔsclB ( sclB comp ) or the sclB ortholog from A . fumigatus ( Afu6g11110 ) sharing 55% similarity , indicating functional conservation ( S2 Fig ) . Quantification of conidiospore formation in light revealed that the ΔsclB strain produced less than 5% of the asexual spores produced by the wildtype after two days and reached a maximum of approximately 20% of the wildtype conidia after 10 days . A . nidulans reduces conidiophore formation during growth in the dark and favors cleistothecia formation . The ΔsclB strain produced significantly less conidiospores during growth in the dark in comparison to light suggesting that light control of development is independent of SclB . Overexpression of sclB ( sclB OE ) under control of a nitrate-inducible promoter ( PniaD ) further increases asexual spore formation in the dark , when the wildtype produced only low amounts of conidia ( Fig 3A ) . Sexual development includes nest formation and the differentiation of cleistothecia as closed fruiting bodies , which is increased in the dark and reduced in light . Cleistothecia formation is similar in the ΔsclB strain in comparison to wildtype and additional control strains suggesting that SclB control is rather targeting asexual than sexual development ( Fig 3C ) . The sclB OE strain increased the production of conidiophores significantly when grown under inhibiting and delaying conditions in the dark under limited oxygen supply , when the wildtype only produced small amounts of conidiophores and the formation of cleistothecia is favored ( Fig 3 ) . This effect in the sclB OE strain is even more pronounced when instead of point inoculated colonies leading to radial zones of different ages [75]; ( Fig 3A upper part ) , plated colonies emerging from separated germinating spores were monitored . Plated colonies form a coherent mycelium due to hyphal fusion through anastomosis tubes , and are of same age at every spot ( Fig 3A lower part , Fig 3B and 3C ) [76 , 77] . These data indicate that SclB is required for significant , efficient and accelerated conidiophore formation of A . nidulans . ChIP-on-Chip experiments showed that VosA binds the sclB promoter in vivo approximately 311 bp upstream of the sclB ORF [2] . Promoter walking electrophoretic mobility shift assays ( EMSAs ) revealed that VosA binds a 40 bp region of the sclB promoter ( marked in Fig 1 ) . EMSAs of this region and purified VosA protein verified dosage-dependent VosA binding in vitro ( Fig 4A ) . In the EMSA protein-DNA complexes run high in the gels and free DNA runs in the lower part . Possible formation of GST-VosA dimers might lead to binding of more than one DNA molecule at the same time . Two putative binding sequences were identified in this region and mutations for both of them , in which the respective putative binding sequence was deleted , showed that VosA specifically binds nine bps , spanning -337 to -329 in front of the sclB ORF ( Fig 4A ) . A vosA deletion mutant ( ΔvosA ) was constructed to analyze the impact of VosA upon sclB gene expression . Transcription levels of sclB were monitored in wildtype and ΔvosA strain with quantitative real-time PCR ( qRT-PCR ) . sclB transcription is upregulated in the absence of vosA in asexually grown colonies 24 h post induction of development ( Fig 4B ) . This indicates a repressing effect of VosA towards sclB expression during asexual development . This is in accordance with transcriptomic data showing an upregulation of sclB gene expression in conidiospores of a ΔvosA strain in comparison to wildtype published by Park and co-workers [78] . AbaA and WetA activate vosA during late asexual development . VosA together with VelB is necessary for trehalose biogenesis to support spore viability [4 , 6] . Spore viability was compared in ΔsclB and sclB OE strains on solid minimal medium . Conidiospores of the ΔsclB strain showed a rapid loss in spore viability compared to spores of wildtype , sclB comp and sclB OE strains after seven days and thereafter ( Fig 4C ) . A similar loss in spore viability was found for the ΔvosA strain , whereas conidiospores of the ΔvosAΔsclB double mutant strain showed further diminished viability after seven days and thereafter . The ΔvosA single mutant produces grey-greenish conidiospores with decreased viability [4] ( Fig 4D ) . The ΔvosAΔsclB double deletion strain supports an epistatic interaction of sclB towards vosA , because it showed the ΔsclB single mutant phenotype of reduced conidia formation with low spore viability ( Fig 4C and 4D ) . These findings place the gene encoding SclB genetically downstream of the gene for VosA . VosA binds upstream of sclB and represses sclB gene expression . VosA acts as homodimer or forms with VelB or VelC the heterodimers VosA-VelB or VosA-VelC [6 , 79] , which fulfill different functions in fungal development and interconnected secondary metabolism . Double deletions of sclB and velB or velC , respectively , were created to discriminate between SclB functions downstream of the VosA-VosA homodimer or the VosA-VelB and VosA-VelC heterodimers . veA was included into these analyses , because VeA competes with VosA for VelB and forms the VeA-VelB heterodimer . The ΔveA and ΔvelB single mutants are unable to form cleistothecia on minimal medium and are misregulated in secondary metabolism producing dark reddish pigments [6 , 33 , 52] ( Fig 4D ) . The ΔsclBΔveA and ΔsclBΔvelB double mutants both show additive phenotypes with impaired asexual and sexual development . The loss of cleistothecia formation of the ΔveA and ΔvelB single mutant is combined with increased amounts of aerial hyphae without conidia and significantly smaller greenish colony centers representing conidiophores . This indicates a SclB function for conidiophores independently of the VeA or VelB governed pathways for fruiting bodies and the corresponding secondary metabolism . The ΔvelC single mutant shows an almost wildtype-like phenotype on minimal medium combined with increased amounts of conidiophores [79] . The ΔsclBΔvelC double deletion strain shows an intermediate phenotype with a colony similar to the ΔsclB phenotype combined with an increased greenish colony center for conidiophores . Therefore , SclB functions independently of the velvet protein heterodimers VosA-VelB or VosA-VelC and is primarily a repression target of the VosA homodimer . SclB functions downstream of VosA and its absence leads to decreased conidiophore formation , whereas the sclB OE strain produces increased numbers of conidiophores during sexual development . This indicates that SclB is an activator of conidiophore formation . Strains were grown in liquid minimal medium to test whether an overexpression of sclB is sufficient to induce development under vegetative conditions . Growth in submerged cultures suppresses development in A . nidulans and results in solely vegetative growth of the wildtype ( Fig 5A ) . No conidiophores were found in wildtype , ΔsclB or sclB comp strains grown in submerged cultures . In contrast , the sclB OE strain forms conidiophores after 18 h of growth in submerged cultures ( Fig 5A ) . VosA represses gene expression of the master regulator-encoding brlA , and a ΔvosA strain forms conidiophores when grown in submerged culture conditions [4] . The expression of brlA was examined in the sclB OE mutant during vegetative growth . Strains were grown under submerged conditions what hinders asexual development in the wildtype . The wildtype only expresses basal levels of brlA under these conditions . In contrast , mRNA levels of brlA are highly upregulated in the presence of high amounts of SclB in the sclB OE strain ( Fig 5B ) . VosA represses brlA during vegetative growth and brlA gene expression was upregulated in the ΔvosA strain grown under submerged culture conditions as well ( Fig 5B ) [4 , 8] . Expression of brlA in a ΔvosA mutant in the sclB OE background was tested to examine , whether SclB is able to activate brlA gene expression . Whereas brlA expression was already upregulated about 40 times in sclB OE compared to wildtype , the ΔvosA sclB OE mutant showed even more than 400 times upregulation compared to wildtype ( Fig 5B ) . This additional upregulation indicates that SclB is able to activate brlA expression in the absence of vosA . Activation of the conidiation pathway is inhibited by the repressors VosA and NsdD during vegetative growth , which are released from the brlA promoter when the fungus becomes developmentally competent [4 , 8 , 9] . SfgA represses conidiation indirectly by regulating the genes for the Flb factors [16 , 80] . Expression levels of sfgA , nsdD and vosA were analyzed by qRT-PCR in sclB mutant strains to exclude the possibility that SclB influences the conidiation pathway by downregulating gene expression of these repressors ( S3A Fig ) . Gene expression of none of these repressor genes is altered in ΔsclB or sclB OE strains in comparison to wildtype . This demonstrates that SclB does not control the conidiation pathway through repression of its repressor genes . Taken together , the presented data indicate that SclB is an activator of the conidiation pathway through the brlA activator gene . The ΔbrlA bristle mutant phenotype of primarily stalks with diminished conidia ( Fig 5C ) is distinctly different from the ΔsclB phenotype . The ΔsclBΔbrlA double mutant resembles the ΔsclB single mutant , supporting an epistasis of sclB towards brlA ( Fig 5C ) . This underlines a function of SclB upstream of brlA in developmental programs . In addition , epistasis of sclB and abaA , a downstream factor of brlA [81] , was analyzed . ΔabaA forms brownish conidiophores with intermittent tumefactions , which are distinctly decreased in number [82] ( S3B Fig ) . The ΔsclBΔabaA mutant shows the ΔsclB single mutant phenotype but has lost the greenish colony center ( S3B Fig ) . This shows that sclB is epistatic to abaA and corroborates the finding that SclB activates the conidiation cascade upstream of its major regulator BrlA . An increased brlA expression directly leads to spore formation from vesicle-like structures [83] , whereas sclB OE activating brlA expression forms conidiophores under submerged culture conditions . Upstream activators of brlA were analyzed to examine whether SclB activates further regulatory genes of asexual development upstream of brlA . FluG is a key upstream activator of the conidiation pathway and acts as a time-dependent repressor of the conidiation-repressor SfgA [8 , 16 , 17] . The deletion of fluG leads to drastically reduced conidiation and a fluffy whitish phenotype with low amounts of conidiophores and high amounts of aerial hyphae [17] ( Fig 6A ) . The back of the colony shows a light orange color indicating an alteration in secondary metabolite production . sclB was knocked out in the ΔfluG strain to analyze epistatic interactions . The ΔfluGΔsclB double mutant strain shows an additive phenotype with large amounts of aerial hyphae , but completely failed to produce conidiophores ( Fig 6A ) . In addition , the orange color was less bright . The ΔfluG phenotype was not rescued by an overexpression of sclB ( Fig 6A ) . This indicates a function of the SclB protein downstream of FluG or the FluG-SfgA pathway . The sclB gene is presumably not a direct downstream target of FluG-mediated gene activation , as sclB OE could not rescue the loss of fluG . Transcription of fluG was increased in qRT-PCR analyses from vegetatively grown ΔsclB strain ( Fig 6B ) . This corroborates that SclB does not function as activator of fluG gene expression . SclB might have repressing effects upon fluG expression during late asexual development ( spore maturation ) , because fluG expression is upregulated in the absence of sclB during asexual growth after 24 h in comparison to wildtype ( Fig 6B ) . The sclB gene expression is decreased in the absence of fluG as well , suggesting regulatory feedback loops or cross talk between both factors and their corresponding genes ( Fig 6C ) . The Flb factors , which act downstream of FluG , activate brlA in two cascades: FlbB/FlbE→FlbD→BrlA and FlbC→BrlA [11–15 , 18 , 80] ( Fig 7 ) . Genome-wide transcriptional ana-lysis showed that flbC and flbD transcript levels are significantly lower in ΔsclB compared to wildtype during late vegetative growth when the fungus reached the state of developmental competence ( S1 Table ) . Transcription of flbB–E was analyzed in more detail through qRT-PCR measurements . flbD gene expression is distinctly lower in submerged cultures in the absence of sclB compared to wildtype ( Fig 7 ) . Moreover , flbC is downregulated in ΔsclB after 24 h of vegetative growth in submerged cultures , but upregulated in the sclB OE strain , compared to wildtype . This is in agreement with the data obtained in genome-wide transcriptomics ( S1 Table ) . Transcription of flbB and flbE is not significantly differentially regulated in the sclB mutants compared to wildtype in qRT-PCR analyses . Nevertheless , expression profiles of both , flbB and flbE in sclB mutants resemble these of flbC and flbD in their tendencies , indicating regulatory effects of SclB upon these factors as well . These analyses suggest an activating role of SclB towards the Flb cascade upstream of brlA and specifically towards flbC and flbD during late vegetative growth at the onset of conidiation . Transcription of flbB , flbC and flbD is upregulated in the absence of sclB compared to wildtype after 24 h of asexual growth . Similarly , the flbA gene for an RGS ( Regulator of G protein Signaling ) domain protein indirectly supporting conidiation [84] , is upregulated during asexual growth in the absence of sclB but not during vegetative growth . These findings indicate that SclB regulation of the conidiation cascade is part of a timely adjusted choreography of asexual development . Single and double knock out strains of the flb genes were created to further investigate the genetic relationship between sclB and the flb genes . All flb single deletions showed fluffy phenotypes [85] that are distinctly different to the ΔsclB phenotype ( Fig 7C ) . Only ΔflbC is an exception with a phenotype similar to ΔsclB , which is in agreement with the finding that SclB activates flbC gene expression . Double deletions of sclB and each of the flb genes showed phenotypes with a complete abolishment of conidiophores ( Fig 7C ) . The ΔflbCΔsclB strain resembles the phenotypes of the other ΔflbΔsclB strains , indicating that SclB functions upstream of both parts of the Flb cascade and underlines the finding that SclB activates flbC and flbD . sclB OE is not sufficient to restore the wildtype phenotype in flb knock out strains , showing that SclB acts upstream of the Flb factors ( S4 Fig ) . Taken together , these findings demonstrate that SclB activates not only brlA but also both Flb cascades through the activation of flbC and flbD , which both merge and further activate brlA . Genome-wide analysis of SclB’s influence on gene expression suggests that approximately 25% of all SM gene clusters in A . nidulans are misregulated in the absence of sclB compared to wildtype ( Table 1 and S1 Table ) . The SclB-regulated interconnection of asexual development and secondary metabolism was examined in more detail by comparing SMs from sclB mutant and wildtype strains . Extracellular SMs were extracted with ethyl acetate from wildtype and the sclB mutant strains either grown for 48 h vegetatively or three and seven days under conditions inducing asexual or sexual development in wildtype . High-performance liquid chromatography ( HPLC ) revealed that the wildtype as well as the sclB OE strain , but not the ΔsclB strain , produce austinol and dehydroaustinol after three and seven days of asexual growth in light . Both compounds were identified in samples extracted from wildtype , the sclB complemented strain and the sclB OE strain according to their masses and UV/VIS absorption maxima ( Figs 8A and S5 ) [86] . ausA , coding for a polyketide synthase producing the intermediate 3 , 5-dimethyl orsellinic acid , and ausF , required for the synthesis of both austinol and dehydroaustinol [39] are not expressed during vegetative growth in wildtype and ΔsclB , but in the sclB OE strain ( Fig 8B ) . A third SM producing gene ausH , which is necessary for austinol and dehydroaustinol production , was basally expressed in wildtype , but not in ΔsclB , whereas the sclB OE strain showed upregulation of ausH transcription ( Fig 8B ) . This is in accordance with transcriptomic data indicating that backbone enzymes of both austinol clusters are downregulated in the absence of sclB compared to wildtype ( Table 1 and S1 Table ) . This indicates that SclB activates expression of the austinol gene cluster during vegetative growth . HPLC coupled to a qToF mass spectrometer revealed that the sclB OE strain produces increased amounts of emericellamide A , C and D [87] during vegetative growth ( Figs 9A and S6 ) . The ΔsclB strain produces only traces of these compounds under tested growth conditions and no fragmentation for emericellamide A and D could be obtained from mass spectrometry ( Fig 9A and S6 ) . Expression of the four genes of the emericellamide gene cluster , easA to easD , was analyzed in vegetatively grown cultures . easA and easD are basally expressed in wildtype . Only easA , but not easB , easC or easD , was basally expressed in the ΔsclB strain . In contrast , all four genes are upregulated in sclB OE ( Fig 9B ) . Furthermore , easD was significantly downregulated in genome-wide transcriptomic analysis in the absence of sclB compared to wildtype ( S1 Tab ) . This shows that SclB acts as activator of the eas gene cluster and is necessary for emericellamide biosynthesis . Taken together , SclB activates the expression of SM clusters for emericellamides , austinol and dehydroaustinol during vegetative growth . The adaptive response to oxidative stress is required for fungal development as endogenous signal and is an important determinant for fungal fitness in corresponding environmental conditions [40 , 88] . SclB is involved in the regulation of spore viability ( Fig 4C ) and genome-wide transcriptional analyses show that several genes related to the response to oxidative stress are differentially expressed when sclB is absent ( Fig 2 and S1 Table ) . Conidiospore survival was tested during H2O2 induced oxidative stress to analyze whether SclB is involved in the regulation of the oxidative stress response as well . Conidiospores of the wildtype , the complemented and the sclB OE strain show a linear loss in spore viability over time in the presence of 100 mM H2O2 ( Fig 10A ) . In contrast , conidiospores of the ΔsclB strain show a more rapid loss in viability over time in the presence of 100 mM H2O2 . Conidiospores from wildtype , sclB comp and sclB OE strains showed survival rates of approximately 86% after 30 min of H2O2 treatment , conidiospores of the ΔsclB strain showed only 62% survival . At the same time point conidiospores of the ΔvosA and the ΔvosAΔsclB strains showed even further reduced viability of only 40% ( ΔvosA ) and 30% ( ΔvosAΔsclB ) , respectively . Similar differences were measured over the whole time period of examination . This suggests that SclB positively regulates the oxidative stress response in A . nidulans . To investigate this further , expression of genes of the oxidative stress response was tested in submerged cultures in the presence or absence of H2O2 . The glutathione and the thioredoxin system are important parts of the oxidative stress response [89–91] . The thioredoxin system is encoded by trxA ( thioredoxin ) and trxR ( thioredoxin reductase ) [90] . trxA was especially induced upon treatment with H2O2 in the sclB OE strain ( S7 Fig ) . trxR is induced in wildtype in the presence of H2O2 but not induced in the ΔsclB strain ( Fig 10B ) . It is also downregulated in the absence of sclB during unstressed growth ( S1 Table ) . The sclB OE strain stressed with H2O2 shows an increased trxR upregulation compared to wildtype ( Fig 10B ) . glrA encodes the glutathione reductase [92 , 93] , which regulation was not dependent on the presence of sclB ( S7 Fig ) . The catA gene , encoding the spore-specific catalase A , is upregulated in wildtype but not induced in ΔsclB in presence of H2O2 ( Fig 10B ) . Expression of catA in the sclB OE strain is already upregulated during unstressed growth . Several transcription factors are involved in the response to oxidative stress . napA encodes the most prominent oxidative stress regulator in A . nidulans . napA gene expression was not found to be significantly regulated under applied conditions ( S7 Fig ) . RsmA is involved in the regulation of SMs and in oxidative stress response [91 , 94] . rsmA expression is around three fold induced in wildtype when H2O2 stress is applied ( Fig 10B ) . In sclB OE the induction of rsmA expression in the presence of H2O2 is even higher ( almost six fold ) , whereas rsmA expression is not induced by H2O2 in the ΔsclB strain . sclB itself is upregulated in wildtype and in sclB OE upon addition of H2O2 in comparison to unstressed situation ( Fig 10B ) . Taken together , these data suggest that SclB is involved in the regulation of the oxidative stress response in A . nidulans and specifically acts as a positive regulator of enzyme encoding genes , such as catA and thioredoxin genes , as well as the transcription factor-encoding gene rsmA . C6 proteins are typical fungal transcription factors . In silico analyses predicted SclB to be localized in the nucleus as determined by CELLO [95] and WoLF PSORT [96] . SclB was fused N- and C-terminally to sGFP to examine subcellular localization in vivo ( S8A Fig ) . The predicted molecular mass of both versions of the SclB GFP-fusion proteins is 87 . 46 kDa . Sizes of both fusion proteins determined by western hybridization are slightly higher than bioinformatically predicted ( S8B Fig ) , indicating posttranslational modifications . Treatment of GFP-SclB crude extracts with Lambda phosphatase resulted in a band shift on a western blot , suggesting that SclB is phosphorylated during vegetative growth ( S8C Fig ) . NetPhos 3 . 1 [97] predicted 28 codons for possible phosphorylation sites ( score value between 0 and 1 , cut off >0 . 7 ) . LC-MS/MS analyses revealed three phosphorylated SclB residues S327 , T464 and S506 in samples derived from vegetatively grown cultures , supporting that SclB is phosphorylated during vegetative filamentous growth ( S9A Fig ) . However , mutation of these residues and two serines adjacent to S506 ( S504 and S505 ) to alanine to mimic constant dephosphorylation ( sclBS327A , T464A , S506A ) or aspartic acid to mimic constant phosphorylation ( sclBS327D , T464D , S506D ) did not result in any obvious phenotype ( S9B Fig ) and the function of these phosphorylation sites therefore remains elusive . Both , the N- and C-terminal GFP fusion of SclB was expressed under control of the native sclB promoter and could complement the loss of sclB , demonstrating , that the fusion proteins are functional ( S9A Fig ) . Fluorescence microscopy revealed a subcellular localization of both versions of the SclB fusion protein in nuclei of hyphae during all growth conditions tested ( vegetatively , asexually and sexually grown ) as well as in conidiospores ( Fig 11A ) and germlings ( Fig 11B ) indicating permanent nuclear localization of SclB . GFP-trap pull downs with both , the N- and C-terminally tagged SclB versions , were conducted to investigate possible interactions of SclB with other proteins . These pull downs were conducted with cultures grown vegetatively , asexually and sexually and pulled down proteins were analyzed with LC-MS/MS . The majority of identified proteins are uncharacterized ( S2 Table ) . Four importins were identified: the essential karyophorin KapF ( importin ) was identified solely in samples of vegetatively grown cultures , whereas KapJ was identified in samples from strains grown in submerged cultures , as well as in light . KapB and KapI were identified in samples grown in light or dark . Together with a predicted NES and a predicted NLS , this indicates specific control of nuclear localization for SclB . RcoA was found in samples grown in submerged cultures and in the dark , conditions inducing sexual development in the wildtype . Furthermore , it was identified in samples grown in light , but below threshold . RcoA acts as transcriptional repressor and the RcoA-SsnF co-repressor-complex , which corresponds to yeast Tup1-Ssn6 , is essential for growth in Aspergilli [98–101] . Bimolecular fluorescence complementation experiments ( Bi-FC ) were performed to verify direct interaction of SclB and RcoA in vivo . Strains were constructed for these experiments , which express fusion proteins , where one half of a split YFP ( cYFP ) was fused to SclB and the other half ( nYFP ) to RcoA [102] . Two additional strains , expressing either SclB-cYFP and free nYFP or RcoA-nYFP and free cYFP , served as controls ( S9D Fig ) . Only a signal of the joint YFP halves , indicating a physical interaction of SclB and RcoA , could be identified in nuclei of hyphae ( Fig 11C ) . This indicates that SclB can interact directly with RcoA in vivo and might execute some of its regulatory roles in developmental programs , secondary metabolism and oxidative stress response as a heterodimer . The velvet domain protein VosA of Aspergillus nidulans binds more than a thousand fungal promoters and affects a substantial part of the transcriptome . One of these genes encodes the novel zinc cluster transcription factor SclB . VosA inhibits the expression of the sclB gene , which results in a slowdown and a decrease in asexual spore formation and a reduced production of secondary metabolites such as austinol , dehydroaustinol and emericellamides . SclB is not part of the fungal light response , which promotes the asexual program , but supports the cellular response upon H2O2 induced oxidative stress . SclB has a dual function as transcriptional activator for asexual development , but also as a repressor , presumably in combination with the repressor subunit RcoA , which we could identify as interacting partner . A genome-wide transcriptional analysis revealed that direct or indirect effects caused by the absence of the sclB gene result in more than 400 differentially expressed genes compared to wildtype ( S1 Table ) . 1 . 5 times as many of these genes are downregulated , as upregulated , in the absence of sclB . A large group of these genes are related to metabolic processes , as carbon or sulphur metabolism , or transporter activity . This most likely is a consequence of the distorted development of the ΔsclB mutant . On the other hand , several secondary metabolite and developmental genes including asexual regulatory genes as flbC or flbD , and rodA or dewA required for asexual spore formation are differentially regulated when SclB is not present in the cell . This suggests that SclB regulates asexual development and interconnected secondary metabolism in A . nidulans . SclB is localized in nuclei of germlings , conidiophores and hyphae . Four karyophorins were identified as putative interaction partners of SclB under different growth conditions and suggest a complex nuclear entry or exit control . SclB is phosphorylated at at least three residues during vegetative growth , but the function of these posttranslational modifications is yet elusive . Asexual spore formation requires the formation of the FluG protein . SclB accelerates an efficient formation of the asexual conidia in the absence of VosA by activating at least three regulatory genes downstream of FluG . Such an additional activator of conidiation had been predicted ( Fig 7B ) [11] . SclB increases flbC and flbD expression . The resulting FlbC and FlbD proteins as well as SclB activate the major asexual activator encoding gene brlA . The formation of the BrlA protein is necessary for the transition from stalk like aerial hyphae into mature conidiophores ( Fig 12 ) [83] . The molecular control mechanism by which VosA inhibits asexual differentiation is complex . VosA does not only repress the formation of the sclB gene product that acts as activator of the conidiation cascade , but also represses brlA itself during vegetative growth . De-repression only takes place , when the fungus obtains developmental competence and is triggered within a time window by the appropriate external signals for conidia formation [4 , 8] . In the further course of ongoing asexual development , the vosA gene is activated by the BrlA-downstream factors AbaA and WetA . The VosA velvet domain protein represses again the brlA and sclB genes and fulfils together with the VelB velvet domain protein its function to support spore viability [4 , 8 , 26] . SclB supports spore viability as well . One possible explanation might be that sclB gene expression is repressed by the VosA-VosA homodimer , which also represses brlA expression , whereas spore viability might be a regulatory function of the VosA-VelB heterodimer . SclB is not involved in the light control of A . nidulans , but is part of the response towards H2O2 induced oxidative stress . An internal oxidative stress signal caused by reactive oxygen species ( ROS ) serves as developmental signal in fungi and requires an appropriate fast and potent protective response [40 , 88 , 103] . ROS homeostasis therefore is crucial for the proceeding of asexual development . SclB activates elements of the fungal oxidative stress response including the thioredoxin system or catA for the spore specific catalase [89 , 90 , 104–106] . In addition , SclB activates the expression of the transcription factor RsmA during oxidative stress , which plays a similar dual role as SclB , because it is also part of the control of oxidative stress response and of secondary metabolism [91 , 94 , 107] . The SclB-mediated control for secondary metabolism includes several possible links to asexual differentiation . It is necessary for austinol , dehydroaustinol and emericellamide production and acts as activator of emericellamide , austinol and dehydroaustinol production through regulation of their gene clusters . An adduct of dehydroaustinol and diorcinol is able to overcome the conidiation defect of a ΔfluG mutant suggesting that they are involved in the FluG signal , which is crucial for the initiation of asexual development [108] . Orsellinic acid and the orsellinic acid-related diorcinol were also produced in high amounts in a ΔcsnE mutant compared to wildtype [40] . CsnE is part of the conserved COP9 signalosome ( CSN ) which controls the specificity of ubiquitin E3 cullin RING ligases for the protein degradation in the 26S proteasome [109 , 110] . CSN is required for the link between sexual development and the appropriate secondary metabolism , light control and the protection against oxidative stress [111–113] . The SclB function is involved in the alternative differentiation program . SclB connects asexual development to its specific secondary metabolism and also acts at the interphase to the response to oxidative stress . SclB interacts with RcoA in vivo . RcoA is a WD40 repeat protein , which regulates developmental programs and is required for the production of the mycotoxin sterigmatocystin as a member or the aflatoxin family [5 , 100 , 114 , 115] . A loss of rcoA in A . nidulans results in poor colony growth , impaired conidiation and the production of an orange pigment as indication of a misregulated secondary metabolism [100] . RcoA is part of the conserved SsnF-RcoA co-repressor complex corresponding to Ssn6-Tup1 in yeast , which represses numerous genes [99–101 , 116 , 117] . Target genes are repressed by several mechanisms such as through interacting with DNA-binding proteins and RNA polymerase II , through competition for promoter binding with other transcription factors , but also through histone acetylation and nucleosome positioning [118–122] . It is unclear whether there is only an RcoA-SclB heterodimer in the A . nidulans cell or whether SclB also interacts with RcoA-SsnF , because SsnF [99] could not be identified as putative SclB interaction partner . The exact molecular function of the SclB-RcoA interaction in the timely choreography of conidiation is unknown and might include as well activating as inhibiting control mechanisms during ongoing asexual development and its link to secondary metabolism and an oxidative stress response . Zinc cluster DNA-binding proteins belong to the most abundant transcription factors in the fungal kingdom [62] . SclB is present in nearly all Aspergilli and especially its C6 DNA-binding domain is highly conserved . Most C6 proteins are involved in either i ) primary or secondary metabolism or ii ) developmental programs [67] . SclB rather acts as global regulator and interconnects asexual development , secondary metabolism and the response to oxidative stress . Its C6 domain exhibits an uncommon architecture that is only found in less than 6% of all C6 proteins in A . nidulans . Other characterized A . nidulans C6 proteins with the same architecture as SclB function specifically in primary metabolic programs ( S7 Table ) [65 , 66] . Scl-2 is the SclB counterpart of A . niger . Loss of the sclB ortholog in A . niger results in reduced conidiation and impaired secondary metabolism [53] . This indicates similar regulatory effects in conidiation and secondary metabolism of A . niger Scl-2 and A . nidulans SclB . Wildtype A . niger cells form sclerotia as resting structures under very defined conditions [53 , 123] . Scl-2 also acts as a sclerotia repressor , because a corresponding scl-2 mutant strain produces sclerotia-like structures under conditions where the wildtype does not form these structures . SclB of A . nidulans is not a repressor of the formation of cleistothecia . Sclerotia have similarities with the sexual fruiting bodies of A . nidulans with the major difference that they are not linked to a sexual meiosis programme . These different control functions suggest that different fungi might have rewired the control of gene expression of this transcription factor in different developmental networks and contexts . The proposed sclB ortholog of A . fumigatus ( Afu6g11110 ) rescues the A . nidulans ΔsclB phenotype , which suggests that the molecular function of sclB therefore is conserved between A . nidulans and A . fumigatus . Some SclB functions might have changed in A . fumigatus , because it is dispensable for conidiation in this opportunistic human pathogen . Alternatively , a second redundant factor might compensate the effects of a sclB deletion , which is in agreement with other findings supporting that the conidiation cascade of A . fumigatus exhibits significant differences to its counterpart in A . nidulans . Deletion of fluG leading to diminished numbers of conidiophores in A . nidulans does not result in an obvious asexual phenotype in A . fumigatus [124 , 125] and functions of WetA , AbaA , velvet proteins or several Flb factors have changed [29 , 126] . Taken together , the VosA repression target SclB controls a novel genetic network in A . nidulans , which links conidiation to secondary metabolism and the response to oxidative stress . Further studies will broaden our understanding of the interconnection and complex mutual control of developmental programs and the production of bioactive molecules in response to environmental conditions and stresses in filamentous fungi . This is especially important , as a vast amount of bioactive natural products are still unknown and might have deleterious as well as beneficial potential to humans [38 , 127 , 128] . The SclB genetic network is a sub-network of the velvet domain network , which bridges secondary metabolism and development in fungi . In contrast , other known subnetworks of VosA , as BrlA regulating the conidiation cascade , are more specialized for a specific program . This study shows that velvet domain subnetworks include different categories as encaptic as BrlA , as well as independently acting elements as SclB . The amount of putative SclB targets and its congeneric as well as independent or even antithetic functions to VosA suggest that SclB , downstream of VosA , itself regulates a large network of downstream genes . VosA binds to more than thousand gene promoters and this network further extends through transcription factors as SclB that act themselves as master regulators . AGB551 ( veA+ ) was used as A . nidulans wildtype . Afs35 was used as A . fumigatus wildtype . Wildtype and mutant strains ( see S3 Table ) were grown in minimal medium ( MM ) ( 1% glucose , 7 mM KCl , 2 mM MgSO4 , 70 mM NaNO3 , 11 . 2 mM KH2PO4 , 0 . 1% trace element solution pH 5 . 5 [129] ) supplemented with 0 . 1% pyridoxine-HCl , 5 mM uridine , 5 mM uracil or 4-aminobenzoic acid , when needed . Strains were grown for two days on solid MM containing 2% agar in light at 37°C and two day old spores were harvested for further experiments . For synchronized growth strains were grown in submerged cultures for 24h and subsequently shifted onto solid agar plates . Escherichia coli strains ( S4 Table ) were grown on solid lysogeny broth ( LB ) [130] medium ( 1% tryptone , 0 . 5% yeast extract , 1% NaCl ) or in liquid LB shaking on a rotary shaker at 37°C . 100 mg/ml ampicillin was added to prevent plasmid loss . For extraction of genomic DNA strains were grown over night ( o/n ) in liquid cultures . Mycelia was harvested through Miracloth filters , frozen in liquid nitrogen and ground with a table mill . Ground mycelia was mixed with 500 μl genomic DNA lysis buffer [131] and incubated 15 min at 65°C . Subsequently mycelia solution was mixed with 100 μl 8 M potassium acetate and centrifuged for 15 min at 13 , 000 rpm at room temperature ( RT ) . Supernatant was mixed with 100 μl 8 M potassium acetate and centrifuged for 15 min at 13 , 000 rpm at RT . Supernatant was mixed with 300μl isopropanol and centrifuged 10 min at 13000 rpm at RT . Pellets were washed twice with 70% ethanol and dried at 42°C before resolving in H2O at 65°C . DNA fragments for plasmid constructions were amplified with PCR from A . nidulans FGSC A4 or A . fumigatus Afs35 genomic DNA , respectively , and cloned into pBluescript SK ( + ) using the Geneart Seamless Cloning and Assembly kit , the Seamless PLUS Cloning and Assembly Kit and the Seamless Cloning and Assembly Enzyme Mix ( Invitrogen ) or via fusion PCR and subsequent cloning into pBluescript SK ( + ) with the CloneJET PCR Cloning Kit ( Thermo Scientific ) or via employment of T4 ligase ( Thermo Scientific ) according to manufacturer’s instructions . Plasmids were amplified in E . coli and extracted with the Qiaprep Spin Miniprep Kit ( Qiagen ) or the NucleoSpin Plasmid Miniprep Kit ( Macherey-Nagel ) according to manufacturer’s instructions . For the production of the plasmids pME4304 and pME4305 the pyrithiamine resistance cassette ( ptrA ) of pSK485 [72] was replaced by the nourseothricin resistance cassette ( natR ) from plasmid pNV1 [132] ( primer pair JG846/847 ) or the phleomycin resistance cassette ( phleoR ) from plasmid pME3281 [133] ( primer pair JG848/849 ) , respectively , by usage of the Seamless Cloning and Assembly Kit ( Invitrogen ) . Both cassettes additionally carried one half of the PmeI restriction site at both ends . The recyclable marker cassettes from pME4304 and pME4305 are called natRM and phleoRM , respectively , in the following . The recyclable marker cassette from pSK485 is called ptrARM in the following . For production of pME4575 , the 2 . 7 kb long 5’ and 2 . 2 kb long 3’ region of the sclB ( AN0585 ) gene were amplified with primer pairs kt208B/214 and kt211/224 , respectively , and together with the natRM cassette cloned into the EcoRV multiple cloning site of pBluescript SK ( + ) , employing the Seamless Cloning and Assembly Kit ( Invitrogen ) . The deletion cassette was subsequently excised with MssI and transformed into AGB551 , resulting in the strain AGB1007 . For production of pME4578 , the 1 . 3 kb nitrate-inducible promoter ( PniaD ) , amplified with primer pair kt251/252 , the sclB open reading frame ( ORF ) itself and a small part of the 3’ region ( 1 . 8 kb ) , amplified with kt241/253 , the sclB 5’ region ( kt208b/214 ) and the natRM cassette were cloned into pBluescript SK ( + ) , employing the Seamless Cloning and Assembly Kit ( Invitrogen ) . The PniaD::sclB construct was subsequently excised with MssI and transformed into AGB551 , resulting in AGB1008 . For production of pME4576 , sgfp was amplified from pME4292 with primers kt229/SR18 and , together with the sclB ORF and its 5’ flanking region ( 4 . 4 kb , primers kt208b/228 ) , the sclB 3’ region ( primers kt211/224 ) and the natRM cassette was cloned into pBluescript SK ( + ) , employing the Seamless Cloning and Assembly Kit ( Invitrogen ) . Subsequently , the sclB::sgfp construct was excised from pME4576 with MssI and transformed into AGB1007 resulting in AGB1009 . Successful transformation at the correct locus was verified by Southern hybridization . For production of pME4579 , the 5’ flanking region of sclB ( primers kt209/307 ) , sgfp ( primers SR120/121 ) , sclB ORF ( primers kt230/231 ) , the phleoRM cassette and the sclB 3’ flanking region ( primers kt211/225 ) were cloned into pBluescript SK ( + ) , employing the Seamless Cloning and Assembly Kit ( Invitrogen ) . Subsequently , the sgfp::sclB construct was excised from pME4579 with MssI and transformed into AGB1007 , obtaining AGB1010 . The plasmid pME3173 was transformed into AGB1009 and AGB1010 , resulting in AGB1012 and AGB1013 , respectively , to facilitate the visualization of nuclei . pME3173 was transformed into AGB551 resulting in AGB1014 to obtain a suitable negative control for microscopy . For production of pME4577 , the sclB ORF and its 5’ UTR ( 4 . 4 kb , primers kt208b/231 ) , the sclB 3’ UTR ( primers kt211/224 ) and the phleoRM cassette were cloned into pBluescript SK ( + ) , employing the Seamless Cloning and Assembly Kit ( Invitrogen ) . The sclB complementation cassette was excised from pME4577 with MssI and cloned into AGB1007 , resulting in AGB1011 . For production of pME4581 , 1 kb of the fluG 5’ flanking region ( primers kt341/342 ) , 1 kb of the 3’ flanking region ( primers kt343/364 ) and the phleoRM cassette were cloned into the EcoRV restriction site of pBluescript SK ( + ) , employing the Seamless Cloning and Assembly Kit ( Invitrogen ) . The fluG deletion cassette was excised from pME4581 with MssI and integrated into AGB551 , AGB1007 and AGB1008 , resulting in AGB1016 , AGB1017 and AGB1018 , respectively . For production of pME4589 , 1 . 7 kb of the brlA 5’ region ( primers kt487/488 ) , 1 . 2 kb of the brlA 3’ region ( primers kt489/490 ) and the phleoRM cassette were cloned into pBluescript SK ( + ) , employing the Seamless Cloning and Assembly Kit ( Invitrogen ) . The ΔbrlA cassette was excised from pME4589 with MssI and integrated into AGB551 and AGB1007 , resulting in AGB1031 and AGB1032 , respectively . For production of pME4591 , 1 . 2 kb of the flbB 5’ region ( primers kt515/516 ) , 1 kb of the flbB 3’ ( primers kt517/518 ) and the phleoRM cassette were cloned into pBluescript SK ( + ) , employing the Seamless Cloning and Assembly Kit ( Invitrogen ) . The ΔflbB cassette was excised from the pME4591 with MssI and integrated into AGB551 , AGB1007 and AGB1008 , resulting in AGB1035 , AGB1036 and AGB1037 , respectively . For production of pME4593 , 1 . 2 kb of the flbC 5’ region ( primers kt519/520 ) , 1 kb of the flbC 3’ region ( primers kt521/522 ) and the phleoRM cassette were cloned into pBluescript SK ( + ) , employing the Seamless Cloning and Assembly Kit ( Invitrogen ) . The ΔflbC cassette was excised from pME4593 with MssI and integrated into AGB551 , AGB1007 and AGB1008 , resulting in AGB1039 , AGB1040 and AGB1041 . For production of pME4595 , 1 . 1 kb of the flbD 5’ region ( primers kt523/524 ) , 1 . 2 kb of the flbD 3’ region ( primers kt525/526 ) and the phleoRM cassette were cloned into pBluescript SK ( + ) , employing the Seamless Cloning and Assembly Kit ( Invitrogen ) . The ΔflbD cassette was excised from pME4595 with MssI and integrated into AGB551 , AGB1007 and AGB1008 , resulting in AGB1043 , AGB1044 and AGB1045 , respectively . For production of pME4597 , 1 . 3 kb of the flbE 5’ region ( primers kt527/528 ) , 1 . 1 kb of the respective 3’ region ( primers kt529/530 ) and the phleoRM cassette were cloned into pBluescript SK ( + ) , employing the Seamless Cloning and Assembly Kit ( Invitrogen ) . The ΔflbE cassette was excised from pME4597 with MssI and integrated into AGB551 , AGB1007 and AGB1008 , resulting in AGB1047 , AGB1048 and AGB1049 . For Bi-FC plasmid construction , sclB and rcoA were amplified from cDNA instead of genomic DNA . The bidirectional nitrate-inducible promoter was excised from pME4607 in a two-step digestion with MssI and SmiI and both , the pME4607 backbone vector and the nitrate inducible promoter were utilized for all Bi-FC constructs . For production of pME4599 , the sclB ( primers kt407/415 ) and rcoA ORFs ( primers kt409/418 ) were fused to ceyfp ( primers kt416/417 ) and neyfp ( primers kt421/422 ) , respectively by fusion PCR [134] . Subsequently , sclB::ceyfp , rcoA::neyfp and the bidirectional nitrate-inducible promoter were cloned into the pME4607 backbone vector , employing the Seamless Cloning and Assembly Kit ( Invitrogen ) . pME4599 was ectopically integrated into AGB1007 resulting in AGB1051 and AGB1014 , resulting in AGB1052 . For production of pME4601 , free ceyfp ( primers kt416/SR195 ) , rcoA::neyfp and the bidirectional nitrate-inducible promoter were cloned into the pME4607 backbone vector , employing the Seamless Cloning and Assembly Kit ( Invitrogen ) . pME4601 was introduced into AGB551 and AGB1014 , resulting in AGB1054 and AGB1056 , respectively . For production of pME4600 , free neyfp ( primers kt422/SR193 ) , sclB::ceyfp and the nitrate-inducible promoter were cloned into the pME4607 backbone vector , employing the Seamless Cloning and Assembly Kit ( Invitrogen ) . pME4600 was introduced into AGB551 and AGB1014 , resulting in AGB1053 and AGB1055 , respectively . For production of pME4574 , the veA 5’ ( primers JG863/985 ) and 3’ ( primers JG865/866 ) regions and the natRM cassette were cloned into pBluescript SK ( + ) , employing the Seamless Cloning and Assembly Kit ( Invitrogen ) . The ΔveA construct was excised from pME4574 with MssI and transformed into AGB551 resulting in AGB1066 . The ΔsclB cassette from pME4575 was integrated into AGB1066 , resulting in AGB1067 . For production of pME4605 , the velB 5’ ( primers SR05/06 ) and 3’ ( primers SR07/08 ) regions and the natRM cassette were cloned into pBluescript SK ( + ) , employing the Seamless Cloning and Assembly Kit ( Invitrogen ) . The ΔvelB construct was excised from pME4605 with MssI and transformed into AGB551 resulting in AGB1064 . The ΔsclB cassette from pME4575 was integrated into AGB1064 , resulting in AGB1065 . For production of pME4602 , the velC 5’ ( primers kt203/145 ) and 3’ ( primers kt146/204 ) regions and the natRM cassette were cloned into pBluescript SK ( + ) , employing the Seamless Cloning and Assembly Kit ( Invitrogen ) . The ΔvelC construct was excised from pME4602 with MssI and transformed into AGB551 resulting in AGB1062 . The ΔsclB cassette from pME4575 was integrated into AGB1062 , resulting in AGB1063 . For production of pME4603 , the vosA 5’ ( primers SR11/12 ) and 3’ ( primers SR13/14 ) regions and the natRM cassette were cloned into pBluescript SK ( + ) , employing the Seamless Cloning and Assembly Kit ( Invitrogen ) . The ΔvosA construct was excised from pME4603 with MssI and transformed into AGB551 and AGB1007 , resulting in AGB1057 and AGB1058 , respectively . pME4578 was integrated into AGB1057 , resulting in AGB1059 . For production of pME4606 , the sclB 5’ ( primers kt215/221 ) and 3’ ( primers kt218/226 ) flanking regions and the ptrARM were cloned into pBluescript SK ( + ) , employing the Seamless Cloning and Assembly Kit ( Invitrogen ) . The ΔsclB cassette was excised from pME4606 with MssI and integrated into Afs35 , resulting in AfGB129 . For production of pME4580 , the sclB 5’ region and the sclB ORF , the sclB 3’region and the phleoRM marker cassette were cloned into pBluescript SK ( + ) , employing the Seamless Cloning and Assembly Kit ( Invitrogen ) : the first 1 kb part of the sclB ORF together with its 1 . 9 kb 5’ region was amplified with primers kt209/430 , introducing the first mutation in the gene product ( S327A ) . The next 431 bp of the sclB ORF were amplified with primers kt431/432 , introducing the mutation T464A in the gene product . Adjacent 135 bp were amplified with the primer pair kt433/434 and the last 172 bp of the sclB ORF were amplified with the primer pair 442/231 , introducing S504-506A in the gene product . The mutated sclB ORF and its 5’ adjacent region were fused in a series of fusion PCRs [134] from these four sequences . The complete mutated sclB ORF and its 5’ region , the sclB 3’ adjacent region ( primers kt211/225 ) and the phleoRM cassette were cloned into pBluescript SK ( + ) in a seamless cloning reaction . The sclBS327A , T464A , S504-506A cassette was excised from pME4580 with MssI and integrated into AGB1007 , resulting in AGB1015 . Similarly , the sclBS327D , T464D , S504-506D plasmid pME4610 was constructed using primers kt209/651 , kt652/653 , kt654/655 and kt657/696 . The sclBS327D , T464D , S504-506D cassette was excised from pME4610 with MssI and integrated into AGB1007 , resulting in AGB1147 . For production of pME4587 , 1 . 5 kb of the abaA 5’ region ( primers kt354/355 ) , the phleoRM cassette and 1 . 4 kb of the abaA 3’ region ( primers kt356/363 ) were cloned into pBluescript SK ( + ) , employing the Seamless Cloning and Assembly Kit ( Invitrogen ) . The ΔabaA cassette was excised from pME4587 with MssI and integrated into AGB551 and AGB1007 , resulting in AGB1028 and AGB1029 , respectively . For production of pME4609 , the sclB 5’ ( primer pair kt209/603 ) and 3’ regions were amplified from A . nidulans genomic DNA . The sclB ORF was amplified with primer pair kt254/233 from A . fumigatus genomic DNA and the three fragments were together with the natRM cassette cloned into pBluescript SK ( + ) . The construct was excised from pME4609 using MssI and transformed into AGB1007 , resulting in AGB1042 . A . nidulans was transformed by polyethylene glycol-mediated protoplast fusion as described before [135 , 136] . E . coli transformations were carried out as described in [137 , 138] . Plasmids used in this study are given in S5 Table and oligonucleotides can be found in S6 Table . Successful transformation of constructs into A . nidulans was verified by Southern hybridization [139] employing the AlkPhos Direct Labelling and Detection System according to manufacturer’s instructions ( GE Healthcare ) . Conidiospores were harvested in 0 . 96% NaCl solution containing 0 . 002% Tween 80 after 2 days and counted with a hemocytometer ( Marienfeld Superior ) . Conidiospores were diluted with 0 . 96% NaCl solution containing 0 . 002% Tween 80 , and kept at 4°C . Aliquots of 200 spores of these dilutions were plated after zero and seven days and plates were incubated for two days at 37°C in the light . This test was performed in triplicates per experimental day . For spore survival in the presence of 100 mM H2O2 , spores were diluted with 0 . 96% NaCl solution containing 0 . 002% Tween 80 in 15 ml reaction tubes and 100 mM H2O2 was added . Reaction tubes were kept in the dark at RT under constant gyration to prevent sedimentation of spores . 200 spores were plated at indicated time points and plates were incubated as mentioned above . Statistical analyses were conducted with t-tests using standard deviations of wildtype data against indicated mutant data sets . For extraction of secondary metabolites from asexually grown cultures 1*106 spores were plated and grown for 3 or 7 days in light . Spores were completely washed off and the agar was cut into small pieces . Subsequently , secondary metabolites were extracted from agar pieces with 300 ml ethyl acetate by shaking at 160 rpm at 30°C for 30 min followed by 15 min ultra-sonication at highest level . Ethyl acetate was evaporated and the crude extract was kept at -20°C . For extraction from vegetatively grown cultures , 1*107 spores were grown in submerged cultures for 48 h at 37°C on a rotary shaker and mycelia were removed with Miracloth filters . Extraction procedure was followed according to Gerke and co-workers [38] . Samples were stored at -20°C . Analytical HPLC/UV-DAD/ELSD measurements were performed using the following system: HPLC pump 420 , SA 360 autosampler , Celeno UV-DAD HPLC detector , ELSD-Sedex 85 evaporative light-scattering detector ( ERC ) ) with a Nucleodur 100–5 C18 end-capped ( ec ) column ( 250 mm x 3 mm ) and the solvent system: A = H2O + 0 . 1% ( v/v ) trifluoroacetic acid ( TFA ) , B = acetonitrile + 0 . 1% ( v/v ) TFA ( Goebel Instrumentelle Analytik GmbH ) . Secondary metabolite extracts were dissolved in 500 μl methanol and an injection volume of 20 μl was analyzed under gradient conditions ( 20% B to 100% B in 20 minutes ) with a flow rate of 0 . 5 ml/min . HPLC data was analyzed with the Geminyx III software ( Goebel Instrumentelle Analytik GmbH ) . For UHPLC-UV and UHPLC-ESI-HRMS/MS analysis crude extracts were solved in 1 ml methanol and analyzed using a Dionex Ultimate 3000 system ( Thermo Scientific ) connected to an Impact II qTof mass spectrometer ( Bruker ) . 5 μl of each sample was injected for separation on an UHPLC reversed phase column ( Acquity UPLC BEH C18 1 . 7 lmRP 2 . 1 x50 mm column ( Waters ) with an Acquity UPLC BEH C18 1 . 7 lmRP 2 . 1 x 5 mm pre-column ( Waters ) ) applying a linear acetonitril/0 . 1% formic acid in H2O/0 . 1% formic acid gradient ( from 20% to 95% acetonitril/0 . 1 formic acid in 20 min ) with a flow rate of 0 . 4 ml/min at 40°C . For internal mass calibration a 10 mM sodium formate solution was used . Data analysis and sum formula predictions were performed with Bruker Compass DataAnalysis 4 . 3 . GST tagged VosA protein was expressed and purified , as described by Ahmed and collaborators [2] . Purification was executed and monitored on an Äkta Explorer10 system ( GE Healthcare ) . Amicon Ultra Centrifugal Filter Units ( Millipore ) were used for concentration after size exclusion chromatography . EMSAs were performed as described earlier [2] . Briefly DNA probes were generated by annealing a reverse-complementary oligonucleotide pair . Protein and DNA was mixed and incubated 15 min at RT and dispersed according to molecular weight on a 6% polyacrylamide gel in 0 . 5% running buffer prior to staining with ethidium bromide . Photomicrographs were obtained with an Axiolab microscope ( Carl Zeiss Microscopy ) and a SZX12-ILLB2-200 binocular microscope ( Olympus ) . Fluorescence microscopy was performed with a Zeiss AxioObserver Z . 1 inverted confocal microscope , equipped with Plan-Neofluar 63x/0 . 75 ( air ) and Plan-Apochromat 100x/1 . 4 oil objectives ( Zeiss ) . The SlideBook 6 . 0 software ( Intelligent Imaging Innovations ) was used for picture processing . Strains were grown in 8-well borosilicate cover glass system ( Thermo Scientific ) in 400 μl MM supplemented as mentioned above , when needed , or on glass slides covered with 1 ml solid MM supplemented as mentioned above , when needed , at 37°C or 30°C . GFP-signals were normalized against wildtype background signal to subtract fungal auto fluorescence . Nuclei were visualized by ectopic integration of pgpdA::rfp::h2A into the respective strains or through staining with 0 . 1% 4’ , 6’-diamidino-2phenylindole ( DAPI ) . Conidiospore numbers were determined with a Coulter Z2 particle counter ( BECKMAN COULTER GMBH , Krefeld , Germany ) or with a Thoma cell counting chamber ( hemocytometer ) ( Marienfeld Superior ) . For quantifying cleistothecia , agar plugs of 5 mm2 were cut out from plated using the larger side of a 200 μl pipette tip and cleistothecia were individualized on a fresh agar plate and counted with help of a binocular microscope SZX12-ILLB2-200 binocular microscope ( Olympus ) . ANOVA and t-test statistical analyses were conducted using standard deviations . Mutant samples were always compared to wildtype data for two-sample comparison through t-test . For RNA isolation strains were grown vegetatively or asexually . Mycelia was harvested through sterile filters ( Miracloth ) and immediately frozen in liquid nitrogen . Frozen mycelia were ground with a table mill ( Retsch ) directly before RNA extraction . RNA was extracted with the RNeasy Plant Miniprep Kit ( Qiagen ) according to manufacturer’s instructions . cDNA was transcribed from 0 . 8 μg RNA with the QuantiTect Reverse Transcription Kit ( Qiagen ) . To measure gene expression real-Time-PCR was performed by using MESA GREEN qPCR MasterMix Plus for SYBR Assay ( Eurogentec ) in a CFX Connect Real-Time System ( BioRad ) and analysed with the CFX Manager software ( BioRad ) . Expression of the housekeeping genes gpdA ( A . nidulans and A . fumigatus ) , h2A ( A . nidulans and A . fumigatus ) and 15S rRNA ( A . nidulans ) were used for normalization . For measurement of the expression of oxidative-stress related genes , strains were grown in submerged cultures at 37°C on a rotary shaker for 24 h . Subsequently , 5 mM H2O2 was added . Control strains were left untreated . Incubation was prolonged for another 30 min shaking on the rotary shaker and mycelia were harvested as described above . Total RNA of strains grown under submerged culture conditions for 24 h at 37°C under constant agitation on a rotary shaker was isolated using the Direct-zol Miniprep Kit ( Zymo Research ) according to manufacturer’s conditions . RNA quality control was performed on a Bioanalyzer 2100 Fragment Analyzer using a Pico Chip ( RNA ) ( Agilent ) . RNA sequencing was performed at the Core Unit , the Transcriptome and Genome Analysis Laboratory , University Medical Center Göttingen . RNA integrity was assessed using the Fragment Analyzer ( Advanced Analytical ) and only samples exhibiting RNA integrity number ( RIN ) > 8 were selected for sequencing . Libraries were performed starting with 800 ng of total RNA using the TruSeq Stranded Total RNA Sample Prep Kit from Illumina ( Cat . No . RS-122-2201 ) . Library sizing ( 295–320 bp ) and quality was performed using the Fragment Analyzer ( Advanced Analytical ) . Library quantitation was performed by using Promega’s QuantiFluor dsDNA System . RNA-sequencing was performed using the Illumina HighSeq-4000 platform ( SR 50 bp; >30 Mio reads /sample ) . Demultiplexig was done using bcl2fastq2 . Raw reads were aligned using STAR version STAR_2 . 4 . 1a [140] against EnsemblFungi [141] revision 37 Aspergillus nidulans genome . Differential expression analysis was performed using edgeR [142] . Information gathered from the Aspergillus Genome Database ( AspGD ) [64] and Fungal and Oomycete Genomic Resources Database ( FungiDB ) [73] were used to categorize genes according to putative functions of their products . AspGD and FungiDB were employed for updated respective descriptions . For genetic ORFs , which were merged into a new ORF in FungiDB ( FungiDB 36; released 19 . Feb . 2018 ) , the new merged ORF was taken into consideration for all downstream analyses . Genome wide transcriptome data was submitted to EBI ArrayExpress under accession E-MTAB-6996 . Strains were grown under vegetative conditions and mycelia were harvested through sterile filter ( Miracloth ) and directly frozen in liquid nitrogen . Frozen mycelia were ground in liquid nitrogen with a table mill and approximately 200 mg was mixed with 300 μl B+ buffer ( 300 mM NaCl , 100 mM Tris pH 7 . 5 , 10% glycerol , 1 mM EDTA , 0 . 1% NP-40 ) supplemented with 1 . 5 mM DTT , complete EDTA-free protease inhibitor cocktail ( ROCHE ) , 0 . 001 mM PMSF , phosphatase inhibitor mix ( 1 mM NaF , 0 . 5 mM sodium-orthovanadate , 8 mM ß-glycerolphosphate disodium pentahydrate ) and 1 . 5 mM benzamidine , and centrifuged for 15 min at 13000 rpm at 4°C . Supernatant was transferred into fresh test tubes and protein concentration was measured with a NanoDrop ND-1000 spectrophotometer . Protein pulldowns employing GFP-trap_A beads ( Chromotek ) were conducted as described earlier [98 , 143] with some alterations . A . nidulans strains were inoculated in a concentration of 5*108 spores in 500 ml MM . Mycelia were harvested and immediately frozen in liquid nitrogen . Frozen mycelia were ground with a table mill in liquid nitrogen . Ground mycelia were mixed with B+ buffer in a ratio of 1:1 and centrifuged twice for 20 min at 4000 rpm at 4°C . Supernatant was filtered through 20 μm sterile filters ( Sartorius ) and mixed with 1:100 GFP-trap_A beads ( Chromotek ) and incubated o/n at 4°C . Equal amounts of protein were loaded on 10% SDS gels ( separation gel: 2 . 8 ml H2O , 3 . 75 ml 1 M Tris pH 8 . 8 , 100 μl 10% ( w/v ) SDS , 3 . 3 ml 30% ( v/v ) acrylamide , 10 μl TEMED , 50 μl 10% ( w/v ) APS; stacking gel: 3 . 67 ml H2O , 625 μl 1 M Tris pH 6 . 8 , 30 μl 10% ( w/v ) SDS , 650 μl 30% ( v/v ) acrylamide , 5 μl TEMED , 25 μl 10% ( w/v ) APS ) and separated at 200V . Proteins from SDS gels were blotted for 1h at 100 V ice cooled or at 35 V o/n at RT to nitrocellulose membranes ( Whatman ) . Membranes were blotted with 5% skim milk powder dissolved in TBST buffer ( 10 mM Tris-HCl pH8 . 0 , 150 mM NaCl , 0 . 05% Tween 20 ) for 1 h at RT and subsequently probed with 1:250 diluted GFP antibody ( sc-9996 , Santa Cruz Biotechnology ) . Following , membranes were washed three times in TBST and horseradish peroxidase coupled mouse antibody ( 115-035-003 , Jackson Immuno Research ) was applied as secondary antibody in a dilution of 1:2000 . Crude cell extracts were prepared as described above . B+ buffer was not supplemented with phosphatase inhibitor mix for this experiment . Crude cell extract were mixed with or without lambda phosphatase ( NEB ) according to manufacturer’s conditions and with or without phosphatase inhibitor mix in excess , and incubated for 30 min . at 30°C prior to boiling for 10 min . at 95°C together with loading dye . Subsequently , western hybridization experiments were performed as described above . Trypsin digestion of proteins was performed as published by Shevchenko and collaborators using Sequencing Grade Modified Trypsin ( Promega ) [144] . Following this procedure peptides were purified using the StageTip purification method [145 , 146] . Purified peptides were separated by reversed-phase liquid chromatography employing an RSLCnano Ultimate 3000 system ( Thermo Scientific ) followed by mass analysis with an Orbitrap Velos ProHybrid mass spectrometer ( Thermo Scientific ) as described [98 , 143 , 147 , 148] . For further details see [149] . MS/MS2 data processing for peptide analysis and protein identification was performed either with the MaxQuant 1 . 5 . 1 . 0 and Perseus 1 . 5 . 3 or the Proteome Discoverer 1 . 4 software ( Thermo Scientific ) and the Mascot and SequestHT search algorithms . Phosphosite probabilities were calculated with the phosphoRS search algorithm [150 , 151] . Three unique peptides [152] and three MS/MS counts were demanded for positive protein identification . Furthermore , only proteins identified from at least two out of three biological repetitions were considered further . Proteins also identified from the control strain ( AGB596 ) were regarded as false-positives and excluded from further consideration .
Velvet domain proteins of filamentous fungi are structurally similar to Rel-homology domains of mammalian NF-κB proteins . Velvet and NF-κB proteins control regulatory circuits of downstream transcriptional networks for cellular differentiation , survival and stress responses . Velvet proteins interconnect developmental programs with secondary metabolism in fungi . The velvet protein VosA binds to more than ten percent of the Aspergillus nidulans promoters and is important for the spatial and temporal control of asexual spore formation from conidiophores . A novel VosA-dependent genetic network has been identified and is controlled by the zinc cluster protein SclB . Although zinc cluster proteins constitute one of the most abundant classes of transcription factors in fungi , only a small amount is characterized . SclB is a repression target of VosA and both transcription factors are part of a mutual control in the timely adjusted choreography of asexual sporulation in A . nidulans . SclB acts at the interphase of asexual development and secondary metabolism and interconnects both programs with an adequate oxidative stress response . This study underlines the complexity of different hierarchical levels of the fungal velvet protein transcriptional network for developmental programs and interconnected secondary metabolism .
You are an expert at summarizing long articles. Proceed to summarize the following text: Cartilage and bone are formed into a remarkable range of shapes and sizes that underlie many anatomical adaptations to different lifestyles in vertebrates . Although the morphological blueprints for individual cartilage and bony structures must somehow be encoded in the genome , we currently know little about the detailed genomic mechanisms that direct precise growth patterns for particular bones . We have carried out large-scale enhancer surveys to identify the regulatory architecture controlling developmental expression of the mouse Bmp5 gene , which encodes a secreted signaling molecule required for normal morphology of specific skeletal features . Although Bmp5 is expressed in many skeletal precursors , different enhancers control expression in individual bones . Remarkably , we show here that different enhancers also exist for highly restricted spatial subdomains along the surface of individual skeletal structures , including ribs and nasal cartilages . Transgenic , null , and regulatory mutations confirm that these anatomy-specific sequences are sufficient to trigger local changes in skeletal morphology and are required for establishing normal growth rates on separate bone surfaces . Our findings suggest that individual bones are composite structures whose detailed growth patterns are built from many smaller lineage and gene expression domains . Individual enhancers in BMP genes provide a genomic mechanism for controlling precise growth domains in particular cartilages and bones , making it possible to separately regulate skeletal anatomy at highly specific locations in the body . The vertebrate skeleton is constructed of cartilage and bone tissues that are formed into highly specific shapes , sizes , and repeating arrays during normal development . Individual bones can show striking morphological specializations in different species , suggesting that separate genetic mechanisms must exist for regulating the growth of skeletal tissue at highly specific anatomical sites in the body [1] , [2] . Despite the importance of skeletal structures for support , protection , eating , breathing , and movement , the detailed genetic mechanisms controlling the shape and growth of individual bones are still poorly understood . Over fifty years ago , Bateman proposed that characteristic skeletal shapes are determined by varying patterns of differential growth and erosion that occur in stereotyped positions along the surfaces of each bone [3] . Localized growth at ends of a bone results in long straight structures . Uniform deposition around a bone produces uniform circumferential growth . Preferential deposition and erosion on opposite surfaces of a bone generates lateral displacement or curvature . Localized patches of deposition and erosion may also produce many of the specific processes , ridges , foramina , and articular surfaces that are characteristic of each bone in the body . Although highly localized patterns of deposition and erosion have long been proposed or visualized in the skeleton [4]–[6] , little is known about how such stereotyped patterns may be encoded in the genome . Previous studies demonstrate that secreted signaling molecules in the bone morphogenetic protein ( BMP ) family play a key role in both formation and repair of skeletal structures [7] . These molecules are expressed both in early skeletal precursors , and in the surface perichondrium and periosteum layers that surround growing cartilage and bone [8]–[13] . Pure recombinant BMPs can induce cartilage and bone formation when implanted at ectopic sites in animals [14] , [15] . Conversely , mouse mutants missing members of the BMP family show defects in subsets of bone and cartilage elements . The classical mouse short ear locus encodes one of the mammalian BMP molecules ( BMP5 ) [16] . Mutations at this locus reduce outer ear growth by disrupting the formation and activity of the surface perichondrium surrounding outer ear cartilage [17] . The same locus also controls the presence or absence of processes on specific vertebrae and the fibula , the morphology of the xiphoid process at the end of the sternum , the number of ribs along the vertebral column , and the total volume of the thoracic cavity [18]–[21] . A large number of spontaneous and induced short ear mutations suggest that the Bmp5 locus is surrounded by large regulatory regions required for developmental expression patterns in bones and other tissues [22]–[24] . Here we carry out detailed enhancer surveys to test the regional specificity of regulatory sequences controlling Bmp5 expression in skeletal tissues . Our studies suggest that stereotyped growth patterns along the surface of both ribs and nasal cartilages are controlled by highly specific “anatomy” elements in the Bmp5 gene . These modular enhancers in BMP genes may provide a flexible basis for encoding the detailed growth and form of specific bones in the vertebrate skeleton . Previous regulatory scans of the Bmp5 locus identified several large regions that could drive expression of a lacZ reporter gene in developing skeletal structures [23] , [24] . Expression in developing ribs was observed when two different bacterial artificial clones ( BACs ) covering non-overlapping regions of the gene ( Figure 1A , E , H ) were coinjected with a minimal heat shock-lacZ expression construct [24] . BAC199 includes most of the exons and introns of the Bmp5 gene . In contrast , BAC178 includes sequences from a large region three prime ( 3′ ) of all Bmp5 coding exons . Chromosome rearrangements in this 3′ region have generated two regulatory alleles of the Bmp5 locus ( Bmp5se38DSD and Bmp5se4CHLd , Figure 1A ) . These alleles confirm that extensive 3′ sequences are required for normal expression and function of the endogenous Bmp5 gene [23] . To compare the lacZ expression in ribs driven by different BACs spanning the Bmp5 locus , we examined a series of coronal sections taken along the dorsoventral axis of the ribs of transgenic embryos beginning near the vertebral column and ending near the sternum . In dorsal sections from a BAC199-lacZ transgenic embryo , β-galactosidase activity was surprisingly restricted to a lateral domain within the rib perichondrium ( Figure 1F ) . This pattern changed as sections progressed ventrally . In later sections , β-galactosidase activity was found in both the lateral and medial rib perichondrium ( Figure 1G ) . Interestingly , the pattern of lacZ expression controlled by the distal BAC clone , BAC178 , was complementary to that seen with proximal BAC199 . In dorsal sections from a BAC178-lacZ transgenic embryo , β-galactosidase activity was found in anterior , medial and posterior domains of the rib perichondrium ( Figure 1I ) . More ventral rib sections showed loss of medial expression but retained β-galactosidase activity predominantly in anterior and posterior rib perichondrium ( Figure 1J ) . Thus , BAC178-lacZ rib expression complements BAC199-lacZ rib expression as it changes along the dorsoventral axis ( Figure 1F , I and G , J ) . Taken together , these results suggest that gene expression in different domains of the rib perichondrium is controlled by distinct regulatory elements in the Bmp5 locus . Notably , the complementary rib regulatory regions are separated by over 100 kilobases ( kb ) ( Figure 1A ) . Analysis of endogenous Bmp5 expression in wild-type and Bmp5se38DSD regulatory mutants confirms the existence of distinct control regions for different domains of the rib perichondrium . The Bmp5se38DSD regulatory mutation derives from a chromosomal rearrangement whose breakpoint lies near the Bmp5 coding exons [22] . Therefore , this rearrangement is predicted to remove all distal rib control sequences ( Figure 1A ) . In situ hybridization analysis of Bmp5 transcripts in dorsal rib sections shows reduction of anterior , medial and posterior rib domain expression within Bmp5se38DSD ribs as compared to wild-type ( Figure 1C , D ) . In contrast , strong Bmp5 expression is still seen in the lateral rib perichondrium ( asterisk in Figure 1D ) , as expected given the location of lateral control elements upstream of the Bmp5se38DSD breakpoint . Therefore , general Bmp5 expression in rib perichondrium appears to be a composite of smaller , independently regulated expression domains . To further characterize and localize Bmp5 regulatory sequences , we used sequence alignment programs PipMaker and LAGAN/VISTA to compare human and mouse Bmp5 loci [25]–[27] . This approach revealed numerous evolutionarily conserved non-coding regions ( ECRs ) scattered across the Bmp5 locus ( Figure 2 , Figure S1 ) . We then cloned multiple small genomic fragments containing single or multiple ECRs upstream of a minimal heat shock-lacZ reporter cassette , injected them into fertilized mouse eggs , and scored expression patterns in transgenic embryos . The survey of putative enhancer regions extended across the entire 400 kb interval detailed in Figure 1A ( see Figure 2A and Figure S1 ) . A 6 . 2 kb clone from the BAC199 region including 4 ECRs surrounding Bmp5 exon 4 ( Ex4r ) drove reproducible expression in nasal cartilages , distal limbs , and ribs ( Figure 2B ) . As with the BAC transgenics , a series of coronal sections was taken along the ribs of Ex4r-lacZ transgenics . Dorsal rib sections again revealed expression in a restricted domain along the lateral surface of the developing rib ( Figure 2C ) . To further characterize this peripheral surface domain we hybridized adjacent sections with molecular markers for perichondrium ( type I collagen , Col1a1 ) , chondrocytes ( type II collagen , Col2a1 , and type X collagen , Col10a1 ) , and developing muscle ( MyoD1 ) [28]–[30] . The lacZ-positive region corresponds to a particular sector of the surface perichondrium surrounding the ribs , which otherwise extends in a continuous circle around the developing rib cartilage ( Figure 2C–F ) . Unlike the BAC199-lacZ transgene , the Ex4r sequence did not drive discrete localized expression in a medial perichondrium domain in more ventral rib sections ( data not shown ) . These results demonstrate that anatomical control sequences for lateral perichondrium expression map within the exon 4 region , and that additional sequences are required for the medial rib expression seen with BAC199 . To further narrow the region required for lateral perichondrium expression , we tested a series of smaller genomic fragments and deletions of conserved ECRs from within the Ex4r subclone ( Figure S2 ) . This analysis demonstrated that the core sequences necessary for lateral perichondrium expression reside in a 1069 bp peak of conservation at the 3′ end of the Ex4r region , and that other sequences in the Ex4r construct are required for expression in limbs and nasal cartilages . Bmp5 is expressed in the perichondrium surrounding many other skeletal structures , including the nasal septum and the shelf-like turbinates that project into the nasal cavity [11] . In addition , new micro computerized tomography ( MicroCT ) analysis of wild-type and Bmp5 mutant skulls shows that the Bmp5 gene is also required for normal development of turbinates in the anterior nasal region ( Figure 3A , B ) , and for normal branching patterns in more posterior nasal regions ( Figure 3C , D ) . To determine whether Bmp5 expression in nasal cartilages is also controlled by separable regulatory sequences , we examined the nasal region in both BAC199-lacZ and Ex4r-lacZ transgenic embryos . The larger BAC199 clone showed widespread β-galactosidase activity throughout the nasal cartilages , including multiple turbinates and the nasal septum ( data not shown ) . In contrast , Ex4r-lacZ transgenics showed activity restricted to a small arc-like domain located on the inner surface of nasal cartilage between turbinate shelves in the anterior nasal cavity and along the neck of the developing turbinates ( Figure 3E ) . Like the lateral rib expression , turbinate expression was seen predominantly in subregions of the surface perichondrium ( Figure S3A , C ) . No expression was seen in the nasal septum , in posterior cartilages or at the tips of the shelf-like projections of the turbinates themselves . Testing fragments of the Ex4r clone demonstrated that sequences directing restricted nasal cartilage expression and restricted lateral rib perichondrium expression are distinct ( Figure S2 ) . Examination of other regions of the Bmp5 locus known to contain skeletal enhancers identified an additional non-overlapping sequence that also gives expression in nasal cartilages . A 17 kb clone ( Phage 7 in Figure 1 ) previously reported to give thyroid cartilage expression [23] also showed strikingly specific nasal cavity expression . β-galactosidase activity was seen at the dorsal tips of the expanding turbinate shelves , colocalizing with Col2a1 in proliferating chondrocytes , but was absent from the ventral tips , the turbinate necks , and the cartilages between turbinate shelves ( Figure 3F , Figure S3B , F ) , a pattern partially complementary to that driven by the Ex4r construct . The sequences included in Phage 7 are located approximately 100 kb 3′ of the chromosome breakpoint in the Bmp5se38DSD regulatory mutation ( Figure 1A ) . Endogenous Bmp5 expression is dramatically reduced in the dorsal tips of turbinate shelves in Bmp5se38DSD mice compared to wild-type ( Figure 3G , H ) , as well as in the cribriform plate , the structural roof of the nasal cavity . These data confirm that 3′ regulatory sequences are required for Bmp5 expression in the tips of turbinate shelves , but not in the surrounding neck and inter-turbinate perichondrium . Bmp5se38DSD mutant mice also show defects in the cribriform plate and branching alterations in nasal turbinates ( data not shown ) . Thus , in both ribs and nasal cartilage , an apparently continuous layer of perichondrium consists of distinct expression domains controlled by separate regulatory elements in the Bmp5 gene . Ribs are derived from somitic mesoderm [31] . Previous chick-quail lineage tracing experiments have shown that rib cells arise from different portions of developing somites: the head , neck , and the inner surface of ribs are derived from the posterior compartment of somites ( white regions in Figure 4A ) , while the lateral surface of the mid shaft of the rib arises from the anterior compartment of somites ( blue regions of Figure 4A ) [32] . We noticed that lacZ expression driven by the Ex4r construct begins some distance from the vertebral column , and is strongest along the midshaft of ribs ( Figure 4B ) , a pattern reminiscent of the anatomical domain thought to arise from anterior somites . To analyze rib enhancer activity at additional developmental stages , we generated stable transgenic lines for the Ex4r-lacZ construct and collected embryos beginning at embryonic day 10 . 5 . At this early stage of development , the Ex4r-lacZ construct is expressed in the anterior halves of developing somites ( Figure 4C ) . Examination of lacZ localization in rib sections at later stages showed that Ex4r-driven expression was largely missing from the head and neck region of ribs , was present in the lateral perichondrium along the rib shaft , and became symmetric around the rib in sternal portions ( Figure 4D–F ) . Both the somitic expression and the changes in patterns along the length of ribs suggest that the lateral rib expression reflects the dual origin of ribs from separate somite compartments . Multiple BMP family members are expressed in overlapping patterns in the developing ribs [12] . To test the biological effects of localized increase or decrease in BMP signaling in subdomains of the rib perichondrium , we used the Ex4r sequence to drive the expression of either a constitutively active ( caBmprIb ) or dominant negative ( dnBmprIb ) version of BMP receptor IB [33] . We chose BmprIb because it is widely expressed throughout the developing skeleton , including rib perichondrium , and is known to be used by multiple BMP ligands [33]–[38] . Each receptor construct was coinjected with the original Ex4r-lacZ clone to generate transgenic embryos . Both Ex4r-caBmprIb and Ex4r-dnBmprIb transgenic embryos showed gross changes in rib development at E14 . 5 when examined by whole-mount skeleton preparations ( Figure 5A–C ) . Increased BMP signaling in the lateral rib domain caused an overgrowth of alcian blue-positive cartilage , beginning midway along the rib shaft ( Figure 5B arrow ) . Sections through Ex4r-caBmprIb embryos that were assayed for β-galactosidase activity showed that rib expansion was accompanied by an excess of lacZ-positive cells in the lateral rib ( Figure 5E ) . This lateral expression marked the outer edge of the rib deformation ( Figure S4A , C ) and overlays a cartilaginous mass of cells made up predominantly of hypertrophic chondrocytes expressing Col2a1 and Col10a1 ( Figure S4E , G ) [28] , [29] . In contrast , decreased BMP signaling caused a marked deflection in rib trajectory ( Figure 5C bracket ) . Ribs in Ex4r-dnBmprIb transgenic embryos emerged normally from the vertebral column but were deflected inwards along the central region of the rib shaft , producing a more constricted upper thorax . This deformation in trajectory was not accompanied by changes in rib cross section ( Figure 5F , Figure S4 ) . Neither construct affected the head or neck of the ribs ( Figure 5B , C ) , as expected from the restricted expression domain of Ex4r control sequences along the rib shaft ( Figure 4B ) . The highly localized domains of Bmp5 expression in rib perichondrium are reminiscent of previous models suggesting that rib growth occurs by differential activity on the lateral and inner surfaces of the rib [3] . To visualize in vivo patterns of bone deposition in growing ribs , we injected mice twice , at 6 and 7 weeks , with calcein , a fluorescent dye that specifically incorporates into newly formed bone ( Figure 6 ) . Dorsal rib cross-sections showed two major growth domains labeled with dye; one visible along the lateral periosteal surface ( D1 ) , and a second predominantly along the anterior , medial , and posterior endosteal surfaces of the rib ( D2 ) . Each bone deposition front is represented by two calcein labelings , reflecting the two separate injections ( Figure 6A ) . Injections with different dye colors demonstrate that the bone fronts labeled by the initial injection ( arrows in Figure 6A ) become embedded in bone after a week of growth , and that the new bone fronts labeled by the second injection are found near surfaces ( arrowheads in Figure 6A , data not shown ) . These deposition patterns show striking asymmetry , with bone deposition occurring preferentially in the lateral domain of the outer surface periosteum and in the anterior , medial , and posterior domains of the inner endosteum . To compare patterns of bone deposition and bone resorption , dorsal rib cross-sections were also examined for tartrate-resistant acid phosphatase activity , an osteoclast marker [39] . Bone resorption was also highly asymmetric , and complementary to the areas of bone deposition ( Figure 6B ) . In the outer periosteum , osteoclast activity was most intense on the anterior , medial , and posterior surfaces of the rib; and was nearly absent along the lateral surface where major bone deposition was occurring . Likewise , along the inner endosteum , osteoclast activity was most intense on the lateral wall , and nearly absent from the anterior , medial and posterior surfaces . During growth , these highly asymmetric patterns of bone deposition and resorption would result in the net lateral displacement of ribs and the expansion of the intrathoracic cavity , while preserving marrow space . Bmp5 mutant mice are known to have a smaller thoracic volume than wild-type animals [21] . To further characterize detailed bone deposition patterns in Bmp5 mutants , we performed dual calcein injections on Bmp5 null and Bmp5 regulatory mutants , and measured the amount of bone deposition in the different rib domains described above ( Figure 6C ) . Mice with null mutations in the Bmp5 gene show a significant reduction in bone deposition in both major ossification domains , D1 and D2 ( Figure 6C ) . In contrast , regulatory mutant mice missing anterior , medial and posterior but not lateral rib control sequences ( Bmp5se4CHLd , Figure 1A ) show significantly reduced bone deposition in D2 , but not in D1 domains . The Bmp5 gene is thus required for normal rates of bone growth on both the outer and inner surface of the rib , and these two growth domains are controlled independently by different regulatory regions of the Bmp5 locus . It has long been recognized that cartilage and bone can be molded into a remarkable range of different shapes and sizes . Previous genetic studies show that the morphology of different skeletal elements is controlled by multiple independent genetic factors [2] , [40] , [41] . Based on studies of jaw and limb morphology in mice , Bailey previously suggested that different subregions of a single bone must be controlled by a large number of independent “morphogenes” , each active in small patches along the surface of a single bone [2] . Despite recent progress identifying genes that regulate formation of all cartilage or all bones , or genes that control skeletal formation in different subdomains along the body axis , little is known about the fine-grained mechanisms that control detailed growth patterns of individual skeletal elements [42] . Here we show that highly defined growth domains in particular bones are controlled by remarkably specific enhancers in the Bmp5 gene ( Figure 7 ) . We propose that anatomy-specific enhancers in BMP genes provide a genomic mechanism for independent developmental control of local growth along discrete domains of individual cartilages and bones in the vertebrate skeleton . When BMP genes were first discovered and assayed for expression in vertebrates , individual members of the family were initially proposed to promote general steps in the differentiation of all skeletal tissue [8] . Although Bmp5 is expressed in a continuous fashion in the perichondrial layer surrounding many developing skeletal structures [11] , [12] , our enhancer surveys do not show evidence for general enhancers in the Bmp5 gene that drive expression around the surface of all cartilage or all bones . Instead , distinct Bmp5 enhancers regulate expression in individual skeletal structures . Furthermore , separate enhancers also exist for discrete domains around the surface of individual bones , including lateral , anterior , medial , and posterior domains of the rib perichondrium , and tip versus neck and inter-turbinate domains in the nasal cartilages ( Figures 1 , 3 ) . This remarkably fine control of gene expression is clearly sufficient to alter skeletal morphology at specific locations ( Figure 5 ) . Null and regulatory mutations also show that the Bmp5 gene is necessary for normal bone deposition rates along particular surfaces of growing ribs ( Figure 6 ) . These results confirm that detailed growth patterns in an individual bone can be encoded by highly specific anatomy enhancers in genes for bone morphogenetic proteins . Previous studies of HOX genes have shown that expression and function at particular anatomical locations in the body are related to the physical location of genes along the chromosome [43]–[45] . The overall correlation between anatomy and gene position may arise from progressive changes in chromatin structure during body axis development; or from proximity to enhancers that map outside the HOX complex , which have decreasing effects on genes that map at increasing physical distances from the enhancer [45] , [46] . In contrast , the Bmp5 skeletal enhancers we have identified to date show no obvious relationship between anatomical position in the body and physical location within the Bmp5 locus . The regulatory elements for discrete surface domains around a single bone clearly map to different regions of the Bmp5 gene ( Figure 1 ) . In addition , rib and nose enhancers are interspersed with each other ( Figure 7 ) and with other separate enhancer regions previously identified controlling expression in the sternum , thyroid cartilage , lung , and genital tubercle [23] , [24] . The dispersed enhancer pattern seen in Bmp5 may reflect the different roles of BMP and HOX genes in skeletal patterning . Nested sets of HOX gene expression are evolutionarily ancient programs used to pattern basic body axes in both vertebrates and invertebrates [44] , [45] , [47] . In contrast , both cartilage and bone are more evolutionarily recent , vertebrate-specific tissues that vary widely in form from species to species [1] . For example , respiratory nasal turbinates are thought to have arisen separately in bird and mammals to help conserve water during breathing [48]–[50] . They vary widely in branching structure within mammals , and are reduced or absent in fish , amphibians , and reptiles [48] , [51] . Since a variety of studies suggest that BMPs are the endogenous signals used to induce cartilage and bone in vertebrates [7] , formation of nasal turbinates and other species-specific skeletal structures presumably occurs through cis- or trans-acting alterations that produce local changes in BMP expression at particular sites in the body . Therefore , the complex architecture of skeletal enhancers in the Bmp5 gene may reflect a historical process of piecemeal gain and loss of regulatory elements controlling local domains of BMP expression . How are the remarkably specific domains of Bmp5 expression generated along the surface of ribs or nasal cartilages ? A variety of data suggests that mechanical forces can give rise to highly localized patterns of bone deposition and erosion [52] , [53] . For example , rib cages and skulls both enclose rapidly growing tissues . Outward pressure from soft tissue growth may lead to bone deposition on skeletal surfaces under mechanical tension ( the convex outermost surface of ribs or cranial bones ) , and bone erosion on surfaces under compression ( the innermost surface of ribs or cranial bones ) . Although mechanical tension and compression are clearly coupled to bone remodeling , we do not think that the restricted patterns of expression we observe for Bmp5 enhancers are simply responding to the distribution of mechanical forces on growing skeletal structures . First , there is no obvious relationship between mechanical forces and the contrasting tip and neck expression patterns seen in nasal cartilages . Second , the Bmp5 enhancer that drives expression along the outer surface of ribs is not similarly expressed along the outer surface of either the sternum or the skull , although these bones should be subject to similar mechanical forces from the rapid expansion of underlying tissue . Third , the Ex4r-lacZ construct that drives highly localized patterns of expression in growing ribs also drives compartmentalized expression in developing somites ( Figure 4 ) . These results suggest that the remarkably specific Bmp5 domains in ribs are related to the dual origin of ribs from different somite compartments , rather than to simple mechanical forces acting during later growth and expansion of the thoracic cavity . Previous lineage tracing experiments have shown that the lateral edges of rib shafts are derived from cells in the anterior half of somites [32] ( Figure 4A ) . Response elements for anterior somite transcription factors could provide a simple mechanism for controlling mid-shaft Bmp5 expression in the lateral perichondrial domain . Conversely , response elements for posterior somite expression could provide another simple mechanism for regulating Bmp5 expression in rib head and necks , and in the anterior , medial , and posterior perichondrial domains along the rib shaft , similar to the patterns seen with BAC178 ( Figure 1 ) . The current sizes of Bmp5 rib enhancers are still too large to identify particular binding sites for upstream factors . However , future narrowing of the minimal sequences capable of driving rib domain expression may make it possible to link specific somite transcription factors with the different domains of rib expression identified in this study . The dual origin of axial structures from anterior and posterior halves of adjacent somites produces vertebrae and ribs that form one half segment out of register with the original metameric pattern seen in somites . The functional significance of this shift has been debated for over a hundred years [31] , [54]–[56] . Resegmentation causes axial muscles , and many of their origin and insertion points on adjacent vertebrae and ribs , to all be derived from a single somite . Our studies suggest resegmentation also plays a key role in establishing detailed growth patterns in developing ribs ( Figure 7 ) . Although ribs are usually thought of as simple tubular structures , they can be extensively modified in different organisms to produce the diverse cross-sectional shapes , as well as the varied curvatures seen in wide- and narrow-bodied animals [1] , [57] . It has long been recognized that differential deposition on the lateral surface of ribs must underlie the expansion and ultimate shape of ribs and thoracic cavities [3] . We suggest that resegmentation helps establish the lineage domains that make it possible to independently control cartilage and bone growth in specific rib surface domains . The multiple enhancers present in BMP genes provide an elegant mechanism for linking such lineage domains to actual sites of bone growth , leading to highly detailed patterns of deposition that can be independently controlled along the length and around the circumference of a single bone . While lineage domains may be used to produce separate lateral versus medial domains of gene expression in developing ribs , we think additional mechanisms must be operating to produce other highly localized patches of Bmp5 expression . For example , our comparison of BAC199 and BAC178 expression suggests at least four different expression domains may exist at certain positions along the ribs ( lateral , medial , anterior , posterior; Figure 1 ) . Control sequences for the lateral domain have been mapped to a single 1069 bp peak of sequence conservation within the Bmp5 Ex4r region , but additional sequences responsible for expression in the other domains remain to be identified in the larger regions covered by BAC199 and BAC178 . Highly localized expression patterns are also seen in multiple spatially restricted patches along the necks and tips of nasal cartilages ( Figure 3 ) . The elements controlling these patches are distinct from those controlling rib expression . In addition , nasal cartilage development is quite different from rib morphogenesis ( Figure 7 ) . For example , the facial bones and cartilages are derived from cranial neural crest that migrates from positions in the developing brain [58]–[60] . HOX genes are not expressed in this cranial region , and transplantation studies have demonstrated a remarkable degree of plasticity in the cranial neural crest populations [61] , [62] . Patterning signals are thought to emerge from the local endoderm and ectoderm to control the shape and size of individual facial skeletal structures [61] , [63] . Therefore , unlike ribs , we currently know of no lineage compartments that can account for the various separate tip and shelf domains seen during the branching morphogenesis of nasal cartilages . In Drosophila , branching morphogenesis takes place during tracheal airway development , and is controlled by numerous local patches of breathless/FGF expression . The specific enhancers controlling breathless expression near tips of growing trachea branches have not been isolated , but may respond to different combinations of transcription factors that are themselves expressed in local or intersecting patterns [64] . Multiple , locally acting enhancers in BMP and FGF genes may thus represent a common molecular strategy for molding skeletal tissue or trachea airways into particular shapes in different animals [7] , [64] . Further studies of anatomy-specific elements in BMP genes should lead to a molecular understanding of the type of “morphogenes” that have long been postulated to control local growth decisions in different subdomains of particular bones [2] . In addition , gain and loss of regulatory elements in BMP genes may provide a simple genomic mechanism for evolutionary modification of skeletal structures . While null mutations in BMP genes often have pleiotropic defects , adaptive changes in specific regulatory sequences could localize effects to particular skeletal structures , making it possible to alter vertebrate anatomy while preserving viability and fitness [7] . This possibility has taken on renewed interest in light of studies linking changes in BMP expression to different beak shapes in naturally occurring bird species [65]–[67] , and to different jaw morphologies in African cichlids [68] . Regulatory lesions are difficult to identify , and it has not yet been possible to track particular bird or fish anatomical changes to specific DNA sequence alterations in BMP genes . Nonetheless we think the kind of modular regulatory architecture we have found for the Bmp5 gene probably exists around many other members of the BMP family [69] , [70] . Isolation and characterization of additional anatomy elements from BMP genes will make it possible to test whether anatomical changes in naturally occurring species result from structural and functional modifications in the type of modular enhancer regions identified in this study . Regulatory ( Bmp5se38DSD , Bmp5se4CHLd ) and null ( Bmp5null ) alleles were described previously [11] , [22] , [23] . All strains used for bone growth assays are on the C57Bl/6J background . The generation of BAC199-lacZ , BAC178-lacZ and Phage7-lacZ transgenics was reported in [23] , [24] . All new DNA constructs were prepared for microinjection as previously described [24] . The Ex4r-caBmprIb and Ex4r-dnBmprIb plasmids were coinjected with the Ex4r-lacZ clone at a 4∶1 molar ratio . Pronuclear injection into FVB embryos was carried out by the Stanford Transgenic Facility and Xenogen Biosciences in accordance with protocols approved by the Stanford University Institutional Animal Care and Use Committee . BAC426K2 ( Genbank accession #AC079245 ) and BAC343K17 ( Genbank Accession #AC079244 ) were isolated from the RPCI-23 Female ( C57Bl/6J ) Mouse BAC library ( Invitrogen ) using a 1334 bp EcoRI probe and/or a 591 bp HaeIII probe located 123 , 536 bp and 225 , 112 bp , respectively , from the Bmp5 transcriptional start site . Sequences were compiled following designation of BAC426K2 and BAC343K17 as clones of high biomedical interest by the National Human Genome Research Institute and sequencing by the Advanced Center for Genome Technology at the University of Oklahoma , Norman . 5′ mouse sequences were added from BAC429A10 ( Genbank accession #AC144940 ) as they became available . Human Bmp5 genomic sequence was compiled from the following clones: Genbank accession numbers AL589796 , AL137178 , AL133386 , AL590290 , AL590406 and AL592426 . The human and mouse Bmp5 sequences were masked using RepeatMasker ( A . F . A . Smit , R . Hubley and P . Green , unpubl . ; http://www . repeatmasker . org/ ) . ECRs were identified using global sequence alignment programs as previously described [69] . The Ex4r-lacZ plasmid was generated by amplifying a 6221 bp fragment corresponding to mouse sequences 93 , 656–99 , 876 bp in Figure 2 using primers 622: 5′GGATTGCGGCCGCTATGGACAGCTTTGAAGAGCTTTGGTA3′ and 624: 5′GGATTGCGGCCGCTATTCTAGCCTCTCCTGTAGGATTATG3′ . Following NotI digestion , the fragment was cloned into the Not5'hsplacZ vector [23] . To generate Ex4r-caBmprIb and Ex4r-dnBmprIb constructs , the constitutively active ( ca ) or dominant negative ( dn ) form of BmprIb was amplified using primers lpf21: 5′CATGCCATGGCCATGCTCTTACGAAGCTCTGGAAAAT3′ and lpf22: 5′GCTCTAGAGCTTAGATCCCCCCTGCCCGGTTATTATTATCAGAGTTTAATGTCCTGGGACTCTG3′ . The PCR products were digested with NcoI/XbaI and cloned into the Ex4r-lacZ plasmid that had been digested with NcoI and XbaI ( partial ) , replacing the lacZ cassette . To generate plasmid Ex4rCD-lacZ , a 3 kb fragment was amplified from the Bmp5 BAC426K2 using primers 624 ( above ) and 627: 5′GGATTGCGGCCGCTATTCTAGGCTGTTGGAAAGCAAGTCTA3′ . The PCR product was digested with NotI and cloned into Not5'hsplacZ . Construct Ex4rΔC-lacZ was generated using primers 750: 5′ATGTGGCCAAACAGGCCTATTAATGGTCAACCAGATGAATACAGCA3′ and 751: 5′ATGTGGCCTGTTTGGCCTATTATAGAACACATAGAGGCATACCAGG3′ to amplify directly from the Ex4r-lacZ plasmid using the Expand Long Template PCR system ( Roche #1681834 ) . The 12 . 9 kb product was digested with SfiI and the free ends were ligated together . The 5488 bp insert with a 733 bp deletion of ECR C from Ex4r was removed by NotI digestion and recloned into an unamplified Not5'hsplacZ vector . The Ex4rΔD-lacZ plasmid was generated by amplifying 4677 kb and 454 bp products from BAC426K2 using primer 622 ( above ) with primer 754: 5′ATGTGGCCTGTTTGGCCTATTCCTTTTGAGAATCTCGGCTTCTAGA3′ and primer 752: 5′ATGTGGCCAAACAGGCCTATTGGCAGGTTAGAGAAAGTAATGATAG3′ with primer 624 ( above ) , respectively . The PCR products were digested with SfiI and ligated together . The resulting 5 . 1 kb product containing a 1069 bp deletion of ECR D from Ex4r was digested with NotI and ligated into the Not5'hsplacZ vector . Plasmid ECRD-lacZ was generated by amplifying a 1127 bp fragment from BAC426K2 using primers 690: 5′ATGTGGCCTGTTTGGCCTATTCTTTCTCTAACCTGCCTCTACCCTG3′ and 736: 5′ATGTGGCCAAACAGGCCTATTGAAGCCGAGATTCTCAAAAGGTGGA3′ . The PCR product was digested with SfiI and ligated into pSfi-hsplacZ [69] . Embryos collected by Xenogen Biosciences were fixed for 1 hour in 4% paraformaldehyde in 1× PBS at 4°C , placed in cold 1× PBS and shipped overnight on ice . Whole-mount staining for β-galactosidase activity was performed as described [69] with the following modifications: Embryo fixation times varied with age ( E10 . 5 for 30 minutes , E13 . 5 for 75 minutes , E14 . 5 for 90 minutes ) . E13 . 5-E14 . 5 embryos were hemisected after 1 hour . Rib and nasal cartilage cryosections from lacZ whole-mount embryos were collected and counterstained as described [69] . Prior to embedding , samples were equilibrated in embedding solution ( 15% sucrose , 7 . 5% gelatin ( 300 Bloom , Sigma #G2500 ) in 1× PBS ) for 1 hour at 42°C . Ex4r-caBmprIb and Ex4r-dnBmprIb transgenic embryos were frozen in OCT compound ( Tissue Tek ) , cryosectioned at 25 microns and counterstained with Nuclear Fast Red ( Vector labs , #H-3403 ) . β-galactosidase activity on cryosections was assayed by fixing samples in 4% paraformaldehyde in 1× PBS for 5–8 minutes at room temperature . Slides were rinsed 3 X 5 minutes with 1× PBS , washed in lacZ wash buffer ( 0 . 1 M sodium phosphate buffer ( pH 7 . 3 ) , 2 mM MgCl , 0 . 01% deoxycholate , 0 . 02% Nonidet P-40 ) for 10 minutes and incubated in lacZ stain ( wash buffer supplemented with 4 mM K3Fe ( CN ) 6 , 4 mM K4Fe ( CN ) 6⋅3 H2O , 0 . 1M Tris ( pH 7 . 4 ) and 1 mg/mL X-gal ( Sigma #B4252 ) ) at 37°C for at least 24 hours . Stained sections were rinsed with 1× PBS , fixed for an additional 10 minutes in 4% paraformaldehyde in 1× PBS , and counterstained with Nuclear Fast Red . The Bmp5 , Col10a1 , and Col2a1 probes used were described [23] , [29] , [71] . The MyoD1 probe was generated from a clone ordered from Open Biosystems ( clone id 372340 ) . The Col1a1 probe was generated using the pMColI-Bam plasmid ( a gift of Dr . Ernst Reichenberger ) . Timed matings were performed to collect wild-type ( C57Bl/6J ) , Ex4r-lacZ , Phage7-lacZ , and Bmp5se38DSD mutant heads and/or torsos at E13 . 5-E15 . 5 . Ex4r-caBmprIb and Ex4r-dnBmprIb embryos generated by Xenogen Biosciences were collected at E15 . 5 and fixed for 1 hour in 4% paraformaldehyde in 1× PBS , bisected , and one half embryo embedded in OCT and one half analyzed for β-galactosidase activity to identify transgenic embryos . 12 micron sections were collected from samples frozen in OCT compound and analyzed for gene expression as previously described [72]; except that the color reagent BM purple ( Roche #1442074 ) was used in place of NBT/BCIP . E14 . 5 skeletons were prepared as described [73] , with the following modifications: Embryos were placed directly into staining solution after ethanol dehydration . Following potassium hydroxide treatment , embryos were cleared in 50% glycerol overnight , and then stored in 100% glycerol . All steps were done at room temperature . Two successive intraperitoneal injections of calcein ( Sigma # C0875 , 2 . 5 mg/ml in 1× PBS ) were performed at postnatal day 43 ( p43 ) and p51 on C57Bl/6J males ( 10 mg injected/kg body weight ) . Whole rib cages were collected at p53 and dehydrated in ethanol for at least 1 week at 4°C , then embedded in methylmethacrylate and ground sectioned to obtain 50 micron coronal sections by HMAC ( Birmingham , AL ) . To quantify levels of bone deposition in wild-type and mutant animals , calcein labeled rib cages from six males of each category ( C57Bl/6J , Bmp5se4CHLd and Bmp5null ) were equilibrated overnight in 15% sucrose in 1× PBS and at least 24 hours in 30% sucrose in 1× PBS , all at 4°C . Rib cages were bisected , and the right half was embedded in OCT . Six 50 micron coronal cryosections were taken approximately 1 mm apart , beginning at the growth plate and moving dorsally . Each section was digitally photographed , and pixel areas between labeled bone deposition fronts were measured with Photoshop . All measurements were taken on the fifth rib . Data are expressed as mean areas±s . e . m . relative to wild-type mice . Differences between groups were evaluated using Student's t-test . C57Bl/6J male rib cages were collected at p53 into cold 1× PBS , fixed in 4% paraformaldehyde in 1× PBS for 3 days at 4°C , and washed 3 times for 30 minutes in cold 1× PBS . The right halves were embedded in paraffin , sectioned , and stained by HMAC [74] . Scans from 4 wild-type and 5 Bmp5null mutant skulls , aged 4 weeks postnatally , were generated using a Scanco MicroCT-40 operated at a tube potential of 45 kV and tube current of 177 microA using a 0 . 30 second integration with 2× averaging . All samples had undergone skeletal preparation prior to scanning .
Every bone in the skeleton has a specific shape and size . These characteristic features must be under separate genetic control , because individual bones can undergo striking morphological changes in different species . Researchers have long postulated that the morphology of individual bones arises from the local activity of many separate growth domains around each bone's surface . Differential growth within such domains could modify size , curvature , and formation of specific processes . Here , we show that local growth domains around individual bones are controlled by independent regulatory sequences in bone morphogenetic protein ( BMP ) genes . We identify multiple regulatory sequences in the Bmp5 gene that control expression in particular bones , rather than all bones . We show that some of these elements are remarkably specific for individual subdomains around the surface of individual bones . Finally , we show that local BMP signaling is necessary and sufficient to trigger highly localized growth patterns in ribs and nasal cartilages . These results suggest that the detailed pattern of growth of individual skeletal structures is encoded in part by multiple regulatory sequences in BMP genes . Gain and loss of anatomy-specific sequences in BMP genes may provide a flexible genomic mechanism for modifying local skeletal anatomy during vertebrate evolution .
You are an expert at summarizing long articles. Proceed to summarize the following text: Identifying sources of variation in DNA methylation levels is important for understanding gene regulation . Recently , bisulfite sequencing has become a popular tool for investigating DNA methylation levels . However , modeling bisulfite sequencing data is complicated by dramatic variation in coverage across sites and individual samples , and because of the computational challenges of controlling for genetic covariance in count data . To address these challenges , we present a binomial mixed model and an efficient , sampling-based algorithm ( MACAU: Mixed model association for count data via data augmentation ) for approximate parameter estimation and p-value computation . This framework allows us to simultaneously account for both the over-dispersed , count-based nature of bisulfite sequencing data , as well as genetic relatedness among individuals . Using simulations and two real data sets ( whole genome bisulfite sequencing ( WGBS ) data from Arabidopsis thaliana and reduced representation bisulfite sequencing ( RRBS ) data from baboons ) , we show that our method provides well-calibrated test statistics in the presence of population structure . Further , it improves power to detect differentially methylated sites: in the RRBS data set , MACAU detected 1 . 6-fold more age-associated CpG sites than a beta-binomial model ( the next best approach ) . Changes in these sites are consistent with known age-related shifts in DNA methylation levels , and are enriched near genes that are differentially expressed with age in the same population . Taken together , our results indicate that MACAU is an efficient , effective tool for analyzing bisulfite sequencing data , with particular salience to analyses of structured populations . MACAU is freely available at www . xzlab . org/software . html . DNA methylation—the covalent addition of methyl groups to cytosine bases—is a major epigenetic gene regulatory mechanism observed in a wide variety of species . DNA methylation influences genome-wide gene expression patterns , is involved in genomic imprinting and X-inactivation , and functions to suppress the activity of transposable elements [1–3] . In addition , DNA methylation is essential for normal development . For example , mutant Arabidopsis plants with reduced levels of DNA methylation display a range of abnormalities including reduced overall size , altered leaf size and shape , and reduced fertility [4–6] . In humans , DNA methylation levels are strongly linked to disease , including major public health burdens such as diabetes [7 , 8] , Alzheimer’s disease [9 , 10] , and many forms of cancer [7 , 11–15] . Together , these observations point to a central role for DNA methylation in shaping genome architecture , influencing development , and driving trait variation . Consequently , there is substantial interest in identifying the genetic [16–19] and environmental [20–23] factors that shape DNA methylation levels . Progress toward this goal requires statistical approaches that can handle the complexities of real world , population-based datasets . Here , we present one such approach , designed specifically for analyses of differential methylation levels in bisulfite sequencing datasets . High-throughput bisulfite sequencing approaches , which include whole genome bisulfite sequencing ( WGBS or BS-seq ) [24] , reduced representation bisulfite sequencing ( RRBS ) [25 , 26] , and sequence capture followed by bisulfite conversion [27 , 28] , are used to estimate genome-wide DNA methylation levels at base-pair resolution . All such methods rely on the differential sensitivity of methylated versus unmethylated cytosines to the chemical sodium bisulfite . Specifically , sodium bisulfite converts unmethylated cytosines to uracil ( and ultimately thymine following PCR ) , while methylated cytosines are protected from conversion . Estimates of DNA methylation levels for each cytosine base can thus be obtained directly from high-throughput sequencing data by comparing the number of C’s ( reflecting an originally methylated version of the base ) versus T’s ( reflecting an originally unmethylated version of the base ) at that position in the mapped reads . The raw data produced by bisulfite sequencing methods are therefore count data , in which both the number of methylated reads and the total coverage at a site contain useful information . Higher total coverage corresponds to a more reliable estimate of the true DNA methylation level , which , in a typical experiment , can vary dramatically across individuals and sites ( e . g . , by several orders of magnitude: S1 Fig ) . Many commonly used methods for testing for differential methylation ( whether by genotype , environmental predictor , or experimental perturbation ) ignore this variability by converting counts to percentages or proportions ( e . g . , t-tests , Mann-Whitney U tests , linear models , and all tools initially designed for array-based data [29 , 30]; Table 1 ) . Thus , a site at which 5 of 10 reads are designated as methylated ( i . e . , read as a cytosine ) is treated identically to a site at which 50 of 100 reads are designated as methylated . This assumption reduces the power to uncover true predictors of variation in DNA methylation levels , because it treats noisy measurements the same way as accurate ones . To address this problem , several recently introduced methods for differential DNA methylation analysis implement a beta-binomial model ( e . g . , ‘DSS: Dispersion Shrinkage for Sequencing data’ [31] , ‘RADMeth: Regression Analysis of Differential Methylation’ [33] , and ‘MOABS: Model Based Analysis of Bisulfite Sequencing data’ [32] ) . These methods model the binomial nature of bisulfite sequencing data , while taking into account the well-known problem of over-dispersion in sequencing reads . Because these methods work directly on count data , they can reliably account for variation in read coverage across sites and individuals . Consequently , beta-binomial methods consistently provide increased power to detect true associations between genetic or environmental sources of variance and DNA methylation levels [31–33] . However , methods based on beta-binomial models only account for over-dispersion due to independent variation , making them unsuited for data sets containing population structure or related individuals . Accounting for genetic relatedness is important because genetic variation can exert strong and pervasive effects on DNA methylation levels [17 , 19 , 38 , 39] . In humans , methylation levels at more than ten thousand CpG sites are influenced by local genetic variation [18] , and DNA methylation levels in whole blood are 18%-20% heritable on average , with the heritability estimates for the most heritable loci ( top 10% ) averaging around 68% [38 , 39] . As a result , DNA methylation levels will frequently covary with kinship or population structure , and failure to account for this covariance could lead to spurious associations or reduced power to detect true effects . This phenomenon has been extensively documented for genotype-phenotype association studies [35 , 36 , 40–42] , and controlling for genetic covariance between samples is now a basic requirement for genome-wide association studies . Similar logic applies to analyses of gene regulatory phenotypes and studies of gene expression variation often do take genetic structure into account by using mixed model approaches [43–45] . However , despite growing interest in environmental epigenetics and epigenome-wide association studies ( EWAS ) , none of the currently available count-based methods appropriately control for genetic effects on DNA methylation levels in bisulfite sequencing data ( Table 1 ) . Consequently , even though count-based methods have been shown to be more powerful , recent bisulfite sequencing studies have turned to linear mixed models to deal with the confounding effects of population structure [19 , 46] . To address this gap , we present a binomial mixed model ( BMM ) for identifying differentially methylated sites that directly models raw read counts while accounting for both covariance between samples and extra over-dispersion caused by independent noise . We also present an efficient , sampling-based inference algorithm to accompany this model , called MACAU ( Mixed model association for count data via data augmentation ) . MACAU works directly on binomially distributed count data from any high-throughput bisulfite sequencing method ( e . g . , WGBS , RRBS , targeted sequence capture ) and uses random effects to not only model over-dispersion ( as in the standard beta-binomial approach [47] ) , but also to model relatedness/population structure . Hence , MACAU enables users to identify differentially methylated sites in a wide variety of settings , with little cost to power even when genetic effects on DNA methylation levels are negligible . We compared MACAU’s performance with currently available methods under two realistic scenarios , using both real bisulfite sequencing data sets ( WGBS and RRBS ) and simulations parameterized based on properties of real data . In the first scenario , we analyzed publicly available data from Arabidopsis thaliana [48] to show that , when a predictor variable of interest is correlated with population structure , MACAU provides better control of type I error than existing methods . This setting is particularly relevant to understanding geographic variation in DNA methylation levels ( e . g . , [19 , 48–50] ) and for identifying genetic or environmental predictors of DNA methylation in structured samples ( e . g . , [50 , 51] ) . In the second scenario , we used newly generated RRBS data from wild baboons ( Papio cynocephalus ) to demonstrate that MACAU also provides increased power to detect truly differentially methylated sites in the presence of kinship—a condition that often holds in analyses of natural populations ( e . g . , [48 , 52 , 53] ) and in tests for epigenetic discordance between siblings [22 , 53–55] . As interest in epigenome-wide association studies ( EWAS ) , environmental epigenetics , and the epigenetic correlates of disease grows , these types of complex data sets will become increasingly common . Here , we briefly describe the model and the algorithm . Additional information is provided in the S1 Text , which includes details on the model , inference method , and algorithm ( including descriptions of the data augmentation approach and efficient MCMC sampling steps ) . To detect differentially methylated sites , we model each potential target of DNA methylation individually ( i . e . , we model each CpG site one at a time ) as a function of x , a predictor variable of interest . Here , x could be a genotype value , as in methylation QTL mapping analyses; an environmental predictor of interest , such as temperature , chemical exposure , or social environment; an individual characteristic , such as age or sex; or an experimental perturbation , as in a treatment-control design . For each site , we consider the following binomial mixed model ( BMM ) : yi=Bin ( ri , πi ) , ( 1 ) where ri is the total read count for ith individual; yi is the methylated read count for that individual , constrained to be an integer value less than or equal to ri; and πi is an unknown parameter that represents the underlying proportion of methylated reads for the individual at the site . We use a logit link to model πi as a linear function of several parameters: log ( πi1−πi ) =wiTα+xiβ+gi+ei , ( 2 ) g= ( g1 , ⋯ , gn ) T∼MVN ( 0 , σ2h2K ) , ( 3 ) e= ( e1 , ⋯ , en ) T∼MVN ( 0 , σ2 ( 1−h2 ) I ) , ( 4 ) where , for a data set including c covariates and n individuals , wi is a c-vector of covariates including an intercept; α is a c-vector of corresponding coefficients; xi is the predictor of interest for individual i and β is its coefficient; g is an n-vector of genetic random effects that model correlation due to population structure or kinship; MVN denotes the multivariate normal distribution; e is an n-vector of environmental residual errors that model independent variation; K is a known n by n relatedness matrix that can be calculated based on pedigree or genotype data; I is an n by n identity matrix; σ2h2 is the genetic variance component; σ2 ( 1 − h2 ) is the environmental variance component; and h2 is the heritability of the logit transformed methylation proportion ( i . e . logit ( π ) ) . Note that K has been standardized to ensure tr ( K ) /n = 1 , so that h2 lies between 0 and 1 and can be interpreted as heritability ( see [56]; tr denotes the trace norm ) . Both g and e model over-dispersion ( i . e . , the increased variance in the data that is not explained by the binomial model ) . However , they model different aspects of over-dispersion: e models the variation that is due to independent environmental noise ( a known problem in data sets based on sequencing reads [57–60] , including analyses of read proportions [61] ) , while g models the variation that is explained by kinship or population structure . Effectively , our model improves and generalizes the beta-binomial model by introducing this extra g term to model individual relatedness due to population structure or stratification . In the absence of g , our model becomes similar to other beta-binomial models previously developed for modeling count data [31 , 33 , 47 , 62] . We are interested in testing the null hypothesis that the predictor of interest has no effect on DNA methylation levels:H0: β = 0 . This test requires obtaining the maximum likelihood estimate β^ from the model . Unlike its linear counterpart , estimating β^ from the binomial mixed model is notoriously difficult , as the joint likelihood consists of an n-dimensional integral that cannot be solved analytically [63 , 64] . Standard approaches rely on numerical integration [65] or Laplace approximation [66 , 67] , but neither strategy scales well with the increasing dimension of the integral , which in our case is equal to the sample size . Because of this problem , standard implementations of binomial mixed models often produce biased estimates and overly narrow ( i . e . , anti-conservative ) confidence intervals [68–72] . To overcome this problem , we instead use a Markov chain Monte Carlo ( MCMC ) algorithm-based approach for inference , using un-informative priors for the hyper-parameters h2 and σ2 . After drawing accurate posterior samples of β , we rely on the asymptotic normality of both the likelihood and the posterior distributions [73] to obtain the approximate maximum likelihood estimate β^ and its standard error se ( β^ ) . This procedure allows us to construct approximate Wald test statistics and p-values for hypothesis testing ( note that the p-values from our procedure diff from tail posterior probabilities usually used in purely Bayesian methods , and are more akin to p-values from frequentists tests; thus , they are not “improper” . ) Despite the stochastic nature of the procedure , the MCMC errors are small enough to ensure stable p-value computation across multiple MCMC runs ( S2 Fig ) . We note that with reasonably large sample sizes ( n = 50 or more ) , the resulting p-values are also robust to prior perturbation on hyper-parameters ( S3 Fig ) ; however , all results reported here are based on calculations with un-informative priors . In addition to the approximate inference procedure described above , we also developed a novel MCMC algorithm based on an auxiliary variable representation of the binomial distribution for efficient , approximate p-value computation [74–76] ( see S1 Text File Section 2: Inference Method Overview and S1 Text File Section 3 . 1: Data Augmentation for more details ) . We did so to reduce the heavy computational burden of standard MCMC algorithms , which would otherwise be prohibitive in terms of run time for large datasets . Building on the auxiliary variable representation , our main technical contribution is a new framework that approximates the distribution of the auxiliary variables ( S4 Fig and S1 and S2 Tables ) while simultaneously taking advantage of recent innovations for fitting mixed effects models [34 , 35 , 37 , 77] ( see S1 Text File Sections 3 . 2 and 3 . 3 ) . This framework reduces per-MCMC iteration computational complexity from cubic to quadratic with respect to the sample size , and results in an approximate n-fold speed up in practice compared with the popular Bayesian software MCMCglmm [78] , where n is the sample size ( S5 Fig and S3 Table; we note that this speed-up is generalizable to other GLMM problems as well ) . Our implementation of the BMM is therefore efficient for data sets ranging up to hundreds of samples and millions of sites , as computational complexity scales only linearly with respect to the number of analyzed sites ( S5 Fig ) . Because our model effectively includes the beta-binomial model as a special case , we expect it to perform similarly to the beta-binomial model in settings in which population structure is absent ( we say “effectively” because the beta-binomial model uses a beta distribution to model independent noise while we use a log-normal distribution ) . However , we expect our model to outperform the beta binomial in settings in which population structure is present . In addition , in the presence of population stratification , we expect the beta-binomial model to produce inflated test statistics ( thus increasing the false positive rate ) while our model should provide calibrated ones . Below , we test these predictions using two different bisulfite sequencing data sets . We begin with simulations in which the true value of β is known , and the over-dispersion parameter and genetic covariance between samples are motivated by the real data sets . We also motivate our choice of simulated sample sizes based on real bisulfite sequencing data sets , which currently range from ~20–150 samples [19 , 26 , 46 , 53 , 79–82] . However , because sample sizes are only likely to grow in the future , for the data set types of most direct interest ( i . e . , those that contain population structure and heritable DNA methylation levels ) we further consider sample sizes that are much larger than currently represented in the literature ( n = 500 and n = 1000 ) . Finally , we apply our model directly to the real data . We first compared the performance of the BMM implemented in MACAU with the performance of other currently available methods for analyzing bisulfite sequencing data in the absence of genetic effects . Intuitively , we expected MACAU and the beta-binomial model to perform similarly , and we expected both methods to outperform those that first transform the raw count data . To test our prediction , we simulated the effect of a predictor variable on DNA methylation levels across 5000 CpG sites ( 4500 true negatives and 500 true positives ) . Motivated by our analysis of age effects on DNA methylation levels in the baboon RRBS data set ( below ) , we conducted this simulation by sampling from a distribution of known age values from the same baboon population . For all simulations , we set the effect of genetic variation on DNA methylation levels equal to zero , which is equivalent to setting either ( i ) the heritability of DNA methylation levels to zero ( unlikely based on prior findings [38 , 39] ) , or ( ii ) studying completely unrelated individuals in the absence of population structure . To explore MACAU’s performance across a range of conditions , we simulated age effects on DNA methylation levels across three effect sizes ( percent of variance in DNA methylation explained ( PVE ) = 5% , 10% , or 15% ) and three sample sizes ( n = 20 , 50 , and 80 ) . These values capture the majority of effect sizes and sample sizes documented in recent genome-wide bisulfite sequencing studies ( e . g . , [45 , 52 , 53 , 83] ) . Because age is naturally modeled as a continuous variable , we focused our comparisons only on approaches that could accommodate continuous predictor variables ( comparisons in which we artificially binarized age , which allowed us to include a larger set of approaches , are shown in S6 Fig and S7 Fig for cases excluding and including genetic effects on DNA methylation , respectively; however , binarizing a truly continuous variable consistently results in poorer performance: see S6 Fig versus S9 Fig ) . Specifically , in addition to the BMM implemented in MACAU , we considered the performance of a beta-binomial model , a binomial model , a linear model , and a linear mixed model ( implemented in the software GEMMA [34] ) . For the linear and linear mixed model case , methylation proportions were quantile normalized to a standard normal prior to modeling ( see Methods and S8 Fig for parallel results using logit , M-value , and arcsin ( sqrt ) transformations prior to linear mixed modeling as alternatives to quantile normalization ) . As expected , we found that MACAU performed similarly to the beta-binomial model , and that these two approaches consistently detected more true positive age effects on DNA methylation levels ( at a 10% empirical FDR ) than all other methods ( S9 Fig ) . For example , in the “easiest” case we simulated ( PVE = 15% , n = 80 ) , we found that the beta-binomial model detected 30% of simulated true positives , while the BMM implemented in MACAU detected 27 . 8% . The slight loss of power in the BMM is a consequence of the smaller degrees of freedom caused by the additional genetic variance component . In comparison , the linear model detected 21 . 2% of true positives; the linear mixed effects model , 14%; and the binomial model , 8 . 4% ( S9 Fig ) . Although it is often used to test for differential methylation [53 , 84 , 85] , the binomial model exhibits low power when an empirical FDR is used to control for multiple hypothesis testing due to poor type I error calibration , as has been previously reported [33] . Area under a receiver operating characteristic curve ( AUC ) was also consistently very similar between the beta-binomial and MACAU ( S9 Fig ) , although the advantage of the count-based methods was less clear by this measure . This reduced contrast is because AUC is based on true positive-false positive trade-offs across the entire range of p-value thresholds: methods can consequently yield high AUCs even when they harbor little power to detect true positives at FDR thresholds that are frequently used in practice . Taken together , our simulations suggest a general advantage to count-based models for samples that contain no genetic structure . Further , the differences in performance between the beta-binomial model and the BMM implemented in MACAU were consistently small in this setting ( S9 Fig ) . We next evaluated each model’s performance in a more realistic setting , in which genetic covariance between samples could potentially confound tests for environmental or genetic effects on DNA methylation levels . As a case study example , we drew from publicly available phenotype data and SNP genotype data for 24 Arabidopsis thaliana accessions [86 , 87] in which leaf tissue samples had been recently subjected to whole genome bisulfite sequencing [48] . Among these accessions , a secondary dormancy phenotype ( measured as the slope of the relationship between length of cold treatment and seed germination percentages [88] ) is correlated with population structure ( R2 = 0 . 38 against the first principal component of the genotype matrix for these accessions; p = 7 . 84 x 10−4; S10 Fig ) . Because secondary dormancy is associated with environmental conditions that are experienced after the seed has already dispersed , we have no expectation that secondary dormancy should be associated with DNA methylation levels in leaf tissue . Consequently , this data set provided the opportunity to evaluate calibration of Type I error ( false positives ) using MACAU , which controls for population structure , versus other available approaches . To do so , we first used the true distribution of secondary dormancy characteristics and the true genetic structure among these 24 accessions to simulate a dataset that consisted entirely of null associations . Specifically , we simulated data sets ( containing 4000 sites each ) in which the secondary dormancy had no effect on DNA methylation levels , but the effect of genetic variation on DNA methylation levels was either moderate ( h2 = 0 . 3 ) or large ( h2 = 0 . 6 ) . Thus , in these data sets , population structure could confound the relationship between the predictor variable ( the capacity for secondary dormancy ) and DNA methylation levels if not taken into account . As predicted , we found that the BMM implemented in MACAU appropriately controlled for genetic effects on DNA methylation levels: whether DNA methylation levels were moderately ( h2 = 0 . 3 ) or strongly ( h2 = 0 . 6 ) heritable , MACAU did not detect any sites associated with secondary dormancy at a relatively liberal false discovery rate threshold of 20% ( whether calculated against empirical permutations or calculated using the R package qvalue [32] ) . In addition , the p-value distributions for secondary dormancy effects on DNA methylation levels , in both simulations , did not differ from the expected uniform distribution ( Fig 1; Kolmogorov-Smirnov ( KS ) test when h2 = 0 . 3: D = 0 . 015 , p = 0 . 909; when h2 = 0 . 6: D = 0 . 016 , p = 0 . 874; genomic control factors: 0 . 90 when h2 = 0 . 3 , 0 . 93 when h2 = 0 . 6 ) . In contrast , when we analyzed the same simulated data sets with a beta-binomial model , we erroneously detected 2 CpG sites associated with secondary dormancy when heritability was set to 0 . 3 , and 4 CpG sites when heritability was set to 0 . 6 ( at a 20% FDR in both cases ) . More concerningly , the distributions of p-values produced by the beta-binomial model were significantly different from the expected uniform distribution and skewed towards low ( significant ) values ( KS test when h2 = 0 . 3: D = 0 . 084 , p = 1 . 75 x 10−8; when h2 = 0 . 6: D = 0 . 096 , p = 2 . 80 x 10−11; genomic control factors: 1 . 18 when h2 = 0 . 3 , 1 . 32 when h2 = 0 . 6 ) . These results suggest an increasing problem with false positives as the heritability of DNA methylation levels increases ( see S11 Fig for similar results when comparing a linear model to a linear mixed model ) . Notably , this problem should become more acute with increasing sample size , which provides greater power to detect false positives generated by this type of confounding [89] . Indeed , both increasing the simulated sample size and increasing the simulated correlation between the predictor variable and genetic structure produces increasingly poorly calibrated results . For example , when sample sizes were simulated from 25 up to 1000 individuals ( and the heritability of DNA methylation levels was set to 0 . 6 ) , we observed genomic inflation factors ranging from 1 . 03–3 . 49 for data sets analyzed with a beta-binomial ( Fig 2A ) . Not surprisingly , for a dataset of a fixed size , the beta-binomial genomic control factor increased as the confounding between population structure and the predictor variable of interest became more extreme ( see S12A Fig for comparable results for a linear model ) . In contrast , when we analyzed the same simulated datasets with the BMM implemented in MACAU , the genomic control factors consistently ranged from 0 . 82–1 . 08 , even when sample sizes were large and/or the correlation between population structure and the predictor variable was substantial ( Fig 2B; see S12B Fig for comparable results from a linear mixed model ) . Importantly , these differences in genomic control factors can translate into substantial differences in the results suggested by a given method . For example , when n = 1000 and the predictor variable is highly confounded with population structure ( R2 = 0 . 5 ) , a beta-binomial falsely identified 32% of sites in the data set as differentially methylated ( 10% FDR ) , while MACAU correctly identified no differentially methylated sites ( 10% FDR; S13 Fig ) . To investigate the calibration of test statistics in the real data set , we then analyzed the relationship between the secondary dormancy phenotype and WGBS data for the 24 Arabidopsis accessions in which both phenotype and WGBS data were available ( n = 830 , 676 CpG sites tested [32 , 33 , 34] ) . We again compared the performance of a simple linear model , a binomial model , a beta-binomial model , the BMM implemented in MACAU , and an LMM implemented in GEMMA . Further illustrating its poor handling of Type I error , the binomial model detected more than 100 , 000 secondary dormancy-associated sites at a 10% empirical FDR threshold , respectively , with a genomic control factor of 3 . 81 . A beta-binomial model substantially improved over the binomial model , but still detected 39 secondary dormancy-associated sites at a 20% empirical FDR threshold , and 150 sites and 690 sites at a 10% or 20% FDR qvalue threshold , respectively ( genomic control factor = 1 . 16 ) . Given the clear confounding of population structure and secondary dormancy in this sample , as well as the results of our simulations , these associations are probably largely , if not completely , spurious . In contrast , MACAU , the linear mixed model ( GEMMA ) , and the simple linear model did not identify any CpG sites associated with secondary dormancy , either at a 10% or a 20% false discovery rate threshold ( Fig 1 and S11 Fig; genomic control factors: MACAU– 0 . 89 , GEMMA– 0 . 97 , Linear model– 0 . 99 ) . Based on our earlier simulations , the similarity of performance among the three approaches likely stems from different reasons: the linear model is poorly powered to detect positive hits with this sample size ( either true positives or false positives ) ; the linear mixed model controls for population structure but has low power to detect true associations; while MACAU combines both the increased power conferred by modeling the raw count data with appropriate controls for population structure ( see Fig 1 and results below ) . In other data sets , a predictor variable of interest may not be confounded with genetic structure , but modeling genetic similarity between samples could reduce residual error variance and improve power . To investigate this scenario , we focused on the relationship between age and DNA methylation levels in a wild baboon population . Female baboons remain in their natal groups throughout their lives , producing relatedness values that are primarily due to matrilineal descent . The resulting genetic structure is one in which females tend to be more closely related to each other , on average , than males or male-female dyads [90] , but in which not all females are related ( because multiple matrilines co-reside in a single group ) . Data sets drawn from baboon populations therefore include a substantial number of unrelated individuals , but also some dyads that are genetically non-independent ( i . e . , relatives: S14 Fig ) . To test the relative performance of different modeling approaches in this setting , we first simulated moderate to large genetic effects on DNA methylation levels ( h2 = 0 . 3 and 0 . 6 respectively , as in the Arabidopsis simulation above ) and relatedness values based on the observed distribution of relatedness values within baboon social groups ( n = 80 , 500 , or 1000 baboons ) . We again simulated a range of non-zero effect sizes ( percent variance explained by age = 5% , 10% , or 15% ) for 500 true positive sites , and an effect size of zero for 4500 true negative sites . In simulations in which age had a moderate effect on DNA methylation levels ( PVE = 10% ) , MACAU detected 11 . 4% ( when h2 = 0 . 3 ) and 20 . 6% ( when h2 = 0 . 6 ) of simulated true positives at a 10% empirical FDR , and produced well calibrated p-values for sites with no simulated age effect ( S15 Fig ) . In comparison , the beta-binomial model ( the next best model ) detected 8 . 2% and 10 . 4% of true positives , respectively ( Fig 3 ) . As in the simulations , we again observed that a simple binomial model was prone to type I error , which resulted in failure to detect true age-associated sites when empirical FDRs were calculated against permuted data . Our additional simulations at PVE = 5% or PVE = 15% , and n = 500 or n = 1000 , confirmed MACAU’s advantage over other methods across a range of conditions ( S16 and S17 Figs ) . As expected , the magnitude of this advantage was positively correlated with the heritability of DNA methylation levels . Finally , we analyzed the new baboon RRBS data set for differential methylation patterns by age ( n = 50 , age range = 1 . 76–18 . 01 years in our sample , S4 Table ) . Because age-related effects on DNA methylation levels are well described , this approach allowed us to not only evaluate MACAU’s ability to detect differentially methylated sites , but also to identify known age-related signatures in DNA methylation data [38 , 39 , 91–93] . This data set included 433 , 871 CpG sites , enriched for putatively functional regions of the genome ( e . g . , genes , gene promoters , CpG islands , as expected in RRBS data sets [25 , 26]: S18 Fig; see also S19 Fig and S4 Table for additional information on data quality , including bisulfite conversion rates , MspI digest efficiency , correlation with gene expression levels , and methylation level distributions by genomic regions ) . As in our simulations , we found that MACAU provided increased power to detect age effects in the presence of familial relatedness . We detected 1 . 6-fold more age-associated CpG sites at a 10% empirical FDR using MACAU compared to the results of a beta-binomial model , the next best approach ( 1 . 4-fold more sites at a 20% empirical FDR; Fig 4 and S20 Fig ) . This advantage was consistently observed across all FDR thresholds we considered , except for relatively low ( <7 . 5% ) empirical FDR thresholds , when all of the methods were very low powered as a result of the modest sample size . We performed several analyses to investigate the likely validity and functional importance of the age-associated CpG sites we identified . Based on the results of previous studies , we expected that age-associated sites in CpG islands would tend to gain methylation with age [92 , 93] , while sites in other regions of the genome ( e . g . , CpG island shores , gene bodies ) would tend to lose methylation with age [92 , 93] . In addition , we expected that , in whole blood , bivalent/poised promoters should gain DNA methylation with age , while enhancers should lose methylation with age ( as discussed in [91 , 92 , 94] ) . Finally , we expected that stretches of differentially methylated sites ( i . e . , differentially methylated regions , or DMRs ) would tend to occur in or near CpG islands and CpG shores , potentially altering how steeply methylation levels change between islands and their surrounding shelves ( e . g . , [95] ) . Our results conformed to these patterns: sites in CpG islands tended to gain methylation with age ( 71 . 4% of sites were positively correlated with age ) ; and sites in promoters , CpG island shores , and gene bodies tended to lose methylation with age ( 72 . 7% , 75 . 4% , and 75 . 2% of sites were negatively correlated with age , respectively; Fig 4 ) . In addition , we found that positively correlated , age-associated sites were highly enriched in chromatin states associated with bivalent/poised promoters ( as defined by the Roadmap Epigenomics Project [96] ) . Specifically , age-associated CpG sites in bivalent/poised promoters were 3 . 4 times more likely to show increases in DNA methylation with age , compared to age-associated CpG sites in other regions ( p < 10−10 , Fisher’s exact test ) . Negatively correlated age-associated sites ( i . e . , sites where DNA methylation levels decreased with age ) were strongly enriched in enhancers ( defined as sites either marked by H3K4me1 in human PBMCs [97] or sites within chromatin states annotated as ‘enhancers’ by the Roadmap Epigenomics Project [96] , p = 2 x 10−4 , Fisher’s exact test ) . Finally , we detected 142 age-related DMRs , the majority of which were found in CpG islands , shores , and bridging islands and shores ( S21 Fig and S5 Table ) . We also reasoned that true positive age-associated CpG sites should contain information about age-associated gene expression levels . To test this hypothesis , we turned to previously generated whole blood RNA-seq data [43] from the same baboon population ( n = 63; only four baboons in the RNA-seq data set were also included in the DNA methylation data set ) . Overall , we observed a strong enrichment of differentially methylated CpG sites in or near ( within 10 kb ) blood-expressed genes ( n = 12 , 018 genes ) , compared to the background set of all CpG sites near genes ( Fisher’s exact test , p < 10−10 ) . Further , CpG sites near age-associated genes ( n = 1396 genes , 10% FDR ) were 30 . 5% more likely to be differentially methylated with age compared to the background set of all CpG sites near genes ( Fisher’s exact test , p = 0 . 032; Fig 4 ) . Notably , this enrichment was almost always stronger for the set of differentially methylated sites identified by MACAU than for the same number of top sites identified when running the linear model , linear mixed model , binomial , or beta-binomial approaches , across different FDR thresholds ( S22 Fig ) . DNA methylation levels can have potent effects on downstream gene regulation , and , in doing so , can shape key behavioral , physiological , and disease-related phenotypes [7 , 20 , 98–100] . These observations have motivated an increasing number of DNA methylation studies in humans and other organisms , highlighting the need for sophisticated statistical methods that can accommodate the complexities of a broad array of data sets [19 , 46] . Here , we demonstrate that the binomial mixed model implemented in our software MACAU can ( i ) effectively control for confounding relationships between genetic background and a predictor variable of interest and ( ii ) provide increased power to detect true sources of variance in DNA methylation levels in data sets that contain kinship or population structure . In addition , MACAU provides increased flexibility over current count-based methods that cannot accommodate biological replicates ( e . g . , Fisher’s exact test ) , continuous predictor variables ( e . g . , DSS , MOABS , RadMeth ) , or biological or technical covariates ( e . g . , MOABS , DSS; see also Table 1 ) . Given the increasing interest in both the environmental [21 , 101 , 102] and genetic [16 , 17 , 19 , 103] architecture of DNA methylation levels , we believe MACAU will be a useful tool for generalizing epigenomic studies to a larger range of populations . MACAU is particularly well suited to data sets that contain related individuals or population structure; notably , several major population genomic resources contain structure of these kinds ( e . g . , the HapMap population samples [104] , the Human Genome Diversity Panel [105] , and the 1000 Genomes Project in humans [106]; the Hybrid Mouse Diversity Panel in mice [107]; and the 1001 Genomes Project in Arabidopsis [108] ) . Indeed , our results suggest MACAU is a useful tool even in data sets that are less affected by genetic structure , or when the heritability of DNA methylation levels is unclear . Because the beta-binomial model is effectively incorporated as a special case , MACAU exhibits only a slight loss of power relative to a beta-binomial model without genetic random effects when h2 = 0 , while conferring better power and better test statistic calibration when h2 > 0 ( S9 and S16 and S17 Figs and Fig 1 ) . Previous studies in humans have shown that , while the heritability of DNA methylation levels varies across loci , an appreciable proportion of loci are either modestly ( h2 ≥ 0 . 3: 21 . 06% of all CpG sites ) or highly ( h2 ≥ 0 . 6: 6 . 95% of all CpG sites ) heritable [39 , 109] . Further , DNA methylation QTLs are widespread across the genome [18 , 38 , 103] . Thus , because investigators will rarely have a priori knowledge of the heritability of DNA methylation levels at a given locus , and because the advantage of a beta-binomial model is small even when heritability is zero , we recommend applying MACAU in cases in which genetic effects on DNA methylation levels are poorly understood . In addition , our model provides a natural framework for incorporating the spatial dependency of DNA methylation levels across neighboring sites [110 , 111] , which we expect to increase power even further [110 , 111] . However , we do note that , even with the efficient algorithm implemented here , fitting the binomial mixed model ( or its extensions ) remains more computationally expensive than other approaches for moderately sized datasets ( S3 Table ) . While it remains appropriate for the sample sizes used in current studies ( e . g . , dozens to hundreds of individuals ) , or even larger with the support of a moderate-sized computing cluster ( because MACAU is easily parallelizable with respect to sites ) , rapid increases in sample size—especially in the context of EWAS—strongly motivate additional algorithm development to scale up the binomial mixed model for data sets that include thousands or tens of thousands of individuals . This is particularly important given that methods tailored for other types of studies ( e . g . , quantile normalization followed by linear mixed modeling or voom + limma , both commonly used for RNA-seq ) do not appear to translate well to bisulfite sequencing data sets ( S8 Fig; see Methods for additional information on the voom + limma comparison ) . Although we developed MACAU with the analysis of bisulfite sequencing data in mind , we note that a count-based binomial mixed model may be an appropriate tool in other settings as well . For example , allele-specific gene expression ( ASE ) can be measured in RNA-seq data by comparing the number of reads originating from a given variant to the total number of mapped reads for that site [77 , 112–114] . Similarly , alternative isoform usage can be represented as a proportion of reads containing a non-constitutive exon versus the total reads for the same gene [47] . The structure of these data are highly similar to the structure of bisulfite sequencing data , which focus on counts of methylated versus total reads . Unsurprisingly , beta-binomial models have also emerged as one of the most popular methods for estimating both ASE values [114–116] and alternative isoform usage [47] . Researchers interested in the predictors of variation in either of these measures—which could include trans-acting genetic effects , environmental conditions , or properties of the individual ( e . g . , sex or disease status ) —might also benefit from using MACAU . Recent work from the TwinsUK study motivates the need for such a model: Grundberg et al . demonstrated a strong heritable component to ASE levels [117] , which could be effectively taken into account using the random effects approach implemented here . Finally , linear mixed models have been recently proposed to account for cell type heterogeneity in epigenome-wide association studies focused on array data [118] . In this framework , the random effect covariance structure is based on overall covariance in DNA methylation levels between samples , which is assumed to be largely attributable to variation in tissue composition . MACAU provides a potential avenue for extending these ideas to sequencing-based data sets . We downloaded publicly available WGBS data generated by Schmitz et al . [48] , as well as previously published SNP genotype data [87] and secondary dormancy data [86] for 24 Arabidopsis accessions . We used the SNP genotype data ( specifically , 188 , 093 sites with minor allele frequency >5% ) to construct a pairwise genetic relatedness matrix , K , as the product of a standardized genotype matrix X , or K = XXT/p [56] , where genotypes were expressed as 0 , 1 , or 2 depending on the number of reference alleles for that site-sample combination . We used this estimate of K for both the simulations and our analyses of the real WGBS data . In these analyses , we focused on CpG sites measured in ≥50% of accessions , and excluded sites that were constitutively hypermethylated ( average DNA methylation level >0 . 90 ) or hypomethylated ( average DNA methylation level <0 . 10 , following [101 , 118] ) . We also excluded highly invariable sites ( i . e . , sites where the standard deviation of DNA methylation levels fell in the lowest 5% of the overall data set ) and sites with very low coverage ( i . e . , sites where the mean coverage fell in the lowest quartile for the overall data set , below a mean of 3 . 34 reads ) . After filtering , the final data set consisted of 830 , 676 sites . For the analysis of test statistic calibration as a function of sample size ( Fig 2 ) , we also used Arabidopsis data , but simulated the phenotype data as a function of genetic covariance between the accessions . Genotype data were obtained from [87] . To simulate the methylated read counts and total read counts that result from WGBS and RRBS , we performed the following procedure: First , we simulated the proportion of methylated reads for each site . To do so , we drew secondary dormancy values or age values , x , as the predictor of interest , from the actual values for the Arabidopsis accessions or from the baboon population , respectively . For simulations that focused on Arabidopsis data sets of various sizes ( e . g . , Fig 2 ) , we simulated x and varied the degree to which it was confounded with population structure . Specifically , for each dataset ( ranging from n = 25 to n = 1000 accessions ) we performed principal components analysis on the SNP genotype data , and extracted the first principal component to capture the major axis of population structure ( PC1 ) . We then added environmental noise from a zero-centered normal distribution to achieve a correlation ( R2 ) between the simulated phenotype and PC1 that reached the desired value ( ranging from R2 = 0 . 1 to 0 . 5 ) . For each simulated data set , we simulated the DNA methylation level at each CpG site , π , as a linear function of x and its effect size , β . In addition , we included the effects of genetic variation ( g ) and random environmental variation ( e ) , passed through a logit link ( based on the model described in the Results section ) . For the baboon RRBS and the Arabidopsis WGBS simulations , we determined K from 14 highly variable microsatellite loci or from the publicly available SNP data , as described above . For each simulation , we set h2 to 0 , 0 . 3 , or 0 . 6 to simulate non-heritable , modestly heritable , or highly heritable DNA methylation levels . We also estimated the variance term σ2 from the real data sets . Specifically , we took the mean estimate of σ2 across all sites ( calculated in MACAU ) for each real data set , and used this value as the fixed value of σ2 in the corresponding simulations . Next , for each site , we simulated total read counts ri for each individual i from a negative binomial distribution that models the extra variation observed in the real data: ri∼NB ( t , p ) , ( 5 ) where t and p are site specific parameters estimated from the real data . Specifically , we generated 10 , 000 sets of t and p parameters by fitting a negative binomial distribution to the total read count data from 10 , 000 randomly selected CpG sites in the real baboon RRBS data set or the real Arabidopsis data set , using the function ‘fitdistr’ in the R package MASS [129] . To simulate counts for a given CpG site , we randomly selected one of these parameter sets to produce the total number of reads . Finally , we simulated the number of methylated reads for each individual at that locus ( y ) by drawing from a binomial distribution parameterized by the number of total reads ( r ) and the DNA methylation level ( π ) . For all simulated and real data sets , we used raw methylated and total read counts to compare the results of a beta-binomial model ( using a custom R script ) , a binomial model ( implemented via ‘glm’ in R ) , and the binomial mixed model implemented in MACAU . For computation time comparison , we used the MCMCglmm software , which also provides an implementation of a binomial mixed model [78] . In addition , we used the same count data to run a Fisher’s exact test ( implemented in R ) , DSS [31] , and RadMeth [33] in the subset of analyses that utilized these programs . To analyze simulated and real data sets using a linear model ( implemented using ‘lm’ in R ) or the linear mixed model implemented in GEMMA [34] , we estimated DNA methylation levels by dividing the number of methylated reads by the total read count for each individual and CpG site . We then quantile normalized the resulting proportions for each CpG site to a standard normal distribution , and imputed any missing data using the K-nearest neighbors algorithm in the R package impute [130] . In addition to the quantile normalization approach , we also evaluated three other methods for transforming methylation proportions: a logit transformation , following [110]; the “M” value transformation ( log2 ( ( methylated counts + α ) / ( unmethylated counts + α ) ) , where α = 0 . 01 , following [30]; and an arcsin square root transformation , following [131] . All four approaches produced qualitatively identical results ( S8 Fig ) , so we elected to concentrate on the results from quantile normalization in the main text . Finally , we also tested the performance of a powerful , commonly used method for modeling RNA-seq data: the combination of the voom function for data weighting with limma , a linear model approach [132] . Our results indicated that voom + limma performs more poorly than even a simple linear model ( S24 Fig ) , probably because read depth variation is much more complicated in bisulfite sequencing studies than in RNA-seq studies ( S1 Fig ) . Because voom + limma also cannot account for population structure , we report these results in the SI but focus on results from the simple linear model in the main text . To compute empirical false discovery rates in simulated data , we divided the number of false positives detected at a given p-value threshold by the total number of sites called by the model as significant at that threshold ( i . e . , the sum of false positives and true positives ) . To compute empirical false discovery rates in the real data , in which the false positives and true positives were unknown , we used permutations . Specifically , we permuted the predictor variable for each data set four times , reran our analyses , and then calculated the false discovery rate as the average number of sites detected at a given p-value threshold in the permuted data divided by the total number of sites detected at that threshold in the real data . For simulated data sets only , we also calculated the area under the receiver operating characteristic curve ( AUC ) to produce a measure of the overall tradeoff between detecting true positives and calling false positives . Our initial analyses of the baboon RRBS dataset focused only on the relative ability of each method to detect age-associated sites . For these analyses , we therefore did not control for other biological covariates that may contribute to variance in DNA methylation levels ( note that biological covariates cannot be incorporated into several implementations of the beta-binomial model [31 , 32]: see Table 1 ) . However , to investigate patterns of age-related changes in DNA methylation levels , and to compare them to previously described patterns in the literature , we wished to control for such covariates . To do so , we reran the differential methylation analysis in MACAU , this time controlling for sex , sample age , and efficiency of the bisulfite conversion rate estimated from the lambda phage spike-in . First , we investigated whether age-associated sites were enriched in functionally coherent regions of the genome , many of which have previously been identified as age-related [38 , 92 , 93] . To do so , we defined gene bodies as the regions between the 5’-most transcription start site ( TSS ) and 3’-most transcription end site ( TES ) of each gene using Panu 2 . 0 annotations from Ensembl [133] . We defined promoter regions as the 2 kb upstream of the TSS . CpG were annotated based on the UCSC Genome Browser track for baboon [134] , with CpG island shores defined as the 2 kb regions flanking either side of the CpG island boundary ( following [26 , 135 , 136] ) . Finally , because no enhancer annotations are available that are specific to baboons , we used H3K4me1 ChIP-seq data generated by ENCODE ( from human peripheral blood mononuclear cells ) to define enhancer regions [97] . In addition , we used chromatin state annotations from the Roadmap Epigenomics Project ( also generated from human peripheral blood mononuclear cells ) to further investigate biases in the locations of age-associated sites [96] . Using these annotation sets , we performed Fisher’s Exact Tests to ask whether age-associated sites were enriched or underrepresented in specific genomic regions . To identify differentially methylated regions ( DMRs ) , we used the criteria proposed by [137] . Specifically , DMRs contained at least 3 differentially methylated sites with an inter-CpG distance ≤1 kb , with only 3 non-differentially methylated sites permitted in the DMR as a whole . Second , we asked whether differentially methylated sites were more likely to fall close to blood-expressed genes . For this comparison , we drew on previously published RNA-seq data , generated from whole blood samples collected in the Amboseli baboon population [43] . We defined blood-expressed genes as those genes that had non-zero counts in more than 10% of individuals in the RNA-seq data sets , and that had mean read counts greater than or equal to 10 . We then compared the number of differentially methylated CpG sites near blood-expressed genes ( i . e . , within the gene body or within 10 kb of the gene TSS or TES ) to the number of differentially methylated CpG sites near genes that were not expressed in blood , using a Fisher’s Exact Test . Finally , we investigated whether CpG sites that occur near genes that are differentially expressed with age were also more likely to be differentially methylated with age . For this comparison , we defined ‘age-associated genes’ as genes differentially expressed with age ( at a 10% FDR ) in the RNA-seq data set [43] . We compared the number of differentially methylated CpG sites near blood-expressed , age-associated genes to the number of differentially methylated CpG sites near genes that were not within this set of genes , again using a Fisher’s Exact Test . The baboon data used in this study was generated from samples collected from wild baboons living in the Amboseli ecosystem of southern Kenya . This research is conducted under the authority of the Kenya Wildlife Service ( KWS ) , the Kenyan governmental body that oversees wildlife ( permit number NCST/RCD/12B/012/57 to Jenny Tung ) . As the animals are members of a wild population , KWS requires that we do not interfere with injuries to study subjects inflicted by predators , conspecifics , or through other naturally occurring events . Permission to perform temporary immobilizations ( for blood sample collection ) was granted by KWS; further , these immobilizations were supervised by a KWS-approved Kenyan veterinarian , who monitored anesthetized animals for hypothermia , hyperthermia , and trauma ( no such events occurred during our sample collection efforts ) . Observational and sample collection protocols were approved though IACUC committees at Duke University ( current protocol is A020-15-01 to Jenny Tung and Susan C . Alberts ) . The MACAU software and a custom script for implementing a beta-binomial model in R is available at: www . xzlab . org/software . html . Previously published data sets are available at http://bergelson . uchicago . edu/regmap-data/regmap . html/ ( Arabidopsis SNP genotype data ) ; http://www . ncbi . nlm . nih . gov/geo/ ( Arabidopsis WGBS data: GSE43857 ) ; http://www . nature . com/nature/journal/v465/n7298/full/nature08800 . html#supplementary-information ( Arabidopsis phenotype data ) ; and http://www . ncbi . nlm . nih . gov/sra ( Baboon RNA-seq data: GSE63788 ) . Baboon RRBS data generated in this study are deposited in NCBI ( project accession SRP058411 ) .
DNA methylation is an important epigenetic modification involved in regulating gene expression . It can be measured at base-pair resolution , on a genome-wide scale , by coupling sodium bisulfite conversion with high-throughput sequencing ( a technique known as ‘bisulfite sequencing’ ) . However , the data generated by such methods present several challenges for statistical analysis . In particular , while the raw data generated from bisulfite sequencing experiments are read counts , they are often converted to proportions for ease of modeling , resulting in loss of information . Furthermore , although DNA methylation levels are known to be heritable—and are thus affected by kinship and population structure—existing approaches for modeling bisulfite sequencing data fail to account for this covariance . Such failure can lead to spurious associations and reduced power . Here , we present a new approach that models bisulfite sequencing data using raw read counts , while also taking into account population structure and other sources of data over-dispersion . Using simulations and two real data sets ( publicly available data from Arabidopsis thaliana and newly generated data from Papio cynocephalus ) , we demonstrate that our model provides well-calibrated p-values and improves power compared with previous methods . In addition , the DNA methylation patterns identified by our method agree with those reported in previous studies .
You are an expert at summarizing long articles. Proceed to summarize the following text: Approximately 14 million persons living in areas endemic for lymphatic filariasis have lymphedema of the leg . Clinical studies indicate that repeated episodes of bacterial acute dermatolymphangioadenitis ( ADLA ) lead to progression of lymphedema and that basic lymphedema management , which emphasizes hygiene , skin care , exercise , and leg elevation , can reduce ADLA frequency . However , few studies have prospectively evaluated the effectiveness of basic lymphedema management or assessed the role of compressive bandaging for lymphedema in resource-poor settings . Between 1995 and 1998 , we prospectively monitored ADLA incidence and leg volume in 175 persons with lymphedema of the leg who enrolled in a lymphedema clinic in Leogane , Haiti , an area endemic for Wuchereria bancrofti . During the first phase of the study , when a major focus of the program was to reduce leg volume using compression bandages , ADLA incidence was 1 . 56 episodes per person-year . After March 1997 , when hygiene and skin care were systematically emphasized and bandaging discouraged , ADLA incidence decreased to 0 . 48 episodes per person-year ( P<0 . 0001 ) . ADLA incidence was significantly associated with leg volume , stage of lymphedema , illiteracy , and use of compression bandages . Leg volume decreased in 78% of patients; over the entire study period , this reduction was statistically significant only for legs with stage 2 lymphedema ( P = 0 . 01 ) . Basic lymphedema management , which emphasized hygiene and self-care , was associated with a 69% reduction in ADLA incidence . Use of compression bandages in this setting was associated with an increased risk of ADLA . Basic lymphedema management is feasible and effective in resource-limited areas that are endemic for lymphatic filariasis . Lymphedema of the leg and its advanced form , known as elephantiasis , are major causes of disability and morbidity in filariasis-endemic areas , with an estimated 14 million cases worldwide [1] . When the World Health Organization's Global Program to Eliminate Lymphatic Filariasis ( GPELF ) was launched in 1998 , its stated goals included not only interrupting transmission of the parasite , but also providing care to persons who suffer from clinical disease [2] , [3] . During the 1990s , several studies in filariasis-endemic areas highlighted the importance of repeated episodes of acute bacterial dermatolymphangioadenitis ( ADLA ) in the progression of lymphedema severity [4]–[8] . These inflammatory episodes , characterized by intense pain , swelling , fever , and chills , accelerate damage to the peripheral lymphatic channels in the skin , which leads to worsened lymphatic dysfunction , fibrosis , and increased risk of further ADLA episodes . Clinical studies suggest that basic lymphedema management – including hygiene , skin care , elevation of the limb , and range-of-motion exercises – can halt , or perhaps even partially reverse , this progression [9]–[14] . The current prospective study was done to test , under field conditions , the feasibility and effectiveness of basic lymphedema management as a public health intervention in a resource-poor setting . Leogane , Haiti , located approximately 30 km west of Port au Prince , has long been endemic for lymphatic filariasis; the prevalence of Wuchereria bancrofti microfilaremia was 16% in 2000 [15] . The outpatient clinic at Ste . Croix Hospital , the major health facility for Leogane Commune , was the site of this study . Lymphedema of the leg , which disproportionately affects women [16] , is a major public health problem in Leogane [17] . As in many other areas where bancroftian filariasis is endemic , few persons with lymphedema of the leg remain infected with the parasite [18] . In June 1995 , the physical therapist at Ste . Croix ( JLC ) recruited a group of 30 patients with lymphedema of the leg to participate in a pilot project of basic lymphedema management . The goals of this pilot were to assess the feasibility and acceptance of basic lymphedema management in a small group of patients and to test the feasibility of volumetric measurement in this setting . During this pilot phase , leg volume sometimes was measured twice a day to assess diurnal variation ( data not shown ) . The pilot was intended to last two months , but it continued with these 30 patients until the spring of 1996 , when increased funding allowed the project to scale up . For the purposes of analysis , the pilot study is considered as part of Phase I . With the project expansion in March 1996 , eight additional staff , including a nurse , a community health worker , and others with high school-level education but no health background were trained as lymphedema technicians . A hospital physician ( FL ) provided consultation and medical care as needed . During Phase I , an attempt was made to compare the effectiveness of basic lymphedema management with a more intensive method that included compressive bandaging , a component of “complex decongestive physiotherapy” [20] . Two to four weeks of compressive bandaging ( using Comprilan® ) resulted in rapid volume reduction , which was maintained with a locally designed and produced compressive garment made of Velcro® . However , because it resulted in rapid volume reduction , the popularity of compressive bandaging made it impossible to randomize patients to the non-bandage intervention group . In March 1997 , Dr . Gerusa Dreyer , who had developed a basic lymphedema management program in Recife , Brazil , visited Leogane to review the project and to conduct training for the staff . As a result of that visit , the program shifted its primary focus from reducing leg volume to preventing ADLA through hygiene and skin care . Use of compressive bandaging and garments was no longer encouraged and their use declined dramatically . In November 1997 , a colorful booklet with the messages of basic lymphedema management was given to each patient , and a “soap opera” was broadcast over local radio that depicted the life of a young woman with lymphedema and how she benefited from lymphedema self-care . The analysis was limited to patients who returned to the clinic for at least 5 routinely scheduled visits over a period of least 6 months . We considered shorter periods of observation inadequate to evaluate the effect of the intervention . ADLA incidence was calculated as episodes per person-year of observation . Changes in leg volume were calculated by subtracting last-measured volume from initial volume during a specified time period . The pooled t-test was used to compare differences in age by study phase; the paired t-test to compare changes in leg volumes; the chi-square or Fisher's Exact test to compare proportions associated with gender and literacy; and Poisson regression to calculate and compare ADLA incidence rates and to identify factors associated with ADLA episodes . The effect of factors associated with leg volume was investigated using multivariate regression . All regressions were run using SAS proc genmod , using the generalized estimating equations ( GEE ) procedure to adjust for correlation of multiple observations ( e . g . , ADLA episodes in different legs ) from the same individual over time [21] . Analyses were performed using SAS version 9 . 2 , SAS Institute Inc , Cary , NC , USA . Statistical significance was set at alpha = 0 . 05 . During the 12 months before entering the program , the 175 patients reported a mean of 2 . 1 episodes of ADLA ( range 0–13 ) , with a mean duration of 2 . 6 days each ( range <1 day–44 days ) . Reported ADLA incidence by 12-month recall was not associated with patient age or sex but was significantly associated with illiteracy ( RR 1 . 6 , P = 0 . 01 ) , bilateral lymphedema ( RR 1 . 6 , P = 0 . 02 ) and greater lymphedema stage ( 0 . 67 , 1 . 72 , 2 . 32 , and 3 . 08 episodes per year for persons with stages 1 through 4 , respectively , P = 0 . 01 ) . During the study , a total of 242 ADLA episodes were reported , for an overall incidence of 0 . 75 episodes per person-year , with a range of 0 to 10 per patient . Of these episodes , 141 ( 58% ) were witnessed by clinic staff . Mean reported duration of each ADLA episode was 3 . 9 days ( range 1–22 ) . Eighty-seven patients ( 49 . 7% ) had no ADLA during the study , 67 ( 38 . 3% ) had an annual incidence of <2 . 0 per person-year , and 21 ( 12 . 0% ) experienced two or more episodes per year . In univariate analysis , ADLA incidence during the study was positively associated with lymphedema stage and leg volume , illiteracy , use of compression bandages or garments , and reported frequency of ADLA before entering the study ( Table 2 ) . No consistent seasonal pattern was observed for ADLA incidence , although it tended to increase between the third and fourth quarters of the year; this increase was statistically significant ( P = 0 . 006 ) only for 1997 . Because the emphasis of the program shifted from leg volume reduction during Phase I to ADLA prevention through self-care during Phase II , we compared ADLA incidence for these two periods ( Figure 1 ) . In Phase I , the incidence of ADLA was 1 . 56 episodes per person-year , compared to 0 . 48 overall in Phase II ( P<0 . 0001 ) . A significant reduction was observed even when the analysis was restricted to those who entered the study during Phase I . Among these 127 persons , ADLA incidence decreased to 0 . 54 per person-year during Phase II ( P<0 . 0001 ) . ADLA incidence during Phase II was even lower for the 48 persons who entered the study during Phase II , 0 . 22 episodes per person-year ( P<0 . 0001 ) ( Table 3 ) . A multivariate Poisson regression analysis considered the effect of treatment compliance , illiteracy , gender , and use of compression bandages on ADLA incidence among the 127 persons who entered the study during Phase I , when compressive bandaging use was most common . Only illiteracy and use of compression bandages or garments were significantly associated with ADLA incidence . A significant ( P = 0 . 02 ) interactive effect of bandaging and illiteracy was observed . Among illiterate persons , mean ADLA incidence did not change with or without use of compression bandages ( 1 . 1 episodes per year vs . 1 . 0 episode per year , respectively ) . However , among literate persons , those who ever used compression bandages had an overall ADLA incidence of 0 . 76 per year , compared to 0 . 20 per year for those who never used compression bandages or garments ( P = 0 . 005 ) . Of 175 patients , 137 ( 78 . 3% ) experienced some reduction in leg volume during the study; volume decreased in 66 . 4% of lymphedematous legs ( median reduction , 90 ml ) . In general , volume decreased dramatically following application of compressive bandages and increased rapidly during ADLA . Compared to leg volume on entering the study , mean leg volume at the end of the study was 14 mL greater in normal legs and 5 mL greater in legs with stage 1 lymphedema . Mean decreases of 59 mL , 93 mL , and 571 mL were observed for lymphedema stages 2 , 3 , and 4 , respectively , statistically significant over the entire study period only for stage 2 ( P = 0 . 01 ) ( Table 4 ) . Controlling for study phase , volume reduction in all 175 patients was significantly associated with increasing lymphedema stage ( P = 0 . 005 ) , lower frequency of ADLA during the study period ( P = 0 . 016 ) , and use of compressive bandages ( a mean decrease of 86 . 6 mL , compared to 8 . 2 mL in lymphedematous legs in which no compression was used , P = 0 . 039 ) , but not with age or sex . For the purposes of data analysis , compliance with the four recommended practices of leg washing , exercises , leg elevation , and sleeping with the foot of the bed raised was defined as having reported , on at least 75% of all clinic visits , that the behaviors had been practiced every day since the previous clinic visit . Using this definition , compliance rates were 88 . 0% , 38 . 3% , 69 . 7% , and 49 . 7% for these four practices , respectively . Significant increases in daily compliance between Phase I and Phase II were observed for leg washing ( from 61 . 7% to 94 . 3% , P<0 . 001 ) , leg elevation ( 59 . 2% to 68 . 6% , P = 0 . 01 ) , and sleeping with the foot of the bed elevated ( 40 . 8% to 51 . 4% , P<0 . 001 ) . Overall compliance was calculated by summing the percentage figures for all four recommended practices; a score of ≥300 ( of a possible 400 ) was considered compliant . Overall compliance increased from 45 . 8% to 66 . 3% between Phase I and Phase II ( P = 0 . 003 ) . In a multivariate Poisson regression analysis , compliance was significantly associated with female gender ( OR 1 . 6 , 95% Confidence Interval [CI] 1 . 0–2 . 5 , P = 0 . 05 ) and age >40 years ( OR 1 . 5 , 95% CI 1 . 1–2 . 0 , P = 0 . 02 ) , but not with literacy or lymphedema stage . This prospective study provides evidence that basic lymphedema management is feasible and acceptable to patients in resource-poor areas where lymphatic filariasis is endemic . In general , patients were able to incorporate basic self-care measures , especially leg washing , into their daily routines . When proper hygiene and skin care were emphasized , the incidence of ADLA decreased rapidly to 31% of earlier levels and these reductions were sustained over time . A follow-up study by Dahl and colleagues in 2000–2001 showed that ADLA incidence in these patients remained low and even decreased further ( Dahl , BA , unpublished thesis , Emory University , Atlanta , Georgia ) . Recent studies indicate that , in addition to being a critical risk factor for progression of lymphedema [4]–[8] , [13] , [14] , ADLA episodes are strongly associated with poor quality of life [10] , [22]–[28] . Thus , reducing ADLA frequency is arguably the most important objective of lymphedema management in resource-poor countries . Accordingly , ADLA has emerged as a key indicator for monitoring lymphedema management programs in filariasis-endemic areas [29] . In contrast , the usefulness of leg volume as an indicator of clinical improvement in filariasis-endemic areas seems limited . Leg volume , albeit an “objective” measure , varied widely with use of compression garments and , to an extent , with time of day ( data not shown ) . Overall , leg volume decreased in 78% of our patients , but these reductions were generally modest , and statistically significant only for stage 2 lymphedema . Our initial focus on reducing leg volume , although popular with patients , distracted attention from hygiene and skin care; as a result , no significant reduction in ADLA incidence was observed during Phase I of the study . Compression bandages reduced leg volume quickly , but their use was associated with an increase in ADLA incidence . It was difficult to keep the compression bandages clean , and they were prohibitively expensive – equivalent to the annual income of many patients . Frequent visits to the clinic for reapplication of compression bandages in the initial stages of treatment also fostered dependence on clinic staff and undermined key messages of self-reliance and self-care . Thus , although reduction in leg volume is a desirable outcome , our experience suggests that it should not be the primary goal of lymphedema treatment in resource-poor settings . After receiving training from Dr . Dreyer midway through the study , the clinic staff was better equipped to deliver simple , clear , assertive messages to patients regarding hygiene and skin care . These messages were later reinforced by a simple booklet that was given to each patient , opportunities to participate in support groups , and a radio drama about a young woman with lymphedema that was aired repeatedly on local radio . With these interventions , ADLA incidence dropped dramatically and remained low . The relative contribution of each component of the overall intervention is unclear; we believe that they acted synergistically . Coreil and colleagues showed that patients in Leogane who regularly participated in support groups had a lower incidence of ADLA than those who did not [30] . In addition , self-efficacy ( belief in one's ability to perform lymphedema self-care ) increased significantly following distribution of booklets and broadcasts of the radio dramas ( Wendt , JM , unpublished thesis , Emory University , Atlanta , GA ) . The number and duration of patient contacts required for patients to understand , become proficient in , and fully committed to life-long lymphedema self-care is not clear . In practice , the intensity of patient training and follow-up varies among filariasis programs [29] . Our results indicate that patients who were illiterate and those with advanced disease continued to be at risk of ADLA episodes . These results suggest that more intensive effort may be needed for such patients , such as home visits and support groups . The fact that illiterate persons remained at a 2-fold risk of ADLA throughout the study , even when controlling for other factors , also suggests that illiteracy may be a marker for other factors related to ADLA incidence , such as lower self-efficacy or increased risk of skin lesions that predispose to ADLA [31] , [32] . In addition to reduced ADLA incidence , we observed other improvements in quality of life among patients in this study . These were often dramatic . Patients commonly reported decreased stigma and shame and a return to active life , including school or work [30 , Wendt JM , unpublished] . Those with advanced lymphedema and multiple skin folds and lesions reported , often for the first time in years , the elimination of offensive odor . Serial biopsies in a subset of patients confirmed a decrease in chronic inflammation in lymphedematous legs [33] . Despite these remarkable changes at the tissue level , most patients did not experience reduction in lymphedema stage . The 4-stage classification that we used is not particularly sensitive to localized changes , and considerable heterogeneity can be found within each stage . Most of the lymphedema classification and staging systems that have been proposed suffer from these limitations [34] . Studies have reported that the incidence of adenolymphangitis ( presumably ADLA or ADLA-like ) may decrease following mass treatment with antifilarial drugs [35]–[38] . The current study was completed almost two years before mass treatment was initiated in Leogane [15]; diethylcarbamazine was not readily available . Therefore , it seems unlikely that the clinical improvement and decreased ADLA frequency that we observed can be attributed to reduced transmission of W . bancrofti . Seasonal fluctuations in ADLA , associated with rainfall , have been reported in other studies [39] . We observed no consistent seasonal trends in ADLA incidence . The tendency for ADLA incidence to increase between the third and fourth quarters was statistically significant only for 1997 . The reason for this is not clear . Rainfall patterns varied from year to year in Haiti during the study period , and the fall of 1997 was relatively dry . Prospective ADLA monitoring , as was done in this study , is accurate , but expensive . The accuracy of patient recall over longer periods is unknown , but the pain and suffering associated with ADLA make it a memorable event . Among the 127 patients who entered the study during Phase I , the observed incidence of ADLA during Phase I was 1 . 6 episodes per person-year , compared to 2 . 4 self-reported for the 12 months before entering the study . It is risky to compare retrospective and prospective data , and it also is possible that messages regarding hygiene and skin care during Phase I , albeit sub-optimal , might have reduced ADLA incidence from baseline levels . However , the data suggest that 12-month recall may be adequate for rapid epidemiologic assessments or program monitoring . Additional research is warranted to assess reliability of 12-month ADLA recall in different filariasis-endemic areas . The preponderance of women in our study sample reflects the gender distribution among persons with lymphedema in the Leogane population [16] . In many areas where bancroftian filariasis is endemic , lymphedema of the leg is more common in women than in men , although this finding is not universal [38] . This study has several limitations . First , both the incidence of ADLA and compliance with lymphedema self-care were assessed by self-report – a method that , typically , is less than completely reliable . Recall of ADLA episodes during the year before entering the study was facilitated by careful questioning and using memorable public events to help “frame” the previous 12 months . During the study , patients were encouraged to report ADLA and compliance accurately and honestly , and they were assured that clinical care would continue , free of charge , regardless of what they reported . Some 58% of ADLA episodes reported by patients were observed and confirmed by research staff at the clinic . In many of the remaining cases , patients were seen within 1–2 weeks after the episode , when clinical signs ( such as peeling of the skin , warmth , or edema [19] ) were still visible . Skin condition and interdigital lesions were carefully assessed during routine clinic visits , and they provided a check on self-reported compliance . Ad-hoc home visits also encouraged accurate self-reporting . For example , the absence of soap , a clean towel , or an appropriate washing basin would call into question high levels of self-reported compliance . Second , because of ethical concerns about withholding treatment , we did not collect comparative prospective data on a control group of patients . Further , although we desired to randomize patients with regard to compressive bandaging , we were unable to do so . However , we were able to compare ADLA incidence as a function of the focus of our programmatic intervention ( i . e . , leg volume during Phase I and ADLA incidence during Phase II ) . Third , because of the conditions under which this study was conducted , it cannot be regarded as a study of the efficacy of lymphedema management . We had limited on-site access to specialists in lymphedema management , our staff was comprised mostly of motivated young people rather than health professionals , and the intervention evolved as we gained experience . It is likely that even greater reductions in ADLA frequency would be observed under more optimal conditions . Despite these limitations , our results suggest that basic lymphedema management is both feasible and effective in filariasis-endemic areas where resources are limited . When provided with the skills and motivation to practice lymphedema self-care , most patients will do so . However , simple messages that unequivocally emphasize the importance of hygiene and skin care are important . Our experience also suggests that compressive bandages , although useful for individual patients , should not be the mainstay of treatment in filariasis-endemic areas , and that volume reduction may not be the optimal measure of success .
Lymphatic filariasis is a parasitic disease that is spread by mosquitoes . In tropical countries where lymphatic filariasis occurs , approximately 14 million people suffer from chronic swelling of the leg , known as lymphedema . Repeated episodes of bacterial skin infection ( acute attacks ) cause lymphedema to progress to its disfiguring form , elephantiasis . To help achieve the goal of eliminating lymphatic filariasis globally , the World Health Organization recommends basic lymphedema management , which emphasizes hygiene , skin care , exercise , and leg elevation . Its effectiveness in reducing acute attack frequency , as well as the role of compressive bandaging , have not been adequately evaluated in filariasis-endemic areas . Between 1995 and 1998 , we studied 175 people with lymphedema of the leg in Leogane , Haiti . During Phase I of the study , when compression bandaging was used to reduce leg volume , the average acute attack rate was 1 . 56 episodes per year; it was greater in people who were illiterate and those who used compression bandages . After March 1997 , when hygiene and skin care were emphasized and bandaging discouraged , acute attack frequency significantly decreased to 0 . 48 episodes per year . This study highlights the effectiveness of hygiene and skin care , as well as limitations of compressive bandaging , in managing lymphedema in filariasis-endemic areas .
You are an expert at summarizing long articles. Proceed to summarize the following text: In metazoan integrin signaling is an important process of mediating extracellular and intracellular communication processes . This can be achieved by cooperation of integrins with growth factor receptors ( GFRs ) . Schistosoma mansoni is a helminth parasite inducing schistosomiasis , an infectious disease of worldwide significance for humans and animals . First studies on schistosome integrins revealed their role in reproductive processes , being involved in spermatogenesis and oogenesis . With respect to the roles of eggs for maintaining the parasite´s life cycle and for inducing the pathology of schistosomiasis , elucidating reproductive processes is of high importance . Here we studied the interaction of the integrin receptor Smβ-Int1 with the venus kinase receptor SmVKR1 in S . mansoni . To this end we cloned and characterized SmILK , SmPINCH , and SmNck2 , three putative bridging molecules for their role in mediating Smβ-Int1/SmVKR1 cooperation . Phylogenetic analyses showed that these molecules form clusters that are specific for parasitic platyhelminths as it was shown for integrins before . Transcripts of all genes colocalized in the ovary . In Xenopus oocytes germinal vesicle breakdown ( GVBD ) was only induced if all members were simultaneously expressed . Coimmunoprecipitation results suggest that a Smβ-Int1-SmILK-SmPINCH-SmNck2-SmVKR1 complex can be formed leading to the phosphorylation and activation of SmVKR1 . These results indicate that SmVKR1 can be activated in a ligand-independent manner by receptor-complex interaction . RNAi and inhibitor studies to knock-down SmILK as a representative complex member concurrently revealed effects on the extracellular matrix surrounding the ovary and oocyte localization within the ovary , oocyte survival , and egg production . By TUNEL assays , confocal laser scanning microscopy ( CLSM ) , Caspase-3 assay , and transcript profiling of the pro-apoptotic BCL-2 family members BAK/BAX we obtained first evidence for roles of this signaling complex in mediating cell death in immature and primary oocytes . These results suggest that the Smβ-Int1/SmVKR1 signaling complex is important for differentiation and survival in oocytes of paired schistosomes . Communication of cells with their environment is an essential requirement to regulate fundamental biological processes such as cell growth and differentiation . Different types of membrane-linked receptors mediate these communication processes , sometimes in a solitary , single receptor-mediated way , sometimes in a cooperative , multiple receptors-mediated way . The latter leads to the integration of different signaling cascades to execute one or more complex operations [1–4] . Schistosomes are parasitic platyhelminths causing schistosomiasis , one of the most threating infectious diseases worldwide after malaria [5–7] . As the only members of the trematodes , schistosomes have evolved separate sexes . The pathology of the disease is caused by eggs which are produced by paired schistosome females in the final host . Egg production is a complex process that involves not only the participation of different cell types , oocytes and vitellocytes . It also comprises the participation of different organs , ovary and vitellarium , whose development in the female depends on a close and permanent pairing contact with the male [8–11] . Although this nearly unique way of regulating sexual development in the animal kingdom is long known [12] and fundamental for the reproductive biology of schistosomes as well as for the pathogenic consequences of schistosomiasis , understanding the underlying molecular principles is still in its infancy . A number of signaling cascades have been uncovered that are involved in the control of gonad differentiation in paired schistosome females [13 , 14] . In S . mansoni , a kinase complex of three different cellular tyrosine kinases ( CTKs ) was postulated , whose members were able to interact with different receptors such as β integrin ( Smβ-Int1 ) and venus kinase receptors ( SmVKRs ) [15–18 ) . The latter represent an unusual type of receptor tyrosine kinases ( RTKs ) consisting of an intracellular tyrosine kinase ( TK ) domain with homology to that of insulin receptors ( IR ) and an extracellular venus-flytrap ( VFT ) module , whose structure is similar to the ligand binding domain of G protein-coupled receptors ( GPCRs ) of the C class [19 , 20] . RNAi-mediated knockdown of Smβ-Int1 and SmVKRs exhibited their roles in oogenesis and egg formation of S . mansoni females [17 , 21] . As potential ligands , L-Arginine ( L-Arg ) and calcium ions were discovered , which activated SmVKR1 and SmVKR2 , respectively , when they were expressed in Xenopus oocytes [22] . Studies in Aedes aegypti have substantiated roles of VKRs for reproduction . AaeVKR expression was found in the ovaries of blood-fed adult females and its activation by the neuroparsin , ovary ecdysteroidogenic hormone , was demonstrated [23] . Since neuroparsins are neuropeptides specific for arthropods [24] it still remains elusive whether and which other molecules except ions and amino acids may be able to activate schistosome VKRs [25] . Physical associations were documented between integrins and GFRs [26] . The latter include RTKs , whose activities can be likewise influenced by integrins [27 , 28] . As shown in skin fibroblasts , interactions with integrins support the activation of the GFRs even in the absence of a ligand [29] . Among other functions the αvβ3 integrin was found to directly associate with the insulin-like IGF1 receptor in vascular cells [30] . Such integrin-GFR interactions are mediated by bridging molecules such as ILK ( integrin-linked kinase ) , PINCH ( particularly interesting new cysteine-histidine-rich protein ) and Nck2 ( non-catalytic region of tyrosine kinase adaptor protein ) . They are central parts of an integrin-actin hub mediating many protein interactions that regulate processes such as pericellular matrix deposition , cell morphology , motility and apoptosis [31–33] . Aims of our study were to investigate whether Smβ-Int1 and SmVKR1 , which colocalize in the ovary of S . mansoni females and whose RNAi-mediated knock-downs led to similar phenotypes [17 , 21] , may interact to govern differentiation processes in this organ . Our findings provide first evidence for this cooperation and for a Smβ-Int1-induced activation of SmVKR1 , which is independent from an extracellular VKR ligand . Furthermore , our data suggest that Smβ-Int1/SmVKR1 cooperatively control the differentiation status of oocytes by regulating cell death-associated processes . In eukaryotic systems integrin-GFR cooperation can be accomplished by ILK , PINCH , and Nck2 . As cytoplasmic molecules they bind to the intracellular parts of integrin ( ILK ) or GFR ( Nck2 ) , or simultaneously to both receptors with PINCH as bridging molecule connecting ILK and Nck2 [32 , 33] . To investigate the possibility of such an interaction in S . mansoni , we first searched for orthologs in the schistosome database [34 , 35] . Based on comparisons to orthologs from human , potential candidate genes were identified and analyzed in silico . Deletion clones , including those potentially originating from alternative splicing events , were excluded from further analyses . Finally , full-length cDNAs of the longest variants of SmILK ( Smp_079760 ) , SmPINCH ( Smp_020540 . 2 ) , and SmNck2 ( Smp_014850 ) were amplified by RT-PCR , cloned , and sequenced . Detailed sequence analyses showed the cloned cDNAs of SmILK , SmPINCH , and SmNck2 isolated from the Liberian strain of S . mansoni [36] were 100% identical to those of the Puerto Rican strain used for genome sequencing [34 , 35 , 37] . BLAST analyses showed 97% and 89% identity at the cDNA level to ILK orthologs of S . haematobium ( XM_012945977 . 1 ) and S . japonicum ( AY810458 . 1 ) , respectively . Furthermore , SmILK exhibited all typical domains for this class of enzymes such as three N-terminal ankyrin repeat domains as well as one C-terminal kinase-like domain ( S1A Fig ) . The latter is considered as a catalytically inactive domain , which makes ILK a potential pseudokinase without catalytic but with structural importance [38 , 39] . As zinc-finger adaptor protein , PINCH contains five Lim ( similar to Lin11 , Isl-1 and Mec-3 proteins ) domains including eight zinc-binding residues [40] . SmPINCH follows this characteristic structural organization ( S2A Fig ) . At the cDNA level SmPINCH showed 91% and 81% identity to PINCH orthologs of S . haematobium ( XP_012797616 . 1 ) and S . japonicum ( AAX26687 . 2 ) , respectively . Nck2 , finally , represents another adaptor protein consisting of three SH3-domains and one C-terminal SH2-domain . The latter is important for binding to GFRs whereas one or more of the SH3-domains can support GFR binding or mediate interactions to downstream partners such as PINCH [41] . The occurrence of all these domains at comparable positions ( S3A Fig ) indicated that SmNck2 is an ortholog of Nck2 proteins . BLAST analyses showed 94% and 85% identity of SmNck2 at the cDNA level to Nck2 orthologs of S . haematobium ( XM_012936558 . 1 ) / S . japonicum ( AY809191 . 1 ) , respectively . Phylogenetic analyses of the three molecules with orthologs of vertebrates and invertebrates demonstrated that the schistosome ILK , and Nck2 formed separate clusters together with other parasitic platyhelminths , and schistosome Nck2 was part of a trematode cluster separate from the cestodes and other invertebrates ( S1B–S3B Figs ) . This observation coincides with previous findings made for the schistosome α and β integrins , which according to phylogenetic analyses constitute parasite-specific clades separate from free-living flatworms and further metazoan integrins [17] . In situ-hybridization localized the transcripts of SmILK , SmPINCH and SmNck2 in the ovary and the vitellarium of the female as well as in the testis of the male ( Fig 1 ) . Ovary and testis transcription were independently confirmed by gonad RNA-specific RT-PCRs [42] showing amplification products of the expected sizes ( S4 Fig ) . In each case the in situ-hybridization signals appeared to be stronger in the large part of the bulb-like ovary which contains mature primary oocytes . Furthermore , SmILK and SmPINCH transcripts were found in the parenchyma of both genders and in the subtegumental area , within the gastrodermis of males , and around the ootype , although not as dominant as in the gonads . Sense transcripts of all three genes as controls showed varying degrees of week signals ( very low in case of SmNck2 ) . This indicates antisense regulation , a finding made for different schistosome genes before including integrins and further molecules involved in reproduction [17 , 43 , 44] . To elucidate the roles of SmILK , SmPINCH , and SmNck2 in complex formation with Smβ-Int1 and SmVKR1 , we started a series of biochemical experiments in Xenopus oocytes . Previous studies had demonstrated the efficiency of expression of schistosome genes in this system and , furthermore , the possibility to study kinase activities by their capacities to induce resumption of meiosis and germinal vesicle breakdown ( GVBD ) [15 , 45] . Activation by L-Arg of the SmVKR1 kinase led to GVBD , which failed when a dead-kinase mutant of SmVKR1 was used [22] . No GVBD was observed in Xenopus oocytes when a wildtype form of SmILK was expressed ( Table 1 ) . This is in agreement with the present view that SmILK may represent a pseudokinase ortholog of eukaryote ILKs lacking catalytic activity [38 , 39] . Also PINCH and Smβ-Int1 failed to induce oocyte maturation . According to previously published data [22] , the wildtype form of SmVKR1 induced 90% GVBD only in the presence of its activating ligand L-Arg . The constitutively active SmVKR1 mutant induced GVBD , whereas a dead kinase mutant did not . When Smβ-Int1 , SmILK , and SmPINCH were coexpressed , no GVBD was observed . However , when these three proteins were co-expressed with SmVKR1 , GVBD was obtained independently of the addition of L-Arg ( Table 1 , Inj 11 ) . This suggested that SmVKR1 kinase activation could be induced by its participation to the complex with Smβ-Int1 , SmILK and SmPINCH . However , in this injection ( no . 11 ) GVBD was activated in the absence of SmNck2 . This finding led to the questions whether S . mansoni Nck2 is dispensable for complex formation , or whether Xenopus Nck2 may have rescued complex formation in this case ? When deletion mutants of SmILK ( SmILKΔAnk1 , missing the first ankyrin repeat necessary for interaction with PINCH; [32 , 33] ) or SmPINCH ( SmPINCHΔLIM4 , missing the fourth Lim domain necessary for interaction with Nck2/GFR; [32 , 33] ) or SmNck2 ( SmNck2ΔSH3 , missing the SH3 domain necessary for interaction with PINCH; [33] ) were used , activation of SmVKR1 was no more observed . The result with the deletion mutant of SmNck2 ( Table 1 , Inj 15 ) indirectly indicated the presence of Xenopus Nck2 in the complex and a competitive situation between Xenopus Nck2 and SmNck2ΔSH3 when the latter protein was present . Furthermore , adding the ILK-inhibitor QLT-0267 ( 1 μM ) also prevented GVBD in oocytes expressing the wildtype forms of Smβ-Int1 , SmILK , SmPINCH , SmNck2 , and SmVKR1 . These data suggest a direct interaction of these proteins , and also that Smβ-Int1-SmILK-SmPINCH-SmNck2-SmVKR1 complex formation is able to induce GVBD in Xenopus oocytes in the absence of a ligand for SmVKR1 . This interaction appeared to be specific for SmVKR1 , since other RTKs such as SmVKR2 [22] , SER ( S . mansoni EGF Receptor; [45 , 46] or the insulin receptor orthologs SmIR 1 and SmIR2 [47] were not activated by this complex . Furthermore , since GVBD was supposed to be dependent on the kinase activation of SmVKR1 , we checked the autophosphorylation status of SmVKR1 by Western blot analysis . GVBD occurred only when SmVKR1 was phosphorylated ( see below ) . To confirm the existence and the function of this complex , the HA-tagged intracellular part of Smβ-Int1 [17] was co-expressed in Xenopus oocytes together with V5-tagged variants of SmILK ( wildtype and SmILKΔAnk1 ) , SmVKR1 ( dead kinase and constitutively active mutants ) , or Flag-tagged SmPINCH ( wildtype and SmPINCHΔLIM4 ) and SmNck2 ( SmNck2ΔSH3 ) for co-immunoprecipitation . In this series of experiments , L-Arg was not used for stimulating SmVKR1 activity ( Table 2 ) . Besides investigating the GVBD-inducing activity of appropriate combinations of complex members in their wildtype or mutated forms , oocyte lysates were immunoprecipitated with an α-HA antibody , and Western blot analyses were performed with α-HA or α-V5 to investigate the presence of members complexed with Smβ-Int1 . The results showed V5-tagged SmILK , SmPINCH , and SmVKR1 in HA-tagged precipitates only when the wildtype forms were used ( Fig 2 , lane 11 ) . A replication of the experiment demonstrated complex formation also when the constitutively active SmVKR1 variant was used ( Fig 3 , lane 14 ) . As expected , no V5-tagged precipitates were detected , when the deletion variants SmILKΔAnk1 or SmPINCHΔLIM4 or the dead kinase variant of SmVKR1 were used . These results corresponded to the GVBD results obtained , confirming the formation of a complex of these four proteins . However , complex formation was possible due to the presence of Xenopus Nck2 ( Fig 3 ) , which was detected by Western blot analysis . This confirmed the previous interpretation of the GVBD experiment ( Table 1 ) . To investigate SmVKR1 activation upon complex formation , the phosphorylation status of this receptor was investigated . After confirming that SmNck2 is also part of the immunoprecipitated protein complex ( Fig 4A ) , Western blot analyses showed that SmVKR1 phosphorylation ( without adding L-Arg ) occurred only when it was coexpressed together with the wildtype forms of Smβ-Int1 , SmILK , SmPINCH , and SmNck2 ( Fig 4B ) . When deletion mutants of individual complex partners or the ILK inhibitor QLT-0267 were used , no SmVKR1 phosphorylation was detected . This is in perfect agreement with the GVBD results obtained with the same combinations of molecules ( Table 1 ) . In a previous study it was shown that upon SmVKR1 stimulation with L-Arg signaling pathways known to be involved in RTK signaling were activated in Xenopus oocytes . Among these were ERK , JNK , and Akt pathways [21] . To find out whether Smβ-Int1/SmVKR1 complex formation without L-Arg induction activates the same signaling cascades in Xenopus oocytes , we performed cotransfection experiments and subsequent phosphorylation assays . Indeed , the obtained results showed that SmVKR1 in cooperation with all complex partners induced the phosphorylation of ERK , JNK , and AKT in a ligand-independent manner ( Fig 5 ) . In analogy to the previous results , no phosphorylation of these signaling molecules was observed when one of the complex members was used in its mutated form or when the ILK-inhibitor QLT-0267 was applied . The effect of Smβ-Int1/SmVKR1 complex formation on the phosphorylation of ERK , JNK , and AKT resembled the activation of Xenopus oocyte receptors by insulin or the natural ligand progesterone . Because SmILK is one of the decisive complex partners mediating Smβ-Int1 cooperation with SmVKR1 we functionally analyzed this molecule in more detail . RNAi-mediated SmILK knock-down experiments were performed with S . mansoni couples in vitro , and the knock-down value determined by qPCR to be nearly 90% ( S5 Fig ) . Following treatment with SmILK-dsRNA , pairing stability was not affected , and the amount of couples was similar to the untreated control group . However , egg production per ( remaining ) couple of the treated group significantly decreased during the observation period from 48 h post treatment on compared to the control ( Fig 6 ) . Inhibiting ILK was also achieved by QLT-0267 , and following treatment with different concentrations ( 50–200 μM ) a negative effect on pairing stability was observed . Furthermore , also egg production per remaining couple decreased in a concentration-dependent manner from 48 h post treatment on ( Fig 7 ) . Morphologically , CLSM analysis showed effects of QLT-0267 on oogenesis in paired females . This inhibitor caused not only a reduction of oocyte number and the mislocalization of oocytes of various stages of differentiation in the different parts of the ovary but also oocyte degeneration ( Fig 8 ) . The intensity of the phenotype increased with QLT-0276 concentration . A similar oocyte-related observation was made by RNAi in ILK-dsRNA treated paired females ( Fig 8 ) , although the strength of the observed phenotype ( less oocytes , mislocalization , degeneration ) was weaker compared to inhibitor treatment . Previous studies in cancer cells provided evidence that among other functions ILK is involved in cytoskeletal reorganization and cell survival , and its deregulation can contribute to errors in cell division and genomic instability [48] . Microtubule disruption was shown to induce cytoskeleton as well as cell adhesion changes . This led to focal adhesion kinase hydrolysis and the onset of apoptosis , a phenotype that was rescued by ILK overexpression [49] . Because there is evidence that apoptosis has a biological function for the maintenance of the maturation state of the reproductive organs of paired females [50] , we investigated whether SmILK may be involved in this processes in S . mansoni . To this end we compared paired females treated with QLT-0267 and DMSO as control and performed immunolocalization with a β-tubulin antibody ( S6 Fig ) . Under inhibitor influence the number of immature and primary oocytes was reduced in inhibitor-treated females . Compared to the control , primary oocytes clustered closer together , they appeared more compact , and some appeared as rounded up ( Fig 9 ) . In a previous study first hints were obtained that laminins as extracellular matrix proteins may interact with Smβ-Int1 [17] . To investigate whether there is also an influence on components of the extracellular matrix we immunolocalized laminin in paired females treated with QLT-0267 or DMSO ( S6 Fig ) . Indeed , a concentration-dependent decrease of laminin staining was observed within the epithelium surrounding the ovary of treated females ( Fig 10 ) . TUNEL assays finally confirmed apoptotic processes in ovaries of females treated with QLT-0267 . TUNEL-positive cells occurred mainly within the smaller part of the ovary containing immature oocytes ( Fig 11 ) . To get further support for apoptotic processes induced by QLT-0267 in females , caspase-3 activity was determined in inhibitor-treated females . Following treatment the level of caspase-3 activity increased significantly ( Fig 12 ) . Next we investigated whether the expression of genes involved in early steps of apoptosis is affected . To induce the mitochondrial apoptosis pathway , a number of pro-apoptotic BCL-2 ( B cell lymphoma 2 ) proteins collaborate with the outer mitochondrial membrane to permeabilize it . BAK ( BCL-2 Antagonist Killer 1 ) and BAX ( BCL-2 Associated X protein ) are pro-apoptotic BCL-2 family members which are essential for the permebilization of the mitochondrial outer membrane [51] . We selected these genes because presumptive orthologs exist in the genome of S . mansoni ( BAK , Smp_095190; BAX , Smp_072180 ) . Therefore , we investigated the transcript profiles of Smp_095190 and Smp_072180 in schistosome females after treatment of couples for 72 h with 50 μM QLT . Compared to a DMSO control , we detected an upregulation of BAX after treatment , whereas BAK transcription remained constant . Upon RNAi both schistosome orthologs BAK and BAX were transcribed at higher levels ( S7 Fig ) . Although much research has been performed on integrins and integrin signaling in different organisms , there is not much known about their roles in platyhelminths . Here we report on an integrin-signaling complex in S . mansoni consisting of Smβ-Int1 , SmILK , SmPINCH , SmNck2 , and SmVKR1 . According to phylogenetic analyses , SmILK , SmPINCH , SmNck2 form clusters that are specific for parasitic platyhelminths as it was shown for integrins before [17] . Together with the exclusive role of VKRs [25] , it appears likely that parasites have modified the function of insulin-like signaling as well as integrins and their interacting partners for specific signaling purposes . Among these , at least one deals with the reproductive biology of platyhelminths . In this context schistosomes exhibit remarkable features because of the pairing-dependent development and maintenance of the differentiation status of female gonads . The involvement of schistosome VKRs and integrins for this physiological process has already been demonstrated [17 , 21] , and studies on the VKR ortholog AaeVKR of A . aegypti support the assumption of a specific role of VKRs for oogenesis and/or egg formation [23] . Our study provides first evidence for cooperation between integrin and VKR signaling in S . mansoni . This interaction is mediated by SmILK , SmPINCH , and SmNck2 , cytoplasmic molecules with bridging function . Their colocalization with Smβ-Int1 and SmVKR1 , especially in the ovary , indicated potential functions for the reproductive biology of schistosomes . In all three cases the intensities of localization signals in the ovary were higher in its posterior part which contains mature primary oocytes . Smβ-Int1 and Smα-Int1 were localized in the ovary—also dominating in its posterior part - , the vitellarium , the testes , the ootype-surrounding area , the subtegument , and within the parenchyma [17] . SmVKR1 expression was localized mainly in the female ovary , especially in mature , primary oocytes in the posterior part . In addition , SmVKR1 was also localized around the ootype and in the parenchyma of males [21] . Thus SmILK , SmPINCH , SmNck2 colocalized widely with Smβ-Int1 and SmVKR1 including their preferential occurrence in mature , primary oocytes , a prerequisite for potential interactions . Different experiments with Xenopus oocytes expressing the complex members alone or in defined combinations of wildtype or mutated forms finally confirmed also by co-immunoprecipitation that a Smβ-Int1-SmILK-SmPINCH-SmNck2-SmVKR1 complex can be formed . GVBD assays demonstrated the biochemical function of this complex and the potentiality of SmVKR1 to be activated inside of the complex and to induce—in the absence of its ligand—processes leading to GVBD , which was confirmed by the results obtained . This suggests a new mode of SmVKR1 activation , which is achieved in a ligand-independent fashion by indirect cooperation with a β-integrin receptor . As mediators , GFR-specific and β integrin-specific adapter molecules operate , in this case SmILK , SmPINCH , and SmNck2 . The participation of SmNck2 was shown indirectly by the use of a deletion mutant that negatively influenced GVBD , by Western blot analysis confirming the presence of Xenopus Nck2 in complexes without SmNck2 , and finally by co-immunoprecipitation of SmNck2 after its addition . In Xenopus oocytes , ligand-activated RTKs as IRs trigger the activation of Erk MAPK and PI3K/Akt/mTOR and JNK pathways resulting in meiotic maturation [52] . As shown for L-Arg-activated SmVKR1 [21] , in our actual study the phosphorylation of Erk1/2 , Akt , and JNK in Xenopus oocytes was achieved also by Smβ-Int1/SmVKR1 complex formation without ligand activation . Thus similar to IR activation , complex-activated SmVKR1 induced signaling processes involved in protein synthesis and cellular growth associated with Xenopus oocyte maturation , which substantiates the IR-like function of SmVKR1 but also its conjunction with oogenesis . Functional analyses of SmILK in Xenopus oocytes or as a member of the complex by RNAi and inhibitor studies finally indicated that this molecule represents a pseudokinase being involved in different processes in schistosomes . Among these is inside-out signaling in the ovary because the extracellular matrix as part of the epithelium surrounding the ovary was changed upon inhibiting SmILK as shown by laminin immunolocalization . Furthermore , SmILK appeared to control oocyte localization within the ovary , and oocyte survival . Inhibiting SmILK activity led to the reduction of the amount of immature oocytes and the degeneration of mature , primary oocytes . This is in part explained by apoptotic processes , for which evidence was obtained by TUNEL assays in case of immature oocytes , by determining caspase-3 activity which increased following inhibitor treatment , and by transcriptional analysis of the schistosome orthologs of BAK and BAX , two pro-apoptotic genes [51] . A recently conducted RNA-seq study revealed that both genes were expressed in schistosome females and within the ovary . Interestingly , the profiling of transcript abundance revealed that both genes were more abundantly transcribed in the ovaries of unpaired , immature females . After pairing , transcript abundance of both genes decreased in the ovary ( [53]; S7 Fig ) . This supports the conclusion that apoptosis plays a role in oocyte differentiation , and that males exert a regulatory influence on this—suppressing apoptosis in the gonads of their female partners during a constant pairing contact ( Fig 13 ) . Degenerated primary oocytes were also detected that did not respond to TUNEL staining . Thus it seems feasible that further , apoptosis-independent processes leading to cell death contribute to oocyte degeneration . In summary , the results presented here strongly suggest that the Smβ-Int1/SmVKR1 complex in the ovary of paired schistosomes is important for the maintenance of the differentiation status of oocytes and their survival . Against the background of the unusual reproductive biology of schistosomes this conclusion supports findings of a previous , independent study showing that apoptosis is used to control vitelline cell survival in a pairing-dependent manner in S . mansoni [50] . In this context our results match to a scenario of cell death processes controlling gonad maintenance in schistosome females and thus contribute to the understanding of biological processes controlling reproductive biology in this exceptional parasite . It has been hypothesized before that SmVKR1 , possibly activated by L-Arg delivered with the male seminal fluid [21] , is responsible for meiosis resumption and/or oocyte migration in schistosome females . In view of the new results it appears feasible that integrin-signaling contributes to this process providing a SmVKR ligand-independent alternative for activation ( Fig 13 ) . This could be achieved by mechanosensory forces . Indeed , integrins have been shown to sense , sort , and transduce mechanical forces into cellular responses . This form of integrin-based mechanotransduction contributes among others to cell growth , cell migration , gene expression including the activation of kinases , but also to apoptosis [54–57] . Animal experiments using Syrian hamsters ( Mesocricetus auratus ) as model hosts were performed in accordance with the European Convention for the Protection of Vertebrate Animals used for experimental and other scientific purposes ( ETS No 123; revised Appendix A ) and were approved by the Regional Council ( Regierungspraesidium ) Giessen ( V54-19 c 20/15 c GI 18/10 ) . S . mansoni was maintained in Biomphalaria glabrata as the intermediate host , with Syrian hamsters ( Mesocricetus auratus ) as the definitive hosts [36] . Adult worms were obtained by hepatoportal perfusion at day 46 or day 67 ( in case of single sex infection; [58] ) post-infection , respectively , and kept in M199 medium ( Gibco ) supplemented with 10% newborn calf serum and 1% ABAM-solution ( 10 , 000 units penicillin , 10 mg streptomycin and 25 mg amphotericin B per ml ) at 37°C and 5% CO2 for 24h until the experiments started . Couples were cultivated in 6-well plates in groups of eight per well ( n = 3 ) and 3 ml supplemented M199 medium for RNA interference ( RNAi ) experiments ( see below ) or inhibitor studies . The latter were performed with the integrin-linked kinase ( ILK ) inhibitor QLT-0267 ( Dermira , Inc . , USA; [59] ) which was added at final concentrations and period of times as indicated . It targets the ATP-binding site of ILK and was shown to be as effective as siRNA-mediated depletion of ILK [60] . Since ILK exerts no catalytic function , the inhibitory effect of QLT-0267 was explained by an impairment of the stability of ILK [61] . Equivalent volumes of dissolvent DMSO was used as control . Worms were monitored by bright-field microscopy ( CX21 , Olympus; Labovert FS , Leitz ) over periods of 24 h– 96 h to analyze pairing stability , egg production , gut peristalsis and movement . For cloning of the full-length cDNAs of SmILK ( Smp_079760 ) , SmPINCH ( Smp_020540 . 2 ) , and SmNck2 ( Smp_014850 ) , total RNA was isolated from adult schistosomes using Trizol reagent ( Invitrogen ) . Residual DNA was removed by DNase digestion ( RNAeasy kit , Qiagen ) following the manufacturer’s instruction . RNA quality was checked by Bioanalyzer microfluidic electrophoresis ( Agilent Technologies ) . Starting RT-PCR the synthesis of cDNA was performed with 1 μg RNA using QuantiTect Reverse Transcription Kit ( Qiagen ) . PCR reactions were performed in a final volume of 25 μl using primer end concentrations of 800 nM , denaturation at 95°C for 30 sec , annealing at 54°–64°C depending on the primer combinations ( S1 Table ) , and elongation at 72°C for up to 2 min , and using FirePol-Taq ( Solis biodyne ) . As vectors for cloning , pACT2 ( Clontech ) , pcDNA3 . 1 ( Invitrogen ) , or pBridge ( Clontech ) were used for directional cloning via restriction enzyme sites . Full-length SmILK cDNA was cloned via NotI and XbaI into pcDNA3 . 1 , full-length SmPINCH cDNA via EcoRI/PstI into pBridge , and full-length SmNck2 via BamHI and XbaI into pcDNA3 . 1 ( S1 Table ) . Primers designed for RT-PCRs to generate these cDNAs contained appropriate restriction sites for cloning . The sequence integrities of all cloned cDNAs were verified by sequencing ( LGC Genomics , Berlin ) . Ovaries of female worms were isolated using the combined detergents/enzyme-based organ isolation protocol [42] . In short , isolated adult females ( about 50 each ) were transferred into 2 ml-reaction vessels and washed twice with 2 ml of non-supplemented M199-medium at room temperature . The medium was removed , and 500 μl of tegument solubilisation ( TS ) -solution was added ( 0 . 1% of each following compounds in DEPC ( diethylpyrocarbonate ) /PBS ( phosphate-buffered saline ) : Brij 35 ( Roth ) , Nonidet P40-Substrate ( Fluka ) , Tween80 ( Sigma ) and TritonX-405 ( Sigma ) , pH 7 . 2–7 . 4 ) followed by incubation in a thermal shaker ( TS-100 , Biosan ) for 5 min at 1 , 200 rpm at 37°C . Shaking was repeated twice , and the solution was replaced after each cycle . Then the worms were rinsed three times with M199 and subsequently treated with elastase ( 300 μl elastase solution: 5 U/ml in M199; Sigma ) at 37°C and 650 rpm in the thermal shaker to release the ovaries . Digestion was monitored by bright-field microcopy ( Leica ) and stopped when the gonads were released from the disrupted and digested worm carcasses . Finally , the gonads were manually collected by pipetting and transferred into supplemented M199 medium . Sample preparation of S . mansoni adults was conducted as described previously [62] . In short , schistosome pairs were fixed in Bouin's solution ( picric acid/acetic acid/formaldehyde; 15/1/5 ) followed by embedding in paraplast ( Paraplast plus , Sigma ) . Sections of 5 μm thickness were incubated in xylol and after rehydration , the sections were treated with proteinase K ( 1 μg/ml ) and dehydrated . As probe , in vitro-generated transcripts were synthesized and labeled with digoxigenin as suggested by the manufacturer ( Roche ) . The correct sizes of labeled sense and antisense transcripts were checked by gel electrophoresis , and the RNA quality was tested by blotting and detection of digoxigenin using alkaline phosphatase-conjugated anti-digoxigenin antibodies , naphtol-AS-phosphatase , and Fast Red TR ( Sigma ) . In situ-hybridization was performed at 57°C for 16 h . Afterwards , the sections were washed up to 0 . 5 × SSC ( 75 mM NaCl , 7 . 5 mM sodium citrate , pH 7 . 0 ) , and detection of alkaline phosphatase was performed as mentioned above . The intracellular part of Smβ-Int1 containing the C-terminus with an HA-tag at its N-terminus was subcloned into pcDNA 3 . 1 ( Invitrogen ) as described earlier [17] . Capped messenger RNA ( cRNA ) encoding Smβ-Int1 C-term was synthesized in vitro ( T7 mMessage machine Kit , Ambion , USA ) following a previously established protocol [45] . Furthermore , V5-tagged SmILK and SmPINCH and Flag-tagged SmNck2 were cloned the same way into pcDNA 3 . 1 , and their sequence identities confirmed by commercial sequencing . Also cRNAs were prepared from these clones as well as from V5-tagged SmVKR1 variants ( wildtype SmVKRwt , dead kinase mutant SmVKRdk [= KO] , and a constitutively active mutant SmVKR1YYRE [= XE] ) cloned in pcDNA 3 . 1 as reported in a previous study [22] . Interaction studies between these proteins ( see results ) were done by co-injecting different cRNA combinations into Xenopus oocytes as reported before [17 , 45] . Expressed proteins were detected by immunoprecipitation and Western blot analyses . Following the standard procedure [45] , 30 oocytes were lysed in 300 μl of buffer ( 50 mM HEPES , pH 7 . 4 , 500 mM NaCl , 5 mM MgCl2 , 1 mg/ml bovine serum albumin , 10 μg/ml leupeptin , 10 μg/ml aprotinin , 10 μg/ml soybean trypsin inhibitor , 10 μg/ml benzamidine , 1 mM PMSF , 1 mM sodium vanadate ) after 5 h or 15 h of expression . Following centrifugation at 4°C for 15 min and 10 , 000 g , the resulting supernatants were incubated with anti-HA ( 1:100; Invitrogen ) or anti-V5 ( 1:100; Invitrogen ) then added to protein A-Sepharose beads ( 5 mg , Amersham Biosciences ) for 1 h at 4°C . After washing three times , immune complexes were eluted from the beads in Laemmli buffer and analyzed by SDS-PAGE ( 7 . 5%–15% polyacrylamide gels ) . Western blot analyses were performed using anti-V5 ( 1: 50 , 000 ) , anti HA ( 1: 50 , 000 ) , anti-Flag ( 1: 1 , 000 ) , anti-human nck2 ( 1: 1 , 000 , nck2 ( 8 . 8 ) : sc-20020 , Santa Cruz Biotechnology ) , or PY20 ( 1: 10 , 000; anti-phosphotyrosine , BD Biosciences ) antibodies . The following primary antibodies were applied to confirm the presence of total or phosphorylated ERK2 , JNK and Akt kinases: anti-ERK2 ( 1: 10 , 000; Santa Cruz Biotechnology ) , anti-phospho p44/p42 MAPK ( ERK1/2; Thr 202/Tyr 204; 1: 10 , 000; Cell Signalling Technology ) , anti-c-jun N-terminal kinase JNK ( 1: 10 , 000; Sigma ) , anti-active JNK polyclonal antibody ( 1: 8 , 000; Promega ) , anti-Akt1 ( C-20; 1: 5 , 000; Santa Cruz Biotechnology ) , anti-phospho Akt ( Thr308; 1: 5 , 000; Upstate Biotechnology ) and anti-phospho Akt ( Ser 473; 1: 5 , 000; Upstate Biotechnology ) . Mouse , rabbit or goat Trueblot secondary antibodies ( eBioscience ) were used as secondary antibodies and chemoluminescence was detected using the advanced ECL detection system ( Amersham Biosciences ) . Following standard protocols for RNAi in adult schistosomes [15 , 63] , double-stranded RNA of approximately 500 bp was synthesized ( nucleotide position 559–1016 ) using the MEGAscript RNAi kit ( Life Technologies ) . Gel electrophoresis in 1 . 2% agarose-MOPS was conducted to prove for single RNA bands of the correct size . Schistosome couples in groups of eight pairs ( n = 3 ) were electroporated in the presence of 25 μg dsRNA and subsequently soaked in vitro for 96 h . Every 24 h the treated worms were inspected and different parameters evaluated such as pairing stability , egg production , gut peristalsis and movement . Schistosome samples were collected and transferred into PeqGOLD TriFast ( Peqlab ) . After storage at -80°C or immediately after transfer , RNA isolation was done following the manufacturer’s instructions ( Peqlab ) . About 500 ng total RNA was used for cDNA synthesis using the Quantitect Reverse Transcription Kit ( Qiagen ) . For PCR , 1 μl of a 1:20 dilution of cDNA was tested using exon-spanning PDI ( protein dilsufide isomerase ) 5’/3’ primers; forward: 5´-AAATGATGCCCCGACTTACC-3´ and reverse: 5´- TCATCCCAAACTGGAGCAAG-3`[62 , 64] ) to confirm that the genomic DNA was properly removed . For quantitative RT PCR ( qRT-PCR ) , a RotorGene-Q PCR cycler ( Qiagen ) was used and all reactions were set up in triplicates . Each reaction had a final volume of 25 μl; 5 μl of a 1:20 cDNA served as template and 125 nM ( final concentration ) of each primer were added to 12 . 5 μl of 2x PerfeCTa SYBR Green super mix ( Quanta ) . No template controls ( NTC ) were included in each run . RNAi-mediated knockdown of gene expression was analysed by absolute quantification . Therefore , a standard curve on diluted gel eluate was included in each run [65] . For qPCR analyses to study transcript profiles of SmBAK ( Smp_095190 ) , SmBAX ( Smp_072180 ) , SmmTor ( Smp_122910 ) , and SmSod ( Smp_056440 ) several genes were tested for their suitability as reference . Based on transcriptome data [53] the gene Smp_008900 ( annotated as eukaryotic translation initiation factor 4 gamma ) fulfilled this criterion and was further tested under various condition using an absolute quantification approach . To this end , the Smp_008900 amplicon was cloned into pDrive ( Qiagen ) and served as template in dilution series . Different cDNAs of electroporated and inhibitor-treated S . mansoni couples confirmed constant numbers of Smp_008900 transcripts ( primers , see S1 Table ) . Sliced specimens of 4 μm thickness on slides were deparaffinized , dehydrated and then equilibrated in proteinase-K buffer ( 100 mM Tris/Cl , 50 mM EDTA pH 8 . 0 ) for 5 min . Subsequently , treatment with proteinase K ( 1 μg/ml ) was performed for 20 min at 37°C . Afterwards , slides were rinsed once with 1x PBS and immersed twice with 500 μl washing buffer provided by the fluorometric DNA-fragmentation detection kit III ( F-dUTP; Promokine ) for 5 min at ambient temperature . The staining solution containing FITC-labeled dUTP was prepared according to the manufacturer’s instruction ( Promokine ) . A control solution was prepared without TdT enzyme . Subsequently , specimens were immersed with 100–200 μl of the staining solution and kept in the dark . Following incubation for 60 min at 37°C , the slides were rinsed twice with 500 μl rinse buffer ( Promokine ) for 5 min at ambient temperature and counterstained with 200 μl propidium iodide/RNase A solution ( Promokine ) for 15 min . Slides were finally mounted with FluoroMount ( Roth ) and analyzed within 3 hours after staining by fluorescence microscopy ( Ex/Em = 488/520 nm for FITC , and 488/623 nm for PI ) . Following perfusion , S . mansoni couples were taken in culture and treated with DMSO or with QLT-0267 ( 100 μM ) for 72 h . Following treatment the couples were carefully separated with featherweight forceps , and females and males transferred separately into 1 . 5 ml tubes with 500 μl 1x PBS for washing . After sedimentation , the supernatant was replaced by 50 μl cold ( 4°C ) cell lysis buffer of the caspase-3 colorimetric assay kit ( Promokine ) and the samples kept on ice . After 10 min incubation , worms were homogenized with sterile pestils and kept on ice for further 10 min . Debris were subsequently sedimented by 10 . 000 x g centrifugation for 2 min at 4°C and afterwards placed back on ice . Subsequently , 20 μl of the supernatant were mixed with 30 μl of pre-transferred cell-lysis buffer in 96-well plates , and 50 μl of two-fold reaction buffer complemented with 10 μM DTT was added . Two hours incubation at 37°C allowed cleaving the p-nitroanilide-labeled substrate DEVD ( Promokine ) that was added at a final concentration of 200 μM . Samples were read at 405 nm with a Varioscan plate reader ( Thermo Fisher Scientific ) . Samples were normalized to the protein concentration that was determined using the BCA assay kit ( Pierce ) promptly after the caspase-3 assay was set up . The results relied on the analysis of three biological replicates obtained from independent perfusions . In contrast to previously published protocols [50] caspase-3 activity was not detected when the homogenization step was omitted . Adult worms were stained with carmine red for a general morphological analysis by CLSM according to previously published protocols [14 , 66] . For microscopy ( CLSM; Leica TSC SP2 microscope ) and documentation the probes were excited with a 488 nm He/Ne laser and emission was captured with a 470 nm long-pass filter in reflection mode as described before [14] . Fluorescence microscopy of antibody-stained worm sections ( 5 μM ) was done using an Olympus IX 81 inverted microscope . Anti-laminin ( antibodies-online . com; LN , ABIN268409 ) and anti-β-tubulin ( antibodies-online . com; anti-TUBB , ABIN269949 ) antibodies were used in concentrations of 1: 5 , 000 each , as recommended by the manufacturer ( Novus Biologicals ) . As secondary antibody , a fluorescence-labeled goat anti-rabbit antibody was used ( LI-COR Bioscience , IRDye 680LT; 1: 5 , 000 ) . These antibodies were tested on lysates of adult schistosomes by Western blot analyses as described before [42] using 15 μg protein each , which had been size-separated by SDS-PAGE using 7 . 5%–15% polyacrylamide gels depending on the size of the protein to be detected . The following public domain tools were used: BLASTx ( http://www . ncbi . nlm . nih . gov/BLAST ) , SchistoDB ( http://schistodb . net/schisto/; [67] ) , and WormBase ParaSite ( release 6 , April 2016; http://parasite . wormbase . org/; [37] ) . The online-tool SMART ( http://smart . embl-heidelberg . de/ ) [68] was used to predict protein domains . Primer3Plus was used for primer design ( http://www . bioinformatics . nl/cgi-bin/primer3plus/primer3plus . cgi ) and Oligo Calc for analyzing primer properties ( http://www . basic . northwestern . edu/biotools/oligocalc . html ) .
Parasites of the genus Schistosoma cause schistosomiasis , a life-threatening infectious disease for humans and animals worldwide . Among the remarkable biological features of schistosomes is the differentiation of the female gonads which is controlled by pairing with the male and a prerequisite for egg production . Eggs , however , are not only important for the maintenance of the life-cycle; they also cause the pathological consequences of schistosomiasis . Part of the eggs gets trapped in host tissues such as liver and spleen and trigger inflammatory processes , finally leading to liver cirrhosis . Research activities of the last decade have indicated that different families of cellular and receptor-type kinases but also integrins contribute to the control of mitogenic activity and differentiation the female goands . In this context an unusual class of receptor tyrosine kinases ( RTKs ) has been identified , the venus kinase receptors ( SmVKRs ) . By biochemical and molecular approaches we demonstrate that SmVKR1 activation can be achieved by cooperation with a signaling complex consisting of the beta integrin receptor Smβ-Int1 and the bridging molecules SmILK , SmPINCH , SmNck2 . Besides unravelling a novel way of SmVKR1 activation , we provide evidence that this complex controls the differentiation status of oocytes by regulating cell death-associated processes .
You are an expert at summarizing long articles. Proceed to summarize the following text: Leptospirosis is one of the most important neglected tropical infectious diseases worldwide . Icterohaemorrhagiae has been throughout recent history , and still is , the predominant serogroup of this pathogen in China . However , very little in detail is known about the serovars or genotypes of this serogroup . In this study , 120 epidemic strains from five geographically diverse regions in China collected over a 50 year period ( 1958~2008 ) , and 8 international reference strains characterized by 16S rRNA sequencing and MLST analysis . 115 , 11 and 2 strains were identified as L . interrogans , L . borgpetersenii , and L . kirschneri , respectively . 17 different STs were identified including 69 ST1 strains , 18 ST17 , 18 ST128 , 9 ST143 and 2 ST209 . The remaining 12 strains belonged to 12 different STs . eBURST analysis demonstrated that , among the clonal complexes isolated ( CCs ) , CC1 accounted for 73 . 3% ( 88/120 ) strains representing three STs: ST1 , ST128 and ST98 . ST1 was the most likely ancestral strain of this CC , followed by singleton CC17 ( 17/120 ) and CC143 ( 11/120 ) . Further analysis of adding 116 serogroup Icterohaemorrhagiae strains in the MLST database and studies previously described using global eBURST analysis and MST dendrogram revealed relatively similar ST clustering patterns with five main CCs and 8 singletons among these 244 strains . CC17 was found to be the most prevalent clone of pathogenic Leptospira circulating worldwide . This is the first time , to our knowledge , that ST1 and ST17 strains were distributed among 4 distinct serovars , indicating a highly complicated relationship between serovars and STs . Our studies demonstrated a high level of genetic diversity in the serogroup Icterohaemorrhagiae strains . Distinct from ST17 or ST37 circulating elsewhere , ST1 included in CC1 , has over the past 50 years or so , proven to be the most prevalent ST of pathogenic leptospires isolated in China . Moreover , the complicated relationship between STs and serovars indicates an urgent need to develop an improved scheme for Leptospira serotyping . Leptospirosis , caused by pathogenic Leptospira species , is emerging as one of the most widespread zoonosis with an estimated global burden of more than 500 , 000 cases of severe human leptospirosis and 100 , 000 deaths as well as great economic burden in farm and pet animals per year [1] . However , its actual prevalence might be still largely underestimated due to a lack of convenient and effective diagnostic approach resulting in underreporting and low awareness in medical and public health communities . The symptom of leptospirosis ranges from an asymptomatic or mild infection to severe manifestation causing multi-organ dysfunction and even deaths in humans [2 , 3] . Humans and animals can be infected through the direct or indirect exposure to urine of infected animals and urine-contaminated water or soil [2 , 4 , 5] . Nowadays , the classical taxonomy typing method of Leptospira spp . is mainly based on serological techniques including microscopic agglutination test ( MAT ) and cross-agglutinin absorption test ( CAAT ) . It is the practical taxon at the subspecies level and remains extremely valuable for epidemiology analysis of Leptospira . However , MAT or CAAT is laborious and time-consuming because these methods require the maintenance of a large range of reference strains and corresponding rabbit antisera . In addition , some serovars were found to have a across reaction . Therefore , MAT or CAAT is no longer sufficient to identify isolates to their species level . Recently , several molecular typing methods have been developed to discriminate Leptospira spp including PCR-restriction endonuclease analysis , pulsed-field gel electrophoresis ( PFGE ) [6–8] , multilocus variable number of tandem repeats analysis ( MLVA ) [9 , 10] . The most commonly used multilocus sequence typing ( MLST ) scheme has been recommended as a routine typing Leptospira species method and population phylogenetic analysis [11–14] . To date , Leptospira genus is now classified into 9 pathogenic , 5 Intermediate and 6 saprophytic species [11 , 15 , 16] . L . interrogans , L . borgpetersenii and L . kirschneri are the main pathogenic species of leptospirosis in humans and animals worldwide . Based on antigenic similarity , more than 300 antigenically related pathogenic serovars are clustered into 24 serogroups in the world , and 75 serovars belonging to 18 serogroups are reported in China . Among them , serogroup Icterohaemorrhagiae is the most predominant epidemic-causing strain in China , and is responsible for more than 60% reported cases of lepotospirosis [17] . However , to date , there is very limited information of the detailed predominant serovars or genotypes of serogroup Icterohaemorrhagiae in China , which plays a crucial role in the epidemiology of leptospirosis . Understanding this role may allow for the development of better control strategies of this disease . The aim of this work was to investigate the genetic diversity of predominantly epidemic serogroup icterohaemorrhagiae of pathogenic Leptospira in Mainland China . Therefore , we investigated the genetic characteristics of 120 serogroup Icterohaemorrhagiae strains isolated from leptospirosis patients or rodent sources in five Chinese provinces with the highest leptospirosis prevalence during the past 50 years by a combination of 16S rRNA sequencing and MLST . Our results could provide a more comprehensive overview of the predominant epidemic serogroup icterohaemorrhagiae in Mainland China and should contribute to understanding the changing epidemiological and evolutionary trends of this serogroup . To obtain a more overview of global population structure and microevolution of serogroup Icterohaemorrhagiae Leptospira strains , the available MLST data from MLST database and some previous studies described from other countries representing the international strains were introduced and further analyzed . The results in this study may be used as markers to trace pathogenic strains isolated from the environment and host in the near future , as well as to obtain a more complete overview of global population structure and microevolution of L . interrogans serogroup Icterohaemorrhagiae strains . The information of these patients with leptospirosis in this study was anonymously obtained from national infectious disease surveillance system in China; only lots of the patients in the recent years were required to provide brief informed consent before blood sampling . All of the protocols in the study including collection and application of these anonymous serum specimens were conducted with approval by the ethical committee of the Chinese Center for Disease Control and Prevention ( China CDC , Beijing , China ) . A total of 128 non-epidemiologically related leptospiral isolates , including 120 Chinese strains isolated from five provinces and 8 international reference strains from seven countries ( Indonesia , Congo , Denmark , Japan , Zaire , Sri Lanka and Belgium ) were used ( S1 Table ) . The 120 Chinese strains were collected from human or rats over a 50 year period ( 1958~2008 ) . The 8 reference strains ( 56101 , 56102 , 56103 , 56104 , 56108 , 56166 , 56229 and 56233 ) were isolated between 1915~1966 ( except a Japanese strain without detailed information ) . Serogroup identification of these leptospiral strains was carried out by MAT with 15 Chinese standard serogroup-specific rabbit antisera from the National Institutes of Food and Drug Control , China , representing the most predominantly pathogenic serogroups of Leptospira spp . in China . The serogroup scoring the highest MAT titer of the test stains agglutinating 50% of live leptospiral against a given serogroup-specific rabbit antisera is defined as the presumptive corresponding one . All of the 128 strains were maintained by the National Institute for Communicable Disease Control and Prevention , China . Leptospires were stored long-term at −70°C and have been passaged every six months . When needed , they were subcultured at 30°C in 10ml Ellinghausen-McCullough-Johnson-Harris ( EMJH ) liquid medium to stationary phase , and genomic DNA was extracted using NucleoSpin Tissue kits ( Macherey-Nagel , Germany ) according to the manufacturer’s protocol . As a reference method of species identification , 16S rRNA gene sequencing was performed as previously described by Morey [18] for all the 128 epidemic strains . A total of 20 accessible Leptospira species reference sequences that represented pathogenic , intermediate pathogenic and non-pathogenic Leptospira species were obtained from GenBank database and Turneriella parva NCTC 11395T and Leptonema illini NCTC 11301T were set as outgroup ( S2 Table ) [16 , 18] . The sequences of all the Leptospira strains in this study and the 20 representative sequences from GenBank were compared using ClustalW multiple alignments . A Neighbor-joining tree was constructed with Mega software version 5 . 10 with a bootstrap value of 1 , 000 . MLST were performed based on 7 housekeeping genes including glmU , pntA , sucA , tpiA , pfkB , mreA and caiB as previously described [19] . PCR was conducted using the following parameters: an initial denature step at 94°C for 5 min , followed by 30 cycles of 94°C for 30 seconds , 46°C for 30 seconds , 72°C for 45 seconds , then 72°C for 10 min . The PCR products were sequenced by ABI PRISM 377 DNA sequencer . Each allele and the allelic profiles ( glum-pntA-sucA-tpiA-pfkB-mreA-caiB ) were submitted to the established internet Leptospira database ( http://leptospira . mlst . net ) to assign the sequence types ( STs ) . eBURST algorithm ( http://eburst . mlst . net/ ) was applied to determine the relationships among STs . Clonal complexes ( CCs ) were defined as multiple STs linked through single locus variants ( SLVs ) when they differed from each other at a single locus and named on the basis of the putative founder ST or the ST associated with the largest number of SLVs in the clonal complex . Singletons are defined as the STs differing at least three alleles from other STs . Phylogenetic analysis were performed using UPGMA by the BioNumerics software version 5 . 1 ( Applied Maths , Kortrijk , Belgium ) . Furthermore , multiple concatenated sequences of 7 housekeeping alleles were performed using CLUSTALW software and Phylogenetic analysis was conducted with MEGA 5 . 10 [20] . The Neighbor-joining tree was constructed using bootstrapping at 1 , 000 bootstrap replications . To explore the genetic diversity and evolutionary relationship between the isolates in China and other countries , 121 international isolates previously identified by MLST were added into our analysis ( S3 Table ) [21–23] . Among them , a total of 19 international strains belonging to serogroup Icterohaemorrhagiae from 10 countries , including 5 Chinese isolates in present study , were downloaded from the Leptospira MLST website . In addition , 102 sequence data related to Brazil , Argentina and Russia were obtained from three previous studies [21–23] . All of the 121 international strains are listed in S2 Table . The genetic relationship among the 128 isolates in our lab and the 116 isolates from MLST database and previous studies were further analyzed by a minimum spanning tree ( MST ) analysis using the BioNumerics software version 5 . 1 ( Applied Maths , Inc . , Austin , TX , USA ) . All the 128 strains with the highest agglutinating MAT titer against serogroup Icterohaemorrhagiae of 15 standard serogroup-specific rabbit antisera were confirmed as serogroup Icterohaemorrhagiae in this study . Among the 128 strains , 115 strains were identified as L . interrogans , 11 strains as L . borgpetersenii , and two reference strains isolated from Congo and Zaire as L . kirschneri ( Fig 1 and S1 Table ) . Neighbor-joining trees were constructed for the 128 leptospiral isolates in this study and 20 international representative strains obtained from GenBank database ( Fig 1 ) . Three distinct groups representative of pathogenic , nonpathogenic , and intermediate Leptospira species were obtained . Turneriella parva NCTC 11395T and Leptonema illini NCTC 11301T formed a distinct basal out-group branch . Compared to the 20 representative sequences , 115 including 109 Chinese isolates from the five provinces ( Jiangxi , Sichuan , Anhui , Hunan and Anhui ) and 6 international strains isolated from five countries ( Belgium , Denmark , Indonesia , Japan and Sri Lanka ) were identified as pathogenic L . interrogans . Eleven isolates identified as the pathogenic L . borgpetersenii originated from Jiangxi province in China , and the remaining 2 strains isolated from Congo and Zaire were identified as the pathogenic L . kirschneri . All of the 128 isolates were successfully amplified and sequenced ( S1 Table ) . The discriminatory ability for different species ranged from 0 . 11 ST per isolate for L . interrogans to 1 . 0 ST per isolate for L . kirschneri ( Table 1 ) . Among 120 Chinese Leptospira strains , a total of 10 different STs were obtained , 5 of which were represented by multiple strains , while the remaining 5 STs were found as singleton ( Table 2 and S1 Table ) . The most predominant ST in China was ST1 ( 69/120 ) , followed by ST128 ( 18/120 ) , ST17 ( 17/120 ) , ST143 ( 9/120 ) , ST209 ( 2/120 ) and the remaining 5 isolates belonged to 5 different STs , respectively ( Table 2 ) . The most predominant genotype , ST1 , was temporally ( between 1958 and 2008 ) and geographically diverse ( 4 provinces distributed in Sichuan , Jiangxi , Anhui , Hunan ) . Furthermore , the distributions of STs in China were associated with special geographic regions . For example , ST17 , the second most frequent serotype , was found in Sichuan and Jiangxi provinces between 1969~2008 and ST128 was just found in Hunan province in 2007 . In addition , ST143 and ST209 were found in Jiangxi province between 2005~2007 . It was interesting that only ST143 and ST209 corresponded to L . borgpetersenii and all the other STs corresponded to L . interrogans in China . Whereas eight different STs were identified among 8 international strains , only ST17 was found in Chinese Leptospira isolates ( Table 2 and S1 Table ) . eBURST analysis based on the allelic profiles was first conducted to identify relationships among 10 Leptospira STs found in the 120 Chinese pathogenic strains . Clonal complexes ( CCs ) based on ST Linkages were built using the criteria of at least five shared alleles . Two CCs ( CC1 and CC143 ) and 5 singletons were identified , including the largest CC1 and the largest singleton CC17 ( S1 Fig and S1 Table ) . The CC1 and 5 singletons belonged to L . interrogans , whereas CC143 belonged to L . borgpetersenii . The CC1 contained 69 ST1 strains , 18 ST128 strains and 1 ST98 strain with ST1 being the most likely ancestral strain of this CC . The CC143 , including 9 ST143 strains and 2 ST209 strains , showed no predicted founder type . eBURST analysis has confirmed that there is no coexistence of different species within the same CCs . On the other hand , the relationships between the 10 STs representing 120 Chinese strains were depicted in a UPGMA dendrogram . Three main clades ( Clade1-3 ) were generated and the remaining isolates were dispersed as unrelated singletons ( Fig 2 ) . The UPGMA dendrogram revealed ST clustering patterns relatively similar with eBURST analysis ( Fig 2 and S1 Fig ) . The three clades in UPGMA dendrogram corresponded to the CC1 , CC143 and one Singleton CC17 in eBURST dendrogram , respectively . The rest of the strains were dispersed as unrelated singletons like the ones in eBURST dendrogram . The Clade2 corresponding to CC1 was found among four provinces ( Sichuan , Jiangxi , Hunan and Anhui ) and no relationship was observed between the isolates . Furthermore , the UPGMA dendrogram had shown a geographical relationship between the isolates and STs . For instance , The Clade3 corresponding to CC143 contained 11 ST143 and 2 ST209 strains isolated from Jiangxi province , The Clade1 corresponding to singleton CC17 contained 17 ST17 strains from Sichuan and Jiangxi provinces between 1969~2008 and one SLV of ST128 in Clade2 contained 18 strains isolated from Hunan province . Besides the 128 strains in this study , additional 116 serogroup Icterohaemorrhagiae isolates with diverse geographical regions or countries from MLST database and other studies were also added to perform MLST analysis . However , among the finally identified 22 STs from these 244 strains , only ten STs were found in China . The eBURST analysis revealed five CCs and 8 singletons ( S2 Fig ) . CC17 remained to be the most predominate CC which covered 125 strains corresponding to three different STs ( ST17 , ST199 and ST206 ) and followed by the CC1 including 89 strains corresponding to another three STs ( ST1 , ST128 and ST98 ) . The third largest CC143 included 11 ST143 strains and 2 ST209 strains isolated from China . ST1 and ST17 were defined as the predicted founders of CC1 and CC17 , respectively . The remaining three CCs comprised relatively dispersed STs with no predicted founding type . For instance , CC38 included two relatively distant STs: ST203 and ST38 . The geographic distribution and corresponding STs among the 244 international Leptospira isolates are listed in Table 3 . Generally close clustering of these strains from same geographical regions was observed . For example , 9 ( ST1 , ST92 , ST98 , ST128 , ST143 , ST209 ST201 , ST202 and ST203 ) of 10 STs were found exclusively in China between 1958~2008 . And all of the Brazil , Argentina and Belgium isolates belonged to ST17 , all nine Russia isolates and two Denmark isolates were clustered together into CC17 . The remaining isolates from Japan , Malaysia , Sri Lanka , and Indonesia were classified as relatively independent singletons . Therefore , ST17 was found as one of the most common STs worldwide , including in Asia ( China , Japan ) , Latin America ( Brazil and Argentina ) and Europe ( Denmark , Belgium and Russia ) between 1915~2009 . At the same time , the 244 strains were further analyzed by minimum spanning tree ( MST ) . Five CCs ( CC1 , CC17 , CC143 , CC38 and CC65-122 ) were generated and the remaining isolates were dispersed as unrelated singletons ( Fig 3 ) . The MST dendrogram showed relatively similar ST clustering patterns with eBURST analysis ( Fig 3 and S2 Fig ) . In some cases , isolates within same CCs were generally restricted to one or several countries . For instance , the CC1 and CC143 only contained these Chinese strains which had closely genetic relationship but were distant from all other isolates , whereas , the other three CCs included isolates from more than one country . For example , The CC17 included 120 clustered isolates from Asia ( China , Japan ) , Latin America ( Brazil and Argentina ) and Europe ( Denmark , Belgium and Russia ) . CC38 included 3 clustered isolates from China and Sri Lanka . The CC65-122 included 6 clustered isolates from Africa ( Zaire , Congo ) and Latin America ( Jamaica ) corresponding 4 different STs . The remaining isolates were dispersed as singletons . Based on the MST dendrogram , more than half of Chinese strains were clustered into three large CCs: CC1 ( 88/120 ) , CC143 ( 11/120 ) and CC17 ( 17/120 ) . The remaining 4 isolates from China were dispersed as 4 independently singletons . As seen in Table 3 and Fig 3 , the genetic diversity of Leptospira strains belonging to serogroup Icterohaemorrhagiae from China was generally different from that of isolates elsewhere . From the global population , no common CCs with potential founders were identified as a whole distribution , indicating high diversity of STs . Based on seven MLST housekeeping genes , Neighbor-joining trees were constructed with three distinct clusters corresponding to three different Leptospira species ( Fig 4 ) . The L . interrogans cluster containing 6 international strains and 109 Chinese strains . The L . borgpetersenii cluster containing 11 strains were further divided into two sub-groups that originated from Jiangxi province between 2005~2007 . In addition , the L . kirschneri cluster containing 2 strains originating from Congo . Phylogenetic analysis revealed relatively similar species clustering patterns with 16S rRNA gene sequencing . Among the 120 Chinese strains in this study , 31 isolates with previously confirmed serovar information were utilized to investigate the relationships between serovars and STs ( S4 Table ) . It was found that there were some isolates in same STs generally corresponding to two or more different serovars . 12 STs contained strains in a single serovar ( S4 Table ) . However , for the first time , we reported that ST1 strains distributed among 4 serovars: Lai , Naam , Liangshan and Honghe , and similarly ST17 corresponded to serovar Icterohaemorrhagiae , Lai , Copenhageni , Renshou and Smithi . In addition , interestingly , some serovars were also found to correspond to multiple STs . For example , serovar Copenhageni was found among three STs-ST17 , ST199 and ST201 . Serovar Honghe was also associated with ST1 , ST92 , ST98 and ST203 . Serovar Lai was associated with ST1 and ST17 and serovar Naam was associated with ST1 and ST23 . These observations have shown that the relationship between serovars and STs was highly complicated , suggesting serovar classification as a poor indicator of genetic relatedness . Although the incidence of leptospirosis has significantly decreased in the past few years , leptospirosis is still considered as an important zoonosis in China . Since 2004 , leptospirosis was routinely included in the national epidemic surveillance system that included systematic case reporting and the monitoring efforts aimed at environmental and host animal populations such as pigs , dogs , cattle and rats . The southern provinces of Mainland China had the highest leptospirosis prevalence rates in recent years . A recent report indicated that serogroup Icterohaemorrhagiae has been historically the most prevalent serogroup associated with human and animals outbreaks in China [17] . MAT has been performed only in a limited number of reference laboratories , primary due to the requirement of long-term maintenance of large range of reference strains and serogroup or serovar-specific standard anti-rabbit sera . In addition , pathogenic Leptospira spp . include more than 230 serovars , the majority of them have no corresponding specific antisera and cannot be identified by MAT . On the contrary , MLST has a higher discriminatory power among Leptospira spp . and is widely used for bacterial genotyping [24] , including Leptospira [12 , 13 , 19 , 25] . 16sRNA sequencing used as a tool for phylogenetic analysis has led to a better understanding of evolution of Leptospira . These two techniques can be directly applied to biological ( serum , urine or blood of maintenance hosts and human ) and environmental samples . Furthermore , MLST is also supported by an updated website at http://leptospira . mlst . net/ , which helps to exchange of new information among laboratories or countries . This would allow for epidemiological studies in some laboratories where they are not able to culture Leptospira spp . So far , no detailed studies focusing on the major prevalence and the genetic characterization of leptospirosis disease are available . To investigate the genetic diversity of leptospirosis , a total of 120 Chinese strains and 8 international reference strains belonging to serogroup Icterohaemorrhagiae were analyzed using 16S rRNA gene sequencing and MLST analysis . These isolates were primarily obtained from leptospirosis patients , or a wide range of rodent sources from five major provinces known to have a high incidence of leptospirosis in China . All the 120 strains in this study were differentiated effectively as indicated by clustering patterns ( Fig 1 ) . Two different pathogenic species of L . interrogans , L . borgpetersenii were identified , which was in agreement with previous studies in China [17 , 26] . Among the 120 Chinese isolates , L . interrogans accounted for 90 . 83% ( 109/120 ) ; this has been the predominant pathogenic species of leptospirosis in China over the last fifty years ( 1958–2008 ) . These findings were in agreement with previous studies conducted in Guizhou province [27 , 28] . 9 . 17% ( 11/120 ) strains isolated in Jiangxi province between 2006~2007 in China were identified as L . borgpetersenii . One previous report found that Icterohaemorrhagiae was the serogroup in 51 L . interrogans and L . kirschneri strains isolated from a variety of sources and geographical areas in France [25] . In addition , 43 L . interrogans were uncovered in three outbreaks in Brazilian urban centers [29] . Serovar Copenhageni accounted for 87% of L . interrogans cases in another large urban outbreak in Brazil [30] . The predominant pathogen species isolated in Mayotte were L . borgpetersenii and L . kirschneri [31] . Thaipadungpanit et al . in 2007 had demonstrated that ST34 , corresponding to L . interrogans serovar Autumnalis , accounted for 76% of isolates in 101 L . interrogans isolates in Thailand [12] . Together , these data revealed that the major Leptospira species studied here from different counties were distinct and that the great genetic diversity in geographic epidemiology shown by these isolates reflected this observation . [13 , 19 , 23 , 25] . When compared with L . borgpetersenii , the two species of L . interrogans and L . kirschneri seem to have more closely related to one another and probably evolved from the L . noguchii clade . Close phylogenetic relationships between L . interrogans , L . kirschneri and L . noguchii were reported by Ahmed et al . based on MLST phylogenetic analysis [13] . Furthermore , the UPGMA dendrogram revealed relatively similar ST clustering patterns with eBURST analysis , 2 CCs ( CC1 and CC143 ) and 5 singletons were clustered in 120 Chinese strains ( Fig 2 and S1 Fig ) . CC1 consisted of 3 different STs ( ST1 , ST128 and ST98 ) from diverse sources over the past 50 years in China , with ST1 as the likely founder . Our results were similar with those of previous studies performed in Guizhou province [32] . The predominant serotype in China , ST1 , was widely distributed ( 37 . 68% ( 26/69 ) Sichuan province; 28 . 99% ( 20/69 ) Jiangxi province; 28 . 99% ( 20/69 ) Anhui province and 4 . 35% ( 3/69 ) Hunan province ) . The host ranges were 68 . 12% ( 47/69 ) in Apodemus agrarius , 13 . 04% ( 9/69 ) in human , 13 . 04% ( 9/69 ) in Rattus rattoides and 13 . 04% ( 9/69 ) in Rattus norvegicus during the 1958~2008 collection time span . In addition , ST17 , widely distributed in Sichuan and Jiangxi provinces , was the second most common serotype isolated during this time period . In general , the predominant serotypes , ST1 and ST17 , having distinct sources yet formed a tight group , indicating that there might be one original strain which subsequently diverged evolutionarily into the two STs above within southern China provinces . The remaining 4 singletons of ST92 , ST201 , ST202 and ST203 shared no more than 3 strains , and presumably dispersed independently . The genotyping results from this study showed that Apodemus agrarius could be a main source of leptospirosis transmission in China . MLST is also useful to explore the transmission of specific species between maintenance animal hosts and human . Therefore , it may be useful to implement control strategies for Apodemus agrarius to reduce the transmission from animals to humans . Interestingly , although serogroup Icterohaemorrhagiae strains were found in most Leptospira endemic regions in China and some STs such as ST1/ST17 were widely distributed in this study , there were some prominent serogroups consisting of more than one ST/species in specific regions . For example , CC143 , belonging to L . borgpetersenii , comprised of 11 strains isolated from Rattus rattoides and Rattus norvegicus . CC143 was locally confined to Jiangxi province between 2006~2007 in China . One SLV of ST128 comprised of 18 strains belonging to L . interrogans , and was isolated from Apodemus agrarius and Rattus rattoides hosts in Hunan province in 2007 . Therefore , some clustering of strains from specific geographical regions was observed in China . The strains isolated from Jiangxi and Hunan provinces were from the same monitoring sites , respectively , suggesting that the isolates may have an epidemiological link in that given locale . This also gives a clue that the MLST scheme is capable of dissecting the molecular geographic epidemiology of leptospirosis . No other obvious epidemiological relationship was found between STs and source specimens or isolated locations in these 120 Chinese strains . These results were not surprising because these isolates were epidemiologically unrelated and showed a great diversity in STs; no clustering was detected . More isolates and molecular typing data are needed in order to better understand the epidemiology of leptospirosis in China . Basis on the genotyping results in this study , certain Leptospira genotypes are prevalent in a particular geographical region and associated with special animal reservoirs . The diverse distributions of genotypes may provide a clue for species-specific vaccine preparation to increase the efficacy of a vaccination program in different epidemic regions . This information may also be useful for tracking of the source of leptospirosis outbreak and to establish a control program against leptospirosis in each region . In order to explore the global genetic diversity and evolutionary relationships in the serogroup Icterohaemorrhagiae strains worldwide , a total of 244 serogroup Icterohaemorrhagiae strains from 13 different countries were analyzed and 22 STs were found . The MST dendrogram revealed relatively similar ST clustering patterns with eBURST analysis; five CCs ( CC1 , CC17 , CC38 , CC143 and CC65-122 ) and 8 singletons were clustered in 244 international strains ( Fig 3 and S2 Fig ) . CC1 and CC143 were the dominant clones in China; these two CCs shared a close genetic relationship and were distant from all the other global isolates . The other 3 CCs , on the other hand , included isolates from more than one country . The predominant ST recovered in Asia , Latin America and Europe between 1915~2009 was ST17 . Furthermore , the remaining isolates were dispersed in 8 unrelated singletons . In addition , the eBURST and MLST analyses revealed that the genetically diverse species/strains of serogroup Icterohaemorrhagiae isolates from China was generally different from those isolated in other countries belonging to that particular serogroup . The remaining 9 STs were found in China exclusively , which may indicate that Leptospira may evolve according to different locations and the epidemiology of leptospirosis in China is relatively independent from other countries . This also indicated that MLST is a useful technique to explore the genetic diversity and molecular epidemiology of leptospirosis on a global and/or historical scale . What is more , Thaipadungpanit et al . had applied MLST typing scheme to 101 L . interrogans isolates and 12 STs were identified in Thailand in 2007 . Among the 12 STs found , ST34 , corresponding to L . interrogans serovar Autumnalis , accounted for 76% of isolates [12] . Moreover , Caimi et al . demonstrated that ST37 corresponded to two serogroups of Pomona and Canicola , and was the most frequent genotype in 18 isolates in Argentina . All the 3 serogroup Icterohaemorrhagiae strains isolated between 1993~2005 were identified as ST17 [21] . Among 11 serogroup Icterohaemorrhagiae strains in Russia , four STs ( ST17 , ST199 , ST23 and ST206 ) were found [22] . It was previously reported that Icterohaemorrhagiae was the most prevalent serogroup in Brazil [23 , 33] , and all the 90 serogroup Icterohaemorrhagiae strains isolated between 1986~2009 in the state of Sao Paulo were identified as ST17 [23] . In all , it was indicated that the predominant serogroups or STs were different in different geographical regions of the world . Whereas , ST17 was the most predominant ST in serogroup Icterohaemorrhagiae in Argentina , Russia and Brazil and ST1 was the most frequent ST in serogroup Icterohaemorrhagiae in China irrespective of the scattered spatial and geographic distribution . These predominant isolates are likely to have adaptive selective advantages in the environment or in maintenance hosts , allowing them to develop into pathogenic strains . Based on concatenated sequences of the 7-locus MLST scheme , 128 strains were differentiated effectively into three distinct clusters corresponding to three species , L . interrogans , L . kirschneri and L . borgpetersenii ( Fig 2 ) by Phylogenetic analysis . This is consistent with previous studies that MLST allowed differentiation of the major pathogenic species of Leptospira [13 , 19 , 23 , 25] . The Neighbor-joining tree revealed the phylogenetic relationship between these three different pathogenic species in this study and had shown that two pathogenic species of L . interrogans and L . kirschneri seem to be more closely related than L . borgpetersenii , which was also confirmed using 16S rRNA sequencing in this study . The close genetic relationship of L . interrogans and L . kirschneri was also confirmed by Boonsilp et al [11] . From the Phylogenetic analysis of MLST data , the Leptospira strains belonging to the same serovars were not clustered together . This was also confirmed in earlier findings [12 , 13 , 19 , 23] . This may be due to horizontal gene transfer . Therefore , the MLST method is an alternative suitable method to identify Leptospira up to genome species level . To explore the relationship between STs and serovars , 31 isolates that had both STs and serovar designations in this study were analyzed . We found that there were some isolates belonged to the same STs , but generally corresponded to different serovars . On the other hand some serovars usually were associated with more than one different ST . These observations have shown that serovars are not suitable indicators of genetic relatedness . The diversity of serovars is most likely to be due to horizontal gene transfer events , leading to differences in sequences . Here our focusing on serogroup Icterohaemorrhagiae strains of Leptospira using MLST analysis and 16sRNA gene sequencing as a tool for phylogenetic analysis has led to a better understanding of evolution of Leptospira . MLST provides evidence that the diversity of STs among the serogroup Icterohaemorrhagiae strains is very high in China . The result may be useful to develop a strategy and/or guidelines for the control of leptospirosis in China . However , phylogenetic analysis of more globally dispersed Leptospira strains is necessary; we nonetheless believe that our present study provides a blueprint for further phylogenetic research . More convenient molecular techniques have to be developed to identify and characterize Leptospira species and STs .
Leptospirosis , caused by pathogenic Leptospira spp , is a globally widespread zoonosis . In this study , our focusing on serogroup Icterohaemorrhagiae strains of Leptospira using MLST as a tool for phylogenetic analysis that has led to a better understanding of evolution of Leptospira . This totally consisted of 120 epidemic strains from five geographically diverse regions were isolated over the past 50 years in China and 8 strains from seven different countries . 17 STs were identified in these 128 strains by MLST analysis . Adding 116 serogroup Icterohaemorrhagiae in the Leptospira MLST database and studies previously described , 22 STs were identified in the 244 isolates . The genetic diversity of Leptospira belonging to serogroup Icterohaemorrhagiae from China was generally different from that of isolates elsewhere . Results of the 16S rRNA sequencing typing and MLST genotyping method were nearly consistent . Here , MLST revealed the high diversity of STs among the serogroup Icterohaemorrhagiae strains in China . Our present study provides a blueprint for further phylogenetic research . More convenient molecular techniques have to be developed to identify and characterize Leptospira species and STs .
You are an expert at summarizing long articles. Proceed to summarize the following text: Mosaic Variegated Aneuploidy ( MVA ) syndrome is a rare autosomal recessive disorder characterized by inaccurate chromosome segregation and high rates of near-diploid aneuploidy . Children with MVA syndrome die at an early age , are cancer prone , and have progeroid features like facial dysmorphisms , short stature , and cataracts . The majority of MVA cases are linked to mutations in BUBR1 , a mitotic checkpoint gene required for proper chromosome segregation . Affected patients either have bi-allelic BUBR1 mutations , with one allele harboring a missense mutation and the other a nonsense mutation , or mono-allelic BUBR1 mutations combined with allelic variants that yield low amounts of wild-type BubR1 protein . Parents of MVA patients that carry single allele mutations have mild mitotic defects , but whether they are at risk for any of the pathologies associated with MVA syndrome is unknown . To address this , we engineered a mouse model for the nonsense mutation 2211insGTTA ( referred to as GTTA ) found in MVA patients with bi-allelic BUBR1 mutations . Here we report that both the median and maximum lifespans of the resulting BubR1+/GTTA mice are significantly reduced . Furthermore , BubR1+/GTTA mice develop several aging-related phenotypes at an accelerated rate , including cataract formation , lordokyphosis , skeletal muscle wasting , impaired exercise ability , and fat loss . BubR1+/GTTA mice develop mild aneuploidies and show enhanced growth of carcinogen-induced tumors . Collectively , these data demonstrate that the BUBR1 GTTA mutation compromises longevity and healthspan , raising the interesting possibility that mono-allelic changes in BUBR1 might contribute to differences in aging rates in the general population . Separation of duplicated chromosomes during mitosis is an intricate biological process whose molecular basis is incompletely understood . Inaccurate segregation of whole chromosomes results in numerical chromosome aberrations , referred to as aneuploidy [1] . Human aneuploidy is intimately associated with developmental defects and disease pathology [2] . For example , aneuploidy in gametes is a known cause of infertility , miscarriages and congenital birth defects [3] , while somatic cell aneuploidy is a hallmark of cancer , with evidence mounting that aneuploidy can promote neoplastic transformation [4]–[6] . To safeguard against chromosome missegregation , eukaryotic organisms have developed cellular surveillance systems including the mitotic checkpoint ( or spindle assembly checkpoint ) and the attachment error correction machinery [7] . The mitotic checkpoint is a multi-protein network that inhibits sister chromatid separation until all chromosomes are properly attached to the mitotic spindle [8] . One of the core components of this checkpoint is BubR1 , a modular protein that acts to inhibit the activity of the large multi-protein E3 ubiquitin ligase known as the anaphase promoting complex/cyclosome ( APC/C ) , by binding to the co-activating subunit Cdc20 [9] . Once all chromosomes have achieved bi-orientation , BubR1 dissociates from Cdc20 leading to the polyubiquitination of securin and cyclin B1 , two inhibitors of separase , a protease that initiates anaphase by cleaving cohesin rings that physically join duplicated sister chromosomes together . BubR1 not only promotes accurate chromosome segregation in its role as a Cdc20 inhibitor , but also acts to stabilize microtubule-kinetochore attachments [10] . BUBR1 mutations have been identified in various human malignancies , including gastrointestinal cancers [11]–[14] , and in a rare human hereditary condition called mosaic variegated aneuploidy ( MVA ) syndrome , in which high rates of chromosome missegregation lead to systemic aneuploidy , typically involving more than 25% of cells [15] , [16] . MVA syndrome has clinically heterogeneic features , including growth deficiency ( with prenatal onset ) , mental retardation , microcephaly , facial dysmorphisms , cataracts and other eye abnormalities , short lifespan , and increased risk for childhood cancers such as rhabdomyosarcoma , Wilms' tumor and leukemia [17] . MVA patients with BUBR1 mutations fall into two groups: bi-allelic and mono-allelic mutations . Patients with bi-allelic mutations carry an allele that results in premature truncation of BubR1 protein or an absent transcript , and an allele with a missense mutation often located within the kinase domain [15] . Patients with mono-allelic mutations have either a nonsense or missense mutation combined with a non-mutated allelic BUBR1 variant that expresses low amounts of wildtype BubR1 protein [16] , [17] . BubR1 protein levels are usually very low in patients with BUBR1 mutations , even in those with a missense mutation , largely because mutant BubR1 proteins produced by these alleles tend to be quite unstable [18] . Gene knockout studies in mice support the idea of a causal relationship between BubR1 insufficiency and tumorigenesis . Although homozygous BubR1 knockouts die as pre-implantation stage embryos , heterozygous knockouts are viable and show increased tumor formation when challenged with a carcinogen [19]–[21] . BubR1 depletion beyond the level of heterozygous knockout mice was achieved by the use of knockout ( BubR1− ) and hypomorphic ( BubR1H ) alleles [21] . Mice with one hypomorphic and one knockout allele ( BubR1−/H mice ) express about 4% of normal BubR1 protein levels and die at birth from respiratory failure [21] . However , mice with two BubR1 hypomorphic alleles ( BubR1H/H mice ) that produce around 10% of normal BubR1 protein levels are viable . Like MVA patients , these mice show systemic near-diploid aneuploidy , premature chromosome separation ( PCS ) and are susceptible to tumorigenesis [21] , [22] . However , unlike other mouse models for aneuploidy that have similar features [1] , BubR1H/H mice have a very short lifespan and develop several progeroid phenotypes at a very young age ( within 3 to 5 months ) , including growth retardation ( dwarfism ) , facial dysmorphisms , cataracts , muscle wasting , lordokyphosis ( rearward curvature of the spine ) , fat loss , and cardiac arrhythmias [21] , [23] . This , together with observations that BubR1 protein levels decrease during natural aging in various mouse tissues , raises the possibility that BubR1 is an important regulator of aging [21] , [24] . MVA syndrome has been documented as a hereditary cancer syndrome [15]–[18] , [25]–[27] , but could potentially be classified also as a progeroid syndrome based on its phenotypic resemblance to BubR1 progeroid mice , which includes short lifespan , dwarfism , facial dysmorphisms , and cataract formation . However , whether MVA patients have additional age-related phenotypes observed in BubR1 hypomorphic mice such as fat loss , muscle wasting and cardiac arrhythmias is a key open question that has been difficult to address , largely because MVA syndrome is very rare and because most patient die very young . While patients with the MVA disorder are rare , heterozygous carriers of BUBR1 mutant alleles are expected to be much more prevalent in the general population . If these mutations were to affect healthspan or longevity , or both , this might provide a genetic basis for why certain people develop particular age-related traits at faster rates . Little is known about the health status of parents of MVA patients . They seem to have a minor mitotic phenotype as evidenced by their predisposition to PCS , but whether they are at risk for any of the pathologies associated with MVA syndrome or progeria is unknown [28]–[30] . To address this question and to better understand the relationship between MVA syndrome and progeria , we engineered mice to carry the human MVA BUBR1 nonsense mutation 2211insGTTA [15] . We demonstrate that mice harboring this heterozygous BubR1 MVA mutation have a reduced lifespan and exhibit acceleration of various early age-related features . In addition , reduced BubR1 protein levels in BubR1+/GTTA mice results in mild aneuploidy and increases carcinogen-induced tumor growth . These findings suggest that mono-allelic BUBR1 mutations might contribute to accelerated aging and reduced longevity , further supporting the idea that MVA syndrome is a progeria-like syndrome . Additionally , we provide important experimental evidence for the longstanding concept that variations in select genes accelerate the rate of age-related deterioration of certain tissues and organs . Human BUBR1 encodes a modular 1052 amino acid serine/threonine protein kinase that is highly conserved among mammalian species ( Figure 1A ) . Two of the four MVA patients with bi-allelic BUBR1 mutations reported by Hanks and colleagues [15] carry a nonsense mutation referred to as 2211insGTTA that results in a truncated protein that lacks the kinase domain ( Figure 1A ) . Because of its prevalence , we mimicked this mutation in mice to understand the potential physiological consequences of MVA mutations in a heterozygous state . Using homologous recombination in embryonic stem ( ES ) cells , we inserted a GTTA sequence in the murine BubR1 gene at position 2178 . This position corresponds to nucleotide 2211 of the human BUBR1 transcript ( Figure 1A and 1B ) . We injected correctly targeted ES clones into blastocysts and obtained chimeric mice . These chimeras successfully transmitted the mutated BubR1 allele ( referred to as BubR1NEO;GTTA ) to their offspring ( Figure 1B and 1C ) . BubR1+/NEO;GTTA mice were crossed to protamine-Cre transgenic mice to remove the NEO gene cassette ( Figure 1B and 1D ) . BubR1+/GTTA mice were obtained at the expected frequency and were overtly indistinguishable from control littermates ( data not shown ) . Western blot analysis demonstrated that mouse embryonic fibroblasts ( MEFs ) derived from BubR1+/GTTA mice had reduced amounts of wildtype BubR1 protein ( Figure 1E ) . The level of reduction was similar to that observed in BubR1+/− MEFs ( Figure 1E and Figure S1 ) . The predicted 730 amino acid truncated protein encoded by the BubR1GTTA allele was undetectable , even after long exposure times ( Figure 1E ) , which is consistent with recent observations in cultured skin fibroblast of an MVA patient carrying the BUBR1GTTA allele [18] . Western blot analysis of testis extracts from BubR1+/GTTA and wildtype mice confirmed the reduction of BubR1 in cultured MEFs ( Figure 1F ) . One plausible explanation for the absence of BubR1GTTA encoded protein on western blots is that nonsense mutations tend to produce transcripts that are subject to non-sense mediated mRNA decay [13] , [16] , [18] . Alternatively , the truncated protein may be unstable and subject to rapid proteosomal degradation . Patients with MVA syndrome show systemic near-diploid aneuploidies resulting from an inability to separate duplicated chromosomes with high accuracy during mitosis [15] , [16] , [29] . Although metaphase spreads from heterozygous carriers of MVA BUBR1 mutations exhibit increased rates of PCS , it is unclear whether parents of MVA patients are subject to increased aneuploidization [15] , [16] , [26] , [31]–[34] . To examine the impact of the BubR1GTTA allele on chromosome number integrity , we prepared metaphase spreads of passage 5 BubR1+/GTTA and wildtype MEFs and performed chromosome counts . Aneuploidy rates of wildtype MEFs were on average 12% ( Figure 1G ) , which is consistent with rates reported in previous studies [35]–[37] . Aneuploidy rates in BubR1+/GTTA MEFs were significantly increased by 6% , although it should be noted that this escalation is relatively small compared to that previously observed in BubR1H/H MEFs [21] ( Table S1 ) . Consistent with this , BubR1H/H MEFs exhibited a more profound reduction in wildtype BubR1 protein levels than BubR1+/GTTA MEFs ( Figure 1E and Table S1 ) . The incidence of PCS was also significantly higher in BubR1+/GTTA MEFs than in wildtype MEFs ( Figure 1G ) , but again not as high as in BubR1H/H MEFs [21] . Collectively , these data indicate that the BubR1GTTA allele that we created in mice faithfully mimics its human counterpart , and demonstrate that BubR1+/GTTA mice have relatively mild , but significant , mitotic phenotypes . BubR1H/H mice exhibit various aging-related phenotypes by 3 to 4 months of age [21] ( Table S1 ) . BubR1+/GTTA mice , however , remained overtly indistinguishable from control littermates during this time period . To examine potential late life phenotypes associated with the GTTA mutation , we established large cohorts of BubR1+/GTTA and wildtype mice , which we monitored for signs of ill health and the development of overt age-related phenotypes . Kaplan-Meier overall survival curves of these cohorts showed that the GTTA mutation significantly reduces median and maximum lifespan , with BubR1+/GTTA mice having a median lifespan of 93 weeks compared to 102 weeks for wildtype mice ( Figure 2 ) . By comparison , BubR1H/H mice had a significantly shortened median survival of only 30 weeks ( Figure 2 ) . BubR1H/H mice develop quite severe cardiac arrhythmias that are thought to be the primary cause of premature death of the animals [38] ( Table S1 ) . This prompted us to test whether reduced lifespan of BubR1+/GTTA mice might be due to cardiac problems . However , the frequency of cardiac arrhythmias in BubR1+/GTTA mice was not elevated ( Figure S2A ) . Subsequent cardiac stress tolerance tests , in which a lethal dose of the β-adrenergic agonist isoproterenol was injected and the time to death measured [39] , further indicated that the cardiac performance of BubR1+/GTTA mice is not compromised ( Figure S2B ) . A prominent phenotype of BubR1 hypomorphic mice is lordokyphosis [21] . The underlying condition here is skeletal muscle deterioration rather than osteoporosis [23] . BubR1+/GTTA and wildtype mice in our cohorts were biweekly monitored for this phenotype . Wildtype mice are known to develop lordokyphosis as part of their normal aging process [40] , a finding commonly attributed to a combination of muscle wasting and osteoporosis [41] , [42] . While mice in both our cohorts indeed developed lordokyphosis with aging , the median onset of this age-related phenotype was markedly accelerated in BubR1+/GTTA mice ( 89 weeks versus 116 weeks; Figure 3A ) . To determine whether this acceleration might be due to early muscle degeneration , we sacrificed 15-month old BubR1+/GTTA and wildtype mice and performed muscle fiber diameter measurements on cross sections of the gastrocnemius , paraspinal and abdominal muscles . As shown in Figure 3B , average muscle fiber diameters of BubR1+/GTTA mice were significantly reduced in all three muscle groups . No such reductions were observed in 3-month-old BubR1+/GTTA mice ( Figure S3 ) . Using qRT-PCR we analyzed the gastrocnemius of aged BubR1+/GTTA and wildtype mice for expression of p16Ink4a and p19Arf and found that both senescence markers were expressed at elevated levels [23] , [38] , suggesting that senescent cells might contribute to accelerated muscle degeneration in BubR1+/GTTA mice ( Figure 3C ) . Furthermore , 15-month old BubR1+/GTTA mice had similar bone mineral densities and contents as wildtype mice as measured by dual energy x-ray absorptiometry ( DEXA; Figure S4 ) , demonstrating that the early kyphosis is not due to accelerated osteoporosis . To assess whether early muscle wasting resulted in decreased muscle function , we performed treadmill exercise tests on BubR1+/GTTA and wildtype mice at various ages [38] , [43] . As shown in Figure 3D–3F , wildtype mice showed similar exercise ability at 3 and 15 months of age . However , while BubR1+/GTTA mice showed normal exercise ability at 3 months , the duration of exercise , distance travelled and overall amount of work performed were all significantly decreased at 15 months . A second early aging-associated phenotype of BubR1 hypomorphic mice is cataract formation [21] . MVA patients are also prone to cataracts , as well as other eye anomalies [16] , [29] . Our biweekly inspections of BubR1+/GTTA and wildtype mice revealed that cataract formation was significantly accelerated in BubR1+/GTTA mice , with 50% of BubR1+/GTTA mice having cataracts at 101 weeks versus 116 weeks for wildtype mice ( Figure 4A ) . Affected lenses of both BubR1+/GTTA and wildtype mice had nuclear cataracts , as revealed by histological evaluation ( Figure 4B ) . Similar to BubR1H/H lenses [21] , BubR1+/GTTA lenses exhibited posteriorly located epithelial cells ( Figure 4B and 4C ) , although Morgagnian globules , which are a distinguishing feature of hypomorphic lenses , were not detected . In contrast , posterior epithelial cells were rarely observed in wildtype lenses . Taken together , the above data demonstrate that skeletal muscle degeneration and cataract formation , two hallmarks of chronological aging in humans [44] , are accelerated in mice carrying the BubR1 GTTA mutation found in human MVA syndrome . In humans , the amount of fat tissue increases during middle age but then decreases at advanced age [45] . Furthermore , during and after middle age , fat redistributes from subcutaneous to intra-abdominal visceral depots . In turn , these deposits shrink in the elderly as a result of fat redistribution to bone marrow , muscle , and liver [45] . Previous studies have demonstrated that BubR1H/H progeroid mice prematurely lose fat from various depots and the subdermal adipose layer [38] , [46] , [47] . To probe for premature changes in fat mass and redistribution in BubR1+/GTTA mice , we measured the overall amount of fat ( using DEXA scanning on live animals ) , the mass of various fat deposits , the subdermal adipose thickness , and the fat cell size of 15- and 24-month-old BubR1+/GTTA and wildtype males . Body weight , total fat mass , and weights of major fat depots , including inguinal adipose tissue ( IAT ) , subscapular adipose tissue ( SSAT ) and mesenteric adipose tissue ( MES ) , were all normal in 15-month-old BubR1+/GTTA mice ( Figure 5A–5C ) . All these values remained unchanged in 24-month-old wildtype mice . In contrast , however , body weight , percentage of body fat , and total fat mass of BubR1+/GTTA were all significantly reduced at 24 months . In addition , several fat depots shrank significantly , including IAT and brown fat , while SSAT and MES were trending downward . Histological analysis showed that the average diameter of fat cells in IAT of BubR1+/GTTA mice declined significantly between 15 and 24 months ( Figure 5D ) . Fat cell size also decreased in wildtype mice , but to a lesser extent than in BubR1+/GTTA mice . Cross sections prepared from lateral skin from 15- and 24-month-old BubR1+/GTTA and wildtype males revealed a dramatic decline in subdermal adipose layer thickness with aging selectively in BubR1+/GTTA males ( Figure 5E ) . In contrast , dermal layer thickness did not decline ( Figure S5 ) . Loss of fat tissue in BubR1H/H mice is , at least in part , due to accumulation of senescent cells [38] , [47] . To determine whether accelerated fat loss in BubR1+/GTTA mice might involve cellular senescence , we measured p16Ink4a and p19Arf transcript levels in IAT of 24-month-old BubR1+/GTTA and wildtype mice by qRT-PCR . Levels of both senescence markers were markedly increased in BubR1+/GTTA mice ( Figure 5F ) , indicating that early accumulation of senescent cells contributes to age-related fat loss in heterozygous carriers of the BubR1 GTTA mutation found in MVA patients . MVA patients are prone to tumor formation , including patients with mono-or bi-allelic BUBR1 mutations [15] , [16] . Consistent with this , BubR1H/H mice are highly susceptible to carcinogen-induced tumors , although it should be noted that spontaneous tumor rates are not increased [21] , [23] ( Table S1 ) . Similar results have been reported for mice in which one BubR1 allele has been inactivated [19] . As a first step to evaluate whether BubR1+/GTTA mice might be tumor prone we sacrificed BubR1+/GTTA and wildtype mice at 24 months of age and screened internal organs for overt tumors . This analysis revealed that the incidence of spontaneous tumors was similar for both genotypes ( Figure S6A ) . Analysis of the tumor spectra revealed no significant increases in incidence of individual tumor types ( Figure S6B ) , although there was a trend for increased tumor multiplicity in BubR1+/GTTA mice . To complement these studies , we treated BubR1+/GTTA and wildtype mice with the carcinogen 7 , 12-dimethylbenz ( a ) anthracene ( DMBA ) at postnatal day 5 . The mice were then sacrificed at 4 months and analyzed for lung tumor formation . We found that the tumor incidence and the tumor multiplicity were both very similar in BubR1+/GTTA and wildtype mice ( Figure 6A and 6B ) . Interestingly , however , tumor size was dramatically increased in BubR1+/GTTA mice ( Figure 6C ) . These findings suggest that the BubR1 GTTA mutation has no obvious impact on tumor initiation but can promote growth of established tumors . The finding that BubR1+/GTTA and BubR1+/− MEFs have similar wildtype BubR1 protein levels and the truncated protein encoded by the GTTA allele is expressed at non-detectable levels raised the question whether BubR1+/− mice might be phenotypically similar to BubR1+/GTTA mice . BubR1+/− MEFs show karyotypic similarity to BubR1+/GTTA MEFs in that their aneuploidy rates are also modestly increased [21] . On the other hand , PCS , a hallmark of MVA patients [15]–[18] , [25]–[27] , is elevated in BubR1+/GTTA MEFs but not in BubR1+/− MEFs . In an earlier study , in which survival of BubR1H/H , BubR1+/− , BubR1+/H , and BubR1+/+ mice was analyzed for up to 15 month of age , yielded no difference in survival between BubR1+/− and BubR1+/+ mice [21] . However , a retrospective analysis of survival records of BubR1+/− and BubR1+/+ animals that were maintained until natural death revealed that mono-allelic loss of BubR1 significantly reduces the median lifespan ( 90 weeks compared to 102 weeks for the corresponding BubR1+/+ mice , Figure 7 ) . There was no statistically significant decrease in maximum lifespan . We note that these earlier BubR1+/− and BubR1+/+ cohorts of mice were not analyzed for any age-related phenotypes ( see Table S1 ) . Biallelic mutations in WRN , CSA and CSB , and DNA repair genes such as XPB , XPD and TTD are associated with human diseases that have features of premature aging [48]–[50] . MVA syndrome has some progeroid traits , but unlike the above syndromes has not been widely recognized as a progeroid disorder [17] . Whether the spectrum of age-related phenotypes of MVA patients is broader than reported has been difficult to assess , mainly because MVA patients are very rare and die early [15] , [16] . It is also unknown whether parents of MVA patients are susceptible to any of the pathologies associated with MVA syndrome . We engineered a mouse model to mimic the BubR1 nonsense mutation 2211insGTTA found in MVA patients with bi-allelic BUBR1 mutations and show that these mice have a significantly shorter lifespan and develop several age-related disorders at accelerated rates , including sarcopenia , cataracts , and loss of fat tissue . These findings strengthen the notion that MVA syndrome is a progeroid syndrome , and provide important experimental evidence for the longstanding concept that variations in select genes may affect the rate of age-related deterioration in certain tissues and organs . To our knowledge , accelerated age-related pathologies have not been reported in parents of affected individuals with any of the classical recessive human progeroid syndromes [48]–[51] . Furthermore , while homozygous knockout or mutant mice have been established for most of the implicated genes , including WRN , CSA and CSB , XPB , XPD , and TTD , whether heterozygotes of these models have reduced longevity or a faster than normal onset of age-related functional decline in particular tissues has not been studied in detail [50] , [52]–[56] . Thus , based on the findings presented here , it will not only be important to determine whether heterozygous MVA BubR1 mutations other than 2211insGTTA cause age-related phenotypes in mice , but also to perform similar studies on mice heterozygous for other progeria-associated genes . Three observations suggest that the phenotypes of BubR1+/GTTA mice are likely to be caused by reduced expression of wildtype BubR1 protein without a contribution of truncated BubR1 protein . First , the truncated protein encoded by the GTTA allele is non-detectable by western blotting , indicating that its level of expression is very low . Furthermore , residual levels of wildtype BubR1 protein are similar in BubR1+/GTTA and BubR1+/− MEFs . Second , BubR1+/GTTA and BubR1+/− MEFs have similar aneuploidy rates , suggesting that the extent to which BubR1 is dysfunctional is the same . Third , BubR1+/GTTA and BubR1+/− mice show very similar reductions in median lifespan . It will be important to complement the survival data of BubR1+/− mice with a comprehensive analysis of age-related phenotypes , and new cohorts of BubR1+/− and BubR1+/+ mice are currently being established for this purpose . Previously , we have shown that clearance of p16Ink4a-positive senescent cells from BubR1H/H mice results in attenuation of sarcopenia , fat loss , and cataracts , indicating that accumulation of senescent cells in skeletal muscle , adipose tissue , and eye drives functional decline in these tissues [38] . This , combined with the observation that p16Ink4a and p19Arf transcript levels are elevated in skeletal muscle and fat of BubR1+/GTTA mice suggests that senescence contributes to the accelerated functional decline in these animals . On the other hand , accelerated cataractogenesis in BubR1+/GTTA mice seems to be senescent cell independent ( data not shown ) . Perhaps , the mere accumulation of epithelial cells in the posterior portion of the lens is sufficient to accelerate cataract formation . The main difference between BubR1+/GTTA and BubR1H/H lenses is that the latter have posteriorly located Morgagnian globules [21] , which may be associated with senescence and explain why cataractogenesis is much more accelerated in BubR1H/H than in BubR1+/GTTA mice . The mechanism by which BubR1 insufficiency induces senescence appears to be more complicated than anticipated [21] , [23] , [57] . It is unlikely that aneuploidy represents the primary lesion that drives senescence , mainly because other aneuploidy models with substantially higher aneuploidy rates do not undergo premature senescence and aging [1] , [4] , [6] , [58] . BubR1 is expressed in interphase where it apparently continues to serve as an inhibitor of APC/CCdc20 activity [37] . Consistent with this , recent reports indicate that APC/CCdc20 E3 ubiquitin ligase activity orchestrates key developmental processes in post-mitotic neurons , including dendrite growth and presynaptic differentiation [59] , [60] . These findings raise the interesting possibility that BubR1 insufficiency might lead to unscheduled degradation of APC/CCdc20 substrates in interphase cells , which , in turn , could lead to cellular stresses that engage p16Ink4a and induce senescence . Key progeroid phenotypes of BubR1H/H mice are also observed in BubR1+/GTTA , but are considerably milder , which correlates with less profound BubR1 protein insufficiency ( Table S1 ) . Various phenotypes seem unique to BubR1H/H mice including cardiac dysfunction , dwarfism , facial dysmorphisms , and thinning of the dermis , suggesting that a more extreme level of BubR1 insufficiency is required for their induction . BubR1+/GTTA mice are not prone to spontaneous tumors and show normal tumor incidence and multiplicity when challenged with the carcinogen DMBA . The most straightforward explanation would be that the level of aneuploidization is insufficient to promote neoplastic transformation . Consistent with this , Bub3+/− mice have similarly mild aneuploidy rates as BubR1+/GTTA mice and are also not prone to spontaneous or DMBA-induced tumors [61] . However , a significant feature of DBMA-induced lung tumors of BubR1+/GTTA mice is their large size , indicating that the mutation promotes tumor aggressiveness without impacting tumor initiation . It will be interesting to determine whether individuals carrying the 2211insGTTA mutation are prone to lethal malignancies . Our current study and previous data support the notion that BubR1 protein levels tightly correlate with aneuploidy rates , cancer susceptibility , lifespan and aging-related phenotypes ( Table S1 ) , indicating that BubR1 is a key determinant of healthspan and lifespan and warrants a comprehensive analysis of the health status of parents of MVA patients and relatives that are also heterozygous carriers of the same MVA BUBR1 mutations . In addition , it would be interesting to screen for BUBR1 mutations in the general population , either in an unbiased manner or more selectively in cohorts prone to conditions associated with BubR1 insufficiency in mice , including sarcopenia , cataracts and fat tissue dysfunction . Subsequent characterization of these mutations , for instance for impact on BubR1 protein stability , might lead to the identification of additional BUBR1 variants that influence rates of age-related deterioration in certain tissues and organs . The BubR1GTTA allele was produced by a recombineering based approach [62] . Briefly , a genomic BubR1 gene fragment of 10 kb spanning exons 14–19 was retrieved from BAC #bMQ_294E2 ( 129S7/SvEv ES Cell , Source BioScience ) and transferred into pDTA . Insertion of the GTTA sequence into the exon 17 ( c . 2178_2179 ) was done as follows: a tetracyclin-resistance gene cassette was made by PCR using pKOEZ-40 plasmid as a template ( gift from Dr . Pumin Zhang ) , the tetra-partite forward primer ( 50 bp homology to the target region , GTTA , ClaI , 24 bp homology to the Tet ) 5′-CCTGG TGTTCACAGTATCGCCTACAACTGTTAAAATCCCTACTAGAATTAGTTAATCGATGGTCGA CGGTATCGATAAGCTTGA-3′ and the tri-partite reverse primer ( 50 bp homology to the target region , ClaI , 24 bp homology to the Tet ) 5′-TCCAGCACAGGCATCGGTC GGTCTTCCACAGAAAACTCCGCAAAAGCACTATCGATTTGGATGGTGAATCC GTTAGCGA-3′ . The GTTA-ClaI-Tet-ClaI cassette was inserted into the pDTA-BubR1 ( E14-E19 ) by recombineering . The resulting construct was digested with ClaI and re-ligated for removal of the Tet gene . Next , a loxP-neomycin phosphotransferase II ( neo ) gene-loxP cassette was inserted into the pDTA-BubR1GTTA ( E14-E19 ) construct , 141 bp upstream of exon 17 , using recombineering [62] . The final targeting vector was linearized with MluI and electroporated into TL1 129Sv/E ES cells . Transfectants were selected in 350 µg/ml G418 and 0 . 2 µM FIAU , and expanded for Southern blot analysis using a 1155 bp 5′ external probe on XmnI/XhoI -digested genomic ES cell DNA . The probe was amplified by PCR from 129Sv/E genomic DNA using the following primers: 5′-GCAGAGTATCCTGACAGGTTAAGGCAC-3′ and 5′-CATAATAATTATCCAACCATGAATGATC-3′ . Chimeric mice were produced by microinjection of three independent ES cell targeted clones with 40 chromosomes into C57BL/6 blastocysts . Chimeric males were mated with C57BL/6 females and germline transmission of the BubR1NEO;GTTA allele was verified by PCR analysis of tail DNA from pups with a agouti coat color . By crossing BubR1+/NEO;GTTA mice to protamine-Cre transgenics [63] the NEO cassette was excised . The following primer combinations were used for PCR genotyping of mice used in our studies: primers a ( 5′-TCAGATCTCCTAGAGCTGGGGTTA-3′ ) and b ( 5′-AATTCTAGTAGGGATTTTAA CAGTTG-3′ ) for BubR1GTTA; primers c ( 5′-GTCTTGTCGATCAGGATGATCTG-3′ ) and d ( 5′-GAAGTAGTATTGTTCCTGTGG CAT-3′ ) for BubR1NEO;GTTA . The targeting vector , targeted ES cells as well as BubR1NEO;GTTA mice were sequenced for the presence of GTTA insertion by PCR amplifying the 1 . 1 kb fragment using primers c and d . Note: in humans , 2211insGTTA results into S738fsX753 ( see Mutation 1 , shared by Family 2 and Family 3 [15] ) . In mice , 2178–9insGTTA results in S727fsX750 . Insertion of GTTA followed by ClaI coding sequence , as in our targeting strategy , leads to S727fsX730 . All mice , including BubR1+/− and BubR1+/+ mice used for survival analysis , were on a mixed 129 X C57BL/6 genetic background and housed in a pathogen-free barrier and maintained on a 12 hours dark/light cycle throughout the study . Mice had ad libitum access to food containing 10% fat and were inspected daily . Animals used for survival analysis were mice found dead or sacrificed when moribund . Mice sacrificed at 15 and 24-months of age were screened for lymphomas , carcinomas and sarcomas to assess spontaneous tumorigenesis . Animals sacrificed at 3 , 15 and 24-months for analyses were omitted from survival , lordokyphosis and cataract incidence curves . All animal protocols were reviewed and approved by the Mayo Clinic institutional animal care and use committee . Wildtype and BubR1+/GTTA MEFs were generated and cultured as previously described [46] . MEFs were frozen at passage 2 or 3 and used for experimentation at the indicated passages . At least three wildtype and BubR1+/GTTA lines were used for all experiments . Mitotic MEFs were generated as previously described [36] . Western blot analysis was performed as previously described [64] . Tissue lysates were prepared by first snap freezing the tissue in liquid nitrogen upon sacrifice . Frozen tissue was ground into fine powder with pestle and mortar . 20 mg of tissue powder was suspended in 200 µl lysis buffer ( 0 . 1% NP-40 , 10% glycerol in PBS , plus protease inhibitors ) and vortexed for 10 min at 4°C . After centrifugation at 14000 rpm at 4°C , 150 µl supernatant was transferred to a 0 . 5 ml PCR tube and 150 µl Laemmli lysis buffer was added . The lysate was boiled for 10 min before loading on Tris-HCl Polyacrylamide gel . Blots were probed with antibodies for BubR1 ( BD ) , pH3Ser10 ( Millipore ) and ß-actin ( Sigma ) . Ponceau S stain was used as a loading control . Quantification of BubR1 protein levels in BubR1+/GTTA and BubR1+/− MEF lysates was done as described [65] . Karyotype analysis on P5 wildtype and BubR1+/GTTA MEFs was performed as described [66] . Bi-weekly monitoring for lordokyphosis and cataract incidence was performed as described [23] . Fiber diameter measurements were performed on cross sections of the gastrocnemius and abdominal muscle from 3 and 15-month-old mice according to previously described methods [23] . Dissection of the paraspinal muscle was performed halfway between the front and hind limb , and processed and measured as the other skeletal muscles . The mean was calculated from a total of fifty measurements obtained with a calibrated program ( Olympus MicroSuite Five ) . Measurements of fat cell diameters were performed according to the same method . Measurements of the total thickness of dermis and subcutaneous adipose layer of lateral skin were performed as described [46] . For histological evaluation of cataracts , whole eyes were paraffin embedded , sagittally sectioned through the middle of the lens and stained with hematoxylin and eosin . The number of cells that had migrated past the epithelial bow of the lens was counted as posterior localized epithelial cells . DEXA scanning was used to measure bone mineral density , bone mineral content , percentage of total body fat , lean mass and fat mass . These measurements were done as described [67] . Treadmill exercise tests were performed as described [43] . For isoproterenol stress tests , a lethal dose of isoproterenol ( 680 mg/kg ) was injected in the chest cavity and time to death was recorded . Mice were monitored for cardiac arrhythmias using a Vevo2100 ultrasound system ( Visualsonics ) as described [68] . qRT-PCR analysis on cDNA derived from RNA isolated from various mouse tissues was as described [23] . Mice were tested for DMBA induced tumor formation as previously described [69] .
Aging is the main risk factor for the majority of chronic diseases and the leading cause of death and disability in humans . Humans age at different rates , but the molecular genetic basis underlying this phenomenon remains largely unknown . Efforts to understand how we age have focused on genetic changes that extend lifespan or underlie progeroid disorders . One potential progeroid disorder , MVA syndrome , has been associated with mutations in the mitotic regulator BUBR1 . Although MVA syndrome is rare due to its recessive nature , individuals carrying heterozygous BUBR1 mutations associated with MVA would be much more prevalent . However , whether such carriers are asymptomatic or at risk of developing aspects of MVA syndrome later in life is unknown . To investigate this , we engineered mice to carry an analogous mutation to the human MVA BUBR1 nonsense mutation 2211insGTTA . We find that these mice have a reduced lifespan and develop several age-related phenotypes at an accelerated rate . These findings suggest that bi-allelic integrity of BUBR1 is a key determinant of healthspan and longevity , and provide a conceptual framework for elucidating differences in aging rates among humans .
You are an expert at summarizing long articles. Proceed to summarize the following text: Praziquantel-based mass treatment is the main approach to controlling schistosomiasis mansoni in endemic areas . Interventions such as provision and use of safe water , minimising contact with infested water , disposal of stool in latrines and snail control provide key avenues to break the transmission cycle and can sustain the benefits of mass treatment in the long term . Efforts are also being made to develop a schistosomiasis vaccine which , if effective , might reduce the incidence of re-infection after treatment . However , any interventions deployed need to be acceptable to , and sustainable by , the target communities . In this qualitative study , we investigated the perceptions of six Lake Victoria island communities of Koome , Uganda , about interventions to control Schistosoma mansoni infection and their willingness to participate in Schistosoma vaccine trials . Thirty-two in-depth interviews , 12 key informant interviews and 10 focus group discussions were conducted . Data were analysed using a thematic content approach . Intestinal schistosomiasis was not regarded as a serious health problem because a mass treatment programme is in place . However , the communities lack safe water sources and latrines . Mass treatment with praziquantel , safe water supplies and use of toilets were deemed the most acceptable interventions by the participants . The communities are willing to participate in Schistosoma vaccine trials . Knowledge of a community’s perception about interventions to control schistosomiasis can be valuable to policy makers and programme implementers intending to set up interventions co-managed by the community members . In this study , the views of the Lake Victoria island communities of Koome are presented . This study also provides data to guide further work on alternative interventions such as Schistosoma vaccine trials in these communities . Schistosomiasis affects an estimated 240 million people worldwide and over 90% of all Schistosoma infections are found in sub-Saharan Africa [1] . In Uganda , an estimated four million people are infected with Schistosoma and about 55% of the population is at risk of infection [2] . The Lake Victoria island communities of Koome sub-county Mukono district carry a large burden of intestinal schistosomiasis: in a recent study 52% of the inhabitants had S . mansoni infections detected on a single stool sample and over 70% using a rapid urine antigen test [3] . The morbidity caused by schistosomiasis is chronic and results in a huge socio-economic burden that is often underestimated [4] . The transmission cycle of intestinal schistosomiasis requires contamination of surface water by egg-laden human excreta , specific freshwater snails as intermediate hosts , and human water contact [5] . To break this cycle , existing intervention strategies include treatment with praziquantel , snail control , proper sanitation and provision of safe water supplies . For the interventions to be effective and sustainable , communities need to be provided with adequate health education [6–8] . Periodic mass treatment of communities with praziquantel is the most widely used approach to control schistosomiasis [9] with numerous gains reported [10 , 11] . Although effective , it does have its drawbacks . Praziquantel does not kill immature schistosomes [12] and therefore does not clear all infection with single treatment , especially in individuals with high intensity infection [13 , 14] . Since praziquantel does not prevent re-infection , the treatment must be provided repeatedly on a regular basis and the side effects can be unpleasant [15] . Due to these drawbacks and other factors such as poor drug coverage and poor drug compliance , control of schistosomiasis by mass treatment with praziquantel has not been consistent in the long-term [11 , 16] . Therefore , effective long-term control of schistosomiasis by praziquantel mass treatment will rely on modifying the other components that facilitate infection transmission [17] . Without those additional interventions , re-infection rates will likely remain very high [18 , 19] . However , a vaccine against Schistosoma could potentially overcome the challenges posed by mass treatment [17]; but currently none is commercially available [20] . Various antigens and vaccine candidates have been proposed [21] and some are investigated in clinical trials [22 , 23] but none is yet readily approved and used for the public . A long-lasting intervention against schistosomiasis needs to be cost-effective , acceptable to and sustainable by the recipient community . Therefore , gauging the readiness of communities in schistosomiasis endemic areas for intervention trials is of paramount importance[24] . In this paper , we investigated the perceptions of island communities with a high burden of the disease about their perceptions of schistosomiasis transmission and control . Specifically , we assessed their knowledge of schistosomiasis , their views on the various control strategies and the control interventions most acceptable to them . We also sought their opinion on their willingness to participate in Schistosoma vaccine trials . This knowledge can be valuable to policy makers and programme implementers intending to transition from project-provided interventions to interventions managed by the community members and local health facilities . This work also provides data to guide further work on alternative interventions such as Schistosoma vaccine trials in these communities . The study was carried out in the Lake Victoria island villages of Koome sub-county , Mukono district , Uganda . Mass drug administration of praziquantel is being provided to these communities as an intervention in a cluster randomised trial investigating the effects of anthelminthic intervention on health outcomes–the Lake Victoria Island Intervention Study on Worms and Allergy-related diseases ( LaVIISWA ) [3]–in collaboration with the Vector Control Division , Ministry of Health , Uganda . In this trial , 13 villages were randomised to receive the standard intervention against helminths ( single dose praziquantel once a year , single dose albendazole twice a year ) and 13 were randomised to intensive intervention ( single dose praziquantel four times a year , triple dose albendazole four times a year ) . The interventions were rolled out in 2012 and are , in 2017 , on-going . The study presented here was cross-sectional and employed qualitative methods through in-depth Interviews ( IDI ) , key informant interviews ( KII ) and focus group discussions ( FGDs ) . Six of the 27 fishing villages in the sub-county were randomly selected by the trial statistician to participate , taking into consideration that big and small villages were equally represented . Using STATA software ( Stata Corp . , College Station , TX , USA ) , a random selection of participating households in each village was generated . Participants for the in-depth interviews were selected from six households in each village . Adult members of the household who had lived in Koome sub-county for at least 6 months were eligible to participate . On day 1 , the selected households were contacted by the research team , a community leader and a member of the village health team . Eligible household members were invited to participate and appointments made to conduct the interviews during the week ( Monday to Friday ) . One adult member was interviewed from each household , alternating between male and female and choosing the most “senior” adult available in each household ( preferably the household head if this person was of the required gender ) . Six participants were interviewed per village ( the first three male and three female participants to consent ) . Two key informants were purposively selected per village . These were community leaders such as Local Council ( LC ) 1 chairpersons , Beach Management Unit ( BMU ) chairpersons , religious leaders and health workers . Beach Management Units are community fisheries management institutions set up in each fishing village . Two focus group discussions were planned for each village , one for each gender ( male and female ) . Five members were purposively selected for each group by the community leaders . These were community members aged 18 years and above and had lived in the sub-county for at least 6 months . For each village , all the interviews ( in-depth and key informant ) and focus group discussions were conducted in a space of one week ( Monday–Friday ) . Prior to commencement of data collection , the study was presented to the district health team and consultations held with them . Thereafter , meetings were held in each of the six villages to present and explain the work to community members and answer questions about the study . The data were collected by an experienced Social Science interviewer from the research team using both an audio recorder and field notes . Each interview lasted for about an hour . All the interviews and discussions were conducted in Luganda , the local language , using a translated topic guide ( S4 Text , S5 Text , S6 Text ) . After the tools ( information sheets , consent forms , interview topic guides and standard operating procedures ) were developed , the procedures were piloted in one of the study villages . The key informant interviews were conducted in each village to obtain the views of the opinion leaders . The key informant interviews were conducted before focus group discussions . For each FGD , a moderator ( from the research team ) led the discussions , and a note-taker was present to document all verbal and nonverbal responses . To assess awareness of the existence , causes , transmission , health problems and control of schistosomiasis , data were collected , using open-ended questions , on the following: The attitudes of the community members towards the following intervention strategies were assessed: mass administration of praziquantel , disposal of faeces in toilets or latrines , provision and use of safe water supplies , minimising contact with infested water and snail control . To determine the schistosomiasis control interventions most acceptable for the community , views were solicited from the participants on the interventions they thought would work and were willing to adopt . Their opinions were also obtained on who should provide the interventions , how people can be motivated to use them and what the obstacles are . The community members’ willingness to participate in future Schistosoma mansoni vaccine trials was also assessed . Characteristics of a mock vaccine trial were utilized and readiness assessed . The vaccine trial attributes considered were: All the notes and audio recordings were transcribed and the data were analysed manually using a thematic content approach . Responses were categorized into themes and ideas formulated by looking at the pattern of responses . After transcription , data were analysed thematically by closely reading and re-reading the interview scripts looking out for commonalities or recurring opinions and any other thoughts or ideas emerging from the data which formed our themes . The themes were then classified into subthemes and organised in relation to the study objectives . Narrative text was applied around the themes and participant direct quotes were added to illustrate the text . The themes included assessment of community health problems , knowledge and awareness of schistosomiasis , perceptions about interventions to control schistosomiasis , knowledge about vaccines , and willingness to participate in Schistosoma vaccine trials . Ethical approval to conduct this study was granted by the Research Ethics Committee of Uganda Virus Research Institute ( reference number GC/127/15/05/510 ) ( S1 Text ) , the London School of Hygiene and Tropical Medicine ( reference number 10109 ) ( S3 Text ) and the Uganda National Council for Science and Technology ( reference number SS 3831 ) ( S2 Text ) . All participants provided written informed consent prior to the interviews and discussions . A majority ( 63 out of 94 ) of the participants did not consider schistosomiasis to be a major health problem . To them , schistosomiasis had been a big problem in the past , which has been averted by mass drug administration of praziquantel . Now , schistosomiasis was not considered dangerous during its early stages and was regarded as not affecting activities of daily living . Respondents said that it only becomes more severe if left untreated and results in abdominal distension , body weakness , loss of appetite and eventually death . The scarcity of toilets , safe water sources and health facilities were frequently reported as major health issues . The most frequently mentioned diseases affecting the community were malaria ( mentioned by 32 participants ) , diarrhoea ( 27 participants ) , respiratory infections ( 28 participants ) and HIV ( 25 participants ) . Most of the participants had previously heard about schistosomiasis from community health workers and community leaders . A few said they studied about schistosomiasis at school . However , despite having heard about it , all participants from the in-depth interviews , 7 out of 12 key informants and 33 out of 50 FGD participants reported , incorrectly , that the main source of infection with schistosomiasis was drinking infested water . Fifty-seven participants also correctly stated that contact with infested lake water ( while fishing , fetching and washing from the lake , swimming and playing in the lake ) and open defecation were sources of infection . Three participants said that eating half-cooked food or food that has been contaminated by flies which have come into contact with faeces causes schistosomiasis . Although everyone who lives or works in the study villages was perceived to be at risk of schistosomiasis infection , fishermen were identified as the most at risk . The other groups of people identified to be at risk are women who do laundry from the lake , children who swim in the lake for recreation and the youth who load and off load passenger boats . Most ( 63 out of 94 ) participants stated that it is very hard to control schistosomiasis in their communities . They attributed this to the nature of their activities which revolve around the lake . Six key informants blamed this on the movement of people from one village to another , and inability of the community members to utilise the control measures in place . The control measures suggested by a few participants include improving sanitation , having access to safe and adequate water facilities , increasing the coverage of mass treatment and health education . Having a vaccine was also suggested as one of the interventions . One female participant reported placing water for domestic use in the sun for seven hours as a method she uses to prevent infection . Two female participants stated that their husbands draw water for domestic use from the middle of the lake as a measure to control schistosomiasis . They believed that water in the middle of the lake is unsuitable for the organisms to survive because it is warm . They also said they draw water for domestic use from routes used by ferries and other boats with big engines because they believe the organisms are repelled by the engines . The treatment seeking behaviour of the community members was said to still be poor . Despite the free mass drug administration of praziquantel , some community members are said to be unwilling to accept treatment . One reason respondents cited for not taking treatment was being “too busy with their work” . It was also revealed that residents are reluctant to seek treatment for schistosomiasis during its early stages . This is because , they said , at these stages , they are asymptomatic and their daily activities are unaffected . Lack of health facilities in the sub-county for testing and treating schistosomiasis was also highlighted as a challenge . Mass administration of praziquantel was perceived to be beneficial by 90 of the 94 participants . The fear of side effects notably dizziness , vomiting , fatigue , diarrhoea and loss of appetite was reported by a few participants as the reason for people’s refusal to take praziquantel . Men , especially fishermen , were reported to be the most notorious for dodging mass treatment for this reason . The side effects were described by one participant as being more severe than the disease itself . Most ( 56 out of 94 ) of the participants said the presence of side effects was because of infection with schistosomiasis . They said that the side effects show that the treatment is effective and these effects are short lived , while the benefits last longer . One participant complained about the bitter taste of the treatment tablets . Most participants ( 79 out of 94 ) viewed disposal of faeces in toilets as a good intervention to control schistosomiasis . They reported that disposal of faeces in latrines/toilets also helps to control the spread of other diseases like cholera , diarrhoea , dysentery and other worm infections . They noted that they lack latrines/toilets in their communities and this has resulted in open defecation . The major reason given for not owning a latrine was high cost of materials required for construction . In addition , it was said that the close proximity to the lake renders the soils too weak to keep a latrine firm . Eight participants blamed their landlords for the failure to have toilets in place . Both the community members and the opinion leaders said some traditional beliefs deter people from owning and using toilets . However , one participant said negligence is the only reason why they do not have toilets . She said people in the islands believe that they are always on the move ( temporarily settled ) and so labouring to have toilets in place would be a waste of time and money . Nearly all participants ( 90 out of 94 ) perceived the provision of safe water supplies to be an effective intervention to control schistosomiasis . They reported that this intervention would mostly favour the women , children and people whose work does not involve regular contact with the lake such as bar and shop owners . For this intervention to work more effectively , participants suggested that many safe water facilities must be erected . This will minimise long queues when accessing safe water and make fetching water directly from the lake less appealing . Most of the participants had reservations about the use of biological agents to control snails . Thirty-six participants felt that chemicals may affect the fish negatively because they ( the chemicals ) could be non-selective and kill the fish as well as snails . Two participants were keen to know the dose of the chemical required to kill all the snails . They also felt that this chemical may remain on the shores and not reach farther into the lake rendering it less effective . On the use of biological agents such as fresh water prawns [25 , 26] or ducks [27] to feed on the host snails , the participants were concerned about the numbers required . They expressed the fear that the agents ( especially ducks ) may be poached . Participants said that in the past , there were many wild ducks on Lake Victoria but now , they are almost extinct . On the use of competitor snails [28 , 29] to control schistosomiasis , most ( 70 out of 94 ) participants wondered how snails can be predators of other snails . Some of the community leaders questioned the effectiveness of this intervention . Six participants who showed interest in the biological agents called for community engagement so that people can appreciate the potential of the intervention . There were mixed reactions about this intervention . The majority of female participants perceived it to be a good intervention saying that it is practical , provided there are other water sources in place . The male participants felt that this intervention is ‘unrealistic’ and ‘unwanted’ because it will directly affect their income . Another participant from the female FGD said: Most participants ( 51 out of 94 ) mentioned mass drug administration of praziquantel as an acceptable intervention which they are willing to adopt . This was closely followed by the provision of safe water sources ( 35 participants ) and improved sanitation through disposal of faeces in toilets and latrines ( 30 participants ) . They said that the residents need to be educated on the proper disposal of faeces in order to curb open defecation . Most participants felt that putting up such interventions requires extensive infrastructure which they are unable to provide because of poverty . Forty-four participants ( 47% ) said the government should provide these interventions . Some ( 25 participants ) suggested LaVIISWA should provide , and the rest suggested a joint venture between government and LaVIISWA or between government and non-governmental organisations ( NGOs ) or well-wishers . Twelve participants ( including three key informants ) were unhappy with the way government operates . They said that government has on numerous occasions pledged to provide them with safe water and toilets . These pledges are yet to be fulfilled . To sustain these interventions , participants felt that , once stringent penalties were in place , fines should be levied on those who do not comply . A few participants called for public engagement and health education . They said that community members should be told the advantages and disadvantages of the interventions before any penalties are enforced . Some participants said providing free access to the interventions will motivate people to use them . Participants demonstrated that they had a basic knowledge about vaccines and their role in disease prevention . Many participants acknowledged that they were unaware of the availability of any Schistosoma vaccine . One participant thought that mass treatment with praziquantel was a form of vaccination . All participants said they would welcome a vaccine becoming available . The community leaders also reported that a vaccine would be the best intervention for controlling schistosomiasis . The majority ( 74 out 94 ) of the participants expressed willingness to participate in a Schistosoma vaccine trial . They stated various reasons for the interest in enrolment: service to humanity , benefit from preventing schistosomiasis and trust in the researchers conducting the trial . The reasons for not participating were religious beliefs , conspiracy theories and fear of side effects . The participants were willing to participate in a vaccine trial for a duration of three years although three participants felt that duration was too long . The majority of the participants were willing to accept vaccine administration by injection . Seven participants ( six of them were female ) said they feared the pain caused by injections and would not participate in the trial for that reason . Most participants had no problems with being randomised to any study arm ( including placebo ) . Eight participants preferred the arm with the candidate vaccine and gave it as a pre-condition for participating in the trial . Most participants said that they were willing to provide the required volumes of blood . Eight participants complained about the volumes and said they would not participate . Eleven participants said they should be given food supplements to replace the blood drawn and be provided with medical treatment during the trial . All but three female participants were willing to delay or defer pregnancy during the trial using birth control methods such as oral and injectable contraceptives . Five participants ( all male ) said they should be acknowledged as heroes and given some monetary compensation at the end of the vaccine trial . In this paper , we have shown that the inhabitants of this schistosomiasis-endemic area prefer mass treatment with praziquantel , safe water supplies and use of toilets to minimising contact with infested water and snail control as the interventions they are willing to embrace . Despite awareness about the existence of schistosomiasis in their communities , they do not consider it as a major health priority because of a mass treatment programme in place . Gaps exist in their basic knowledge about schistosomiasis transmission and prevention such as regarding drinking of infested water as the main source of infection . Provision of mass treatment with praziquantel has faced obstacles such as inadequate supplies of praziquantel , the costs associated with delivery to the target communities and lack of compliance with treatment [30] . Factors such as population migration , change in food supply , conspiracy theories about the intentions of MDA , fear of drug side effects and relations between drug distributors and the target community have been identified to determine MDA success in communities in Uganda [31] . MDA is also not taken up because of inappropriate and inadequate health education and differing biomedical and local understanding of schistosomiasis , absence during drug distribution , pregnancy , breast feeding and feeling healthy [32 , 33] . Indeed the uptake of mass treatment with praziquantel has been sub-optimal in the Lake Victoria island communities [34] and other nearby communities [35] . With support from development partners such as the Schistosomiasis Control Initiative , praziquantel has become more affordable and the supplies more consistent . Logistical support and a motivated drug distribution network in these communities would ensure that the communities access the medication . Health education would address compliance . Providing safe water supplies to the Lake Victoria island communities is still a challenge . Once achieved , the communities need to be educated on the benefits in order to maximise its use . Water for domestic use could be obtained from these safe sources and in the process , vulnerable groups such as women working at home and children would have less contact with the infested lake water . It will still be challenging to stop recreational contact and harder to convince the fishermen to minimise contact with the lake but attempts have to be made . In such a resource limited setting , a concerted effort involving the local communities , government and development partners is required to establish and sustain this intervention . Despite the willingness to use toilets , coverage is very low ( <10% ) in these communities [3] . The communities feel that it is costly to construct and maintain latrines due to the terrain and some landlords are unwilling to provide land . Health education is also key in addressing the misconceptions about the source of infection and wrong beliefs about toilet use . Despite mass treatment with praziquantel , safe water supplies and use of toilets being the most acceptable interventions , the communities felt that they are unable set up and sustain the interventions on their own . Reasons such as prohibitive costs in setting up and maintaining the interventions , mobility of the population , lack of unity in the communities owing to the diverse cultural backgrounds and uncooperative landlords were stated . The communities are willing to participate in sustaining the interventions and they provided suggestions such as setting up stringent byelaws , the need for health education and community engagement . Indeed , health education is important because knowledge about the transmission , severity and consequence of schistosomiasis may be poor [36] . As demonstrated elsewhere , the communities need to be involved in designing the interventions in order to promote ownership of the intervention [24] . To our knowledge , this is the first qualitative study to assess the willingness of a highly endemic community to take part in a potential Schistosoma vaccine trial . The community members were interested and willing to engage in discussion about a trial . However , for the success of such a trial , the concerns raised in this study need to be adequately addressed: the goals of the trial and requirements such as blood sample volume and trial duration need to be clearly explained , and adequate recognition must be given to participants’ contribution to the exercise . As the world targets the elimination of neglected tropical diseases such as schistosomiasis , the perspectives of the target communities about the control strategies do provide very useful insights , especially to policy makers . Community-specific solutions can be designed to address potential barriers to the acceptability and sustainability of an otherwise scientifically proven intervention .
Schistosomiasis , a neglected tropical disease caused by the blood fluke Schistosoma , is still a huge burden in sub-Saharan Africa . The modalities for its control are mass treatment of the population with praziquantel , minimising contact with infested water , provision and use of safe water , intermediate host snail control and disposal of stool in toilets/latrines . For sustainable control of the parasite , the recipient communities need to embrace the interventions . In this study , we investigated the perceptions of fishing communities on the Lake Victoria Islands about interventions to control schistosomiasis and their willingness to participate in Schistosoma vaccine trials . We assessed their knowledge of schistosomiasis , their views on the interventions and the interventions most acceptable to them . We show that the community members of this schistosomiasis-endemic area prefer mass treatment with praziquantel , safe water supplies and use of toilets to minimise contact with infested water and snail control . The communities are also willing to participate in Schistosoma vaccine trials . This information is valuable to policy makers and programme implementers intending to set up interventions co-managed by the recipient communities . In addition , the study provides support for future Schistosoma vaccine trials in these communities .
You are an expert at summarizing long articles. Proceed to summarize the following text: A SARS-CoV lacking the full-length E gene ( SARS-CoV-∆E ) was attenuated and an effective vaccine . Here , we show that this mutant virus regained fitness after serial passages in cell culture or in vivo , resulting in the partial duplication of the membrane gene or in the insertion of a new sequence in gene 8a , respectively . The chimeric proteins generated in cell culture increased virus fitness in vitro but remained attenuated in mice . In contrast , during SARS-CoV-∆E passage in mice , the virus incorporated a mutated variant of 8a protein , resulting in reversion to a virulent phenotype . When the full-length E protein was deleted or its PDZ-binding motif ( PBM ) was mutated , the revertant viruses either incorporated a novel chimeric protein with a PBM or restored the sequence of the PBM on the E protein , respectively . Similarly , after passage in mice , SARS-CoV-∆E protein 8a mutated , to now encode a PBM , and also regained virulence . These data indicated that the virus requires a PBM on a transmembrane protein to compensate for removal of this motif from the E protein . To increase the genetic stability of the vaccine candidate , we introduced small attenuating deletions in E gene that did not affect the endogenous PBM , preventing the incorporation of novel chimeric proteins in the virus genome . In addition , to increase vaccine biosafety , we introduced additional attenuating mutations into the nsp1 protein . Deletions in the carboxy-terminal region of nsp1 protein led to higher host interferon responses and virus attenuation . Recombinant viruses including attenuating mutations in E and nsp1 genes maintained their attenuation after passage in vitro and in vivo . Further , these viruses fully protected mice against challenge with the lethal parental virus , and are therefore safe and stable vaccine candidates for protection against SARS-CoV . Coronaviruses ( CoVs ) are pathogens responsible for a wide range of existing and emerging diseases in humans and other animals [1] . A novel coronavirus causing the severe acute respiratory syndrome ( SARS-CoV ) was identified in Southeast China in 2002 . SARS-CoV rapidly spread worldwide to more than 30 countries within six months , infecting 8000 people and leading to death in approximately 10% of the cases [2 , 3] . While SARS-CoV has not reappeared in humans , CoVs including those similar to SARS-CoV , are widely disseminated in bats circulating all over the world , making future SARS-CoV outbreaks possible [4–7] . Furthermore , in September 2012 , a novel coronavirus infecting humans , the Middle East respiratory syndrome coronavirus ( MERS-CoV ) , was identified in two patients with severe respiratory disease in Saudi Arabia [8 , 9] , again indicating that emergence of other highly pathogenic CoVs is likely . Thus , development of efficacious and safe vaccines and anti-virus therapies for these pathogens is essential . SARS-CoV is an enveloped virus with a positive sense RNA genome of 29 . 7 kb that belongs to the Coronavirinae subfamily , genus β [2] . The virion envelope contains embedded three structural proteins , spike ( S ) , envelope ( E ) , and membrane ( M ) and several group specific proteins: 3a , 3b , 6 , 7a , and 7b [10–12] . The S protein , which mediates virus entry into host cells , the 3a protein and the M proteins , induce neutralizing antibodies , with those specific for S protein being most protective [13–16] . The SARS-CoV S and N proteins trigger T cell responses [17] , which are also important for protection and enhance the kinetics of virus clearance [18 , 19] . SARS-CoV E protein is a small integral membrane protein of 76 amino acids that contains a short hydrophilic amino-terminus followed by a hydrophobic transmembrane domain and a hydrophilic carboxy-terminus [20] . E protein oligomerizes to form an ion-conductive pore in membranes [21–23] , and contains a PDZ-binding motif ( PBM ) formed by its last four carboxy-terminal amino acids [24 , 25] . PDZ domains are protein-protein recognition sequences , consisting of 80 to 90 amino acids that bind to peptide sequences ( PBMs ) [26–28] . These protein-protein interactions modulate cellular pathways important for viral replication , dissemination in the host and pathogenesis [29] . We previously demonstrated that a SARS-CoV lacking the E gene ( SARS-CoV-∆E ) was attenuated in different animal models [30–34] , indicating that SARS-CoV E protein is a virulence factor . SARS-CoV lacking the E protein fully protected both young and elderly BALB/c mice against challenge with virulent mouse-adapted SARS-CoV [32]; therefore , rSARS-CoV-∆E is a promising vaccine candidate . Live attenuated vaccines are considered highly effective because of their ability to replicate within host cells , resulting in high levels of antigenic stimulation , and robust long-term immunological memory [35 , 36] . However , a major safety concern with live attenuated vaccines is the possibility of reversion to a pathogenic form . CoVs are prone to RNA recombination and mutation in tissue culture and during animal infection [37] , so it is crucial that rSARS-CoV-∆E be thoroughly studied after serial passage . In this study , we show that passage of viruses lacking all or part of the E protein in Vero E6 cells and mouse DBT-mACE2 cells [38] led to the incorporation of compensatory insertions . Interestingly , passage of SARS-CoVs lacking the E protein PBM led to regeneration of viral proteins containing PBMs , either by incorporation of novel chimeric proteins or by the insertion of new PBMs into existing proteins , such as the 8a protein or the E protein if E was only partially deleted . Strikingly , these modifications were not observed after passage of SARS-CoVs with mutated E protein that retained the PBM . In fact , we have shown that if instead of deleting the full-length E protein , only small deletions of 8–12 amino acids were introduced into the carboxy-terminus of E protein with retention of the PBM , the SARS-CoVs generated were attenuated and genetically stable both in cell culture or in mice . While this partial E protein deletion resulted in virus stability , we also augmented vaccine safety by introducing mutations into the SARS-CoV nsp1 protein . The nsp1 protein of CoVs suppresses host gene expression by inducing host mRNA degradation and inhibiting protein translation [39–44] , and is an IFN antagonist [45–47] . Nsp1 deletions resulted in attenuated murine coronaviruses that fully protected against the challenge with parental virus [48 , 49] . In this manuscript we show that small deletions within SARS-CoV nsp1 protein resulted in virus attenuation , associated with reduction of inflammation and higher levels of IFN-β and interferon-stimulated genes ( ISGs ) . Vaccination with mutated nsp1 variants protected against challenge with the virulent mouse-adapted SARS-CoV ( rSARS-CoV ) virus . To generate safer vaccine candidates , viruses incorporating deletions in both the nsp1 and E proteins were constructed . These double mutants were protective against virulent virus challenge , and were genetically stable . To determine the stability of SARS-CoV-∆E , or of virus containing deletions of the E protein and several group specific genes including 6 , 7a , 7b , 8a , 8b and 9b ( SARS-CoV-∆[E , 6-9b] ) , we infected Vero E6 and DBT-mACE2 cells with rSARS-CoV , rSARS-CoV-∆E or rSARS-CoV-∆[E , 6-9b] . Supernatants were serially passaged 16 times and the distal third of the genome , from the S gene to the 3´ end ( around 8 kb ) , was sequenced using specific primers ( S1 Table ) . In all cases , an insertion consisting of a partially duplicated M gene fused to the SARS-CoV leader RNA sequence , a 5´ sequence common to coronavirus mRNAs [50–52] was detected upstream of the native M protein ( Fig 1A ) . In contrast , no chimeric proteins were detected after serial passage of the parental virus . All MCH genes encoded the amino terminus and the three transmembrane domains of M and also different PDZ-binding motifs at the carboxy-terminus of the protein ( Fig 1B ) . Genomic evolution occurred rapidly , as the chimeric genes were already detected within 5 passages in both cells lines , Vero E6 and DBT-mACE2 . The expression of viral sgmRNAs corresponding to the chimeric genes was characterized by RT-PCR ( S1A Fig ) . We used RNA harvested from infected cells after serial passage , plaque purification and amplification along with specific primers ( S2 Table ) . PCR products corresponding to specific MCH sgmRNAs were identified in MCH-Vero and MCH-DBT-infected cells ( S1B Fig ) . Expression of the chimeric proteins encoded by these sgmRNAs was confirmed using an antibody specific for all M and MCH proteins and a second one that recognized the MCH-DBT protein . Vero E6 cells were mock infected or infected with different recombinant viruses ( rSARS-CoV , rSARS-CoV-∆E , rSARS-CoV-∆E-MCH-Vero and rSARS-CoV-∆E-MCH-DBT ) at a moi of 0 . 3 . The expression of native M and MCH was confirmed at 24 hpi by Western blot analysis ( Fig 1C and 1D ) . These results indicated that , after serial passages of SARS-CoVs lacking the E protein in cell culture , a similar type of chimeric membrane protein was generated in three independent experimental settings . To test whether the presence of MCH genes conferred a replication advantage to SARS-CoV-∆E in vitro , the growth kinetics of SARS-CoV-∆E-MCH-Vero ( MCH-Vero ) and SARS-CoV-∆E-MCH-DBT ( MCH-DBT ) were analyzed . Vero E6 and DBT-mACE2 cells were infected with the recombinant viruses ( moi of 0 . 001 ) and viral titers were determined at the indicated hpi ( Fig 2 ) . MCH-Vero and MCH-DBT viruses showed lower titers at 24 hpi in Vero E6 cells compared to rSARS-CoV but both virus titers of the two chimeric viruses and rSARS-CoV were similar at 72 hpi . In contrast , a 100-fold decrease in viral growth was observed in rSARS-CoV-∆E-infected cells ( Fig 2 ) . Interestingly , chimeric proteins seemed to be specific for each cell type , as a ∆E virus containing a chimeric protein generated in Vero E6 cells ( MCH-Vero ) grew better in this cell line than in DBT-mACE cells , and the MCH-DBT virus generated in DBT-mACE2 cells specifically enhanced its growth in this cell line . This result indicated that the MCH protein provided a growth advantage for the virus , which partly compensated for the lack of the E protein . To determine the effect of the MCH protein in pathogenesis , BALB/c mice were intranasally infected with the recombinant viruses using 100 , 000 pfu , and weight loss and survival were monitored for 10 days ( Fig 3A ) . Mice infected with the parental virus ( wt ) showed signs of clinical disease at 2 days post infection ( dpi ) , reflected by ruffled fur , shaking , loss of mobility and weight loss , resulting in the death of all mice at 6 dpi ( Fig 3A ) . In contrast , mock-infected mice or mice infected with the ∆E virus , independently of whether the chimeric proteins ( ∆E , MCH-Vero and MCH-DBT ) were present or absent , did not lose weight and all of them survived without symptoms of disease ( Fig 3A ) . To analyze the effect of the MCH protein on virus growth in vivo , BALB/c mice were intranasally inoculated with the recombinant viruses and euthanized at 2 and 4 dpi . Virus titers in the lungs were determined ( Fig 3B ) . Viruses lacking the E protein , in the presence or absence of MCH protein , grew to lower titers in lungs at 2 and 4 dpi , as compared with those observed in mice infected with rSARS-CoV . Notably , rSARS-CoV-∆E replicated in the lung to higher levels than those containing the chimeric proteins ( MCH-Vero and MCH-DBT ) at both days p . i . Chimeric proteins only increased fitness in a cell type-dependent manner , i . e . , viruses with the chimeric protein only grew better in the cell system in which this chimeric protein was generated . The virus containing a chimeric protein generated in Vero E6 cells grew better in this cell line , and the virus containing a chimeric protein generated in DBT-mACE2 cells showed an increased growth in these cells ( Fig 2 ) . Lungs of mice infected with rSARS-CoV were highly edematous and showed profuse hemorrhagic areas at 2 and especially at 4 dpi ( S2A Fig ) , leading to a significant increase in lung weight at 4 dpi ( S2B Fig ) . Lung sections from mock-infected mice or mice infected with rSARS-CoV-∆E , MCH-Vero and MCH-DBT ( Fig 3C ) showed minimal damage at 2 and 4 dpi . In contrast , analysis of the lungs of mice infected with rSARS-CoV revealed extensive inflammatory cell infiltration and edema in alveolar and bronchiolar airways ( Fig 3C ) . The chimeric protein MCH was generated after rSARS-CoV-∆E passage in cell culture , but not when full-length E protein was present , suggesting that it compensated for functions originally performed by E protein . To identify such E protein functional domains , a set of recombinant SARS-CoVs ( Fig 4A ) , with mutations or deletions in different regions of E protein [23 , 24 , 53] , was passaged 16 times in Vero E6 cells ( Fig 4 ) . In Mut 1 , several amino acid substitutions were introduced at the E protein amino-terminal region . rSARS-CoV deletion mutants ∆2 , ∆3 , ∆4 , ∆5 and ∆6 included sequential or partially overlapping small deletions of 6 to 12 amino acids in the carboxy-terminus of E protein . Interestingly , the last 6 amino acids within the Δ6 virus ( YSRVKN; Fig 4 ) revealed an alternative PBM at the carboxy-terminal domain of the protein [53] . In recombinant ∆PBM , the last 9 amino acids of E protein were deleted , truncating the carboxy-terminus and eliminating the E protein PBM . In mutPBM , the PBM was abolished by mutating the last 4 amino acids to glycine , maintaining the full-length E protein . In contrast , in altPBM , 4 amino acids within E protein carboxy-terminal region were mutated to alanine , maintaining an active PBM domain [24] . In SARS-CoV N15A and V25F mutants , ion channel activity of E protein was abolished by one point mutation in the transmembrane domain ( Fig 4A ) . Vero E6 cells were infected with each of the recombinant viruses at a moi of 0 . 5 and supernatants were serially passaged for 16 times , and the presence of MCH gene was determined by sequence analysis . The results indicated that the MCH was not generated when the E protein contained a PBM sequence ( rSARS-CoV , Mut 1 , ∆2 , ∆3 , ∆4 , ∆5 , ∆6 , altPBM , N15A and V25F ) ( Fig 4B ) . When the PBM was absent ( ∆PBM and mutPBM ) , a new PBM containing the original sequence was added to the carboxy-terminal end of mutated E protein in all cases , reinforcing the importance of the PBM domain during infection . A virus incorporating the chimeric MCH protein was only generated when E protein was completely deleted ( ∆E ) , i . e . , when the restoration of a PBM on the E protein was not possible . To further analyze whether SARS-CoV requires a transmembrane protein containing a PBM , we generated two recombinant rSARS-CoV-∆E that contained artificial chimeric proteins ( Fig 4C ) . In SARS-CoV-∆E-MCH-EPBM ( MCH-EPBM ) , a chimeric protein containing the first transmembrane domain of the M protein fused to the last nine amino acids of E protein , encompassing the PBM , was introduced . In SARS-CoV-∆E-3aCH-3aPBM ( 3aCH-3aPBM ) , the chimeric protein was formed by the first transmembrane domain of 3a protein and a PBM composed by the last nine amino acids of 3a protein ( Fig 4C ) . Both viruses were passaged 16 times in Vero E6 cells and compensatory mutations were not detected after sequencing . All these data indicated that the virus requires a transmembrane protein displaying a PBM and that novel proteins with a PBM compensate for the loss of the E protein PBM . MCH-Vero and MCH-DBT exhibited an attenuated phenotype ( see above ) . To analyze the genetic stability of recombinant SARS-CoV-∆E in BALB/c mice , virus was passaged every 48 hours by intranasal inoculation . A partial duplication of 45 nucleotides was found within 8a gene ( Fig 5 ) , leading to the incorporation of a fragment of 15 amino acids at the carboxy-terminus of 8a protein , and generating a novel 8a protein ( 8a-dup ) with an internal PBM ( CTVV ) ( Fig 5A and 5B ) . To determine the virulence of this novel virus ( SARS-CoV-∆E-8a-dup ) , we infected a new cohort of BALB/c mice and assessed survival and weight loss , and measured virus titers . Mice infected with SARS-CoV-∆E did not lose weight and all survived . In contrast , SARS-CoV-∆E-8a-dup grew to titers similar to those of rSARS-CoV and developed profound weight loss , developing signs of illness and death by 7 dpi ( Fig 6A and 6B ) . Histological examination of lungs from SARS-CoV-∆E-infected mice showed absence of lung damage at both dpi . In contrast , lungs of mice infected with rSARS-CoV or SARS-CoV-∆E-8a-dup showed substantial perivascular , peribronchial and interstitial cellular infiltration and edema at 2 and 4 dpi ( Fig 6C ) . To further confirm the relevance of the partial duplication within 8a gene in the induction of virulence , a recombinant SARS-CoV-∆E with an 8a-dup gene was generated ( rSARS-CoV-∆E-8a-dup ) . Virulence during infection with rSARS-CoV-∆E-8a-dup was evaluated as described above ( Fig 6D ) . The engineered virus ( rSARS-CoV-∆E-8a-dup ) was as virulent as the SARS-CoV-∆E-8a-dup generated after SARS-CoV-∆E passage in vivo ( Fig 6D ) . These results indicated that SARS-CoV-∆E regained virulence after serial passage in mice . SARS-CoV infection is associated with p38 mitogen-activated protein kinase ( MAPK ) activation and elevated levels of pro-inflammatory cytokines [24 , 54–56] . To begin to determine the basis of increased virulence exhibited by SARS-CoV-∆E-8a-dup , p38 MAPK activation and pro-inflammatory cytokine expression were analyzed during infection ( Fig 7 ) . p38 MAPK activation was analyzed by Western blot at 24 hpi using a phospho-p38 MAPK ( p-p38 ) specific antibody . Antibodies recognizing the total endogenous p38 MAPK and actin were used as controls . A significant increase in p38 MAPK activation , assessed at 24 hpi using a phospho-p38 MAPK ( p-p38 ) -specific antibody and Western blot analysis , was observed in SARS-CoV-∆E-8a-dup-infected compared to SARS-CoV-∆E or mock-infected cells ( Fig 7A and 7B ) . To test whether pro-inflammatory cytokine expression was induced during SARS-CoV-∆E-8a-dup infection , we analyzed the expression of several genes previously associated with SARS-CoV pathology [24 , 57] including: chemokine ( C-X-C motif ) ligand 10 ( CXCL10 ) , chemokine ( C-C motif ) ligand 2 ( CCL2 ) and interleukin 6 ( IL6 ) ( S3 Table ) . 18S ribosomal RNA was used to normalize the data , as previously described [58 , 59] . BALB/c mice were mock-infected or infected with 100 , 000 pfu of recombinant SARS-CoV , and lungs were collected at 2 dpi . A significant increase in the expression of all pro-inflammatory cytokines in the lungs was observed during infection with virulent viruses ( rSARS-CoV and SARS-CoV-∆E-8a-dup ) ( Fig 7C ) . In contrast , infection with the recombinant virus lacking E protein at passage 0 ( SARS-CoV-∆E ) did not induce the expression of pro-inflammatory cytokines . Activation of p38 MAPK and pro-inflammatory cytokines expression during infection with rSARS-CoV-∆E-8a-dup were evaluated as described above . The engineered virus ( rSARS-CoV-∆E-8a-dup ) induced similar p38 MAPK activation ( Fig 7D and 7E ) and overexpression of proinflammatory cytokines ( Fig 7F ) as compared with the SARS-CoV-∆E-8a-dup generated after SARS-CoV-∆E passage in vivo . These data indicated that reversion of SARS-CoV-∆E-8a-dup to a virulent phenotype was associated with an exacerbated immune response similar to that triggered during infection with the rSARS-CoV . SARS-CoV-ΔE reverted to a virulent phenotype after serial passages in mice . To increase the genetic stability of the vaccine candidate , we introduced small attenuating deletions in the E gene , instead of deleting of the full-length E protein [53] as described above . In addition , to increase vaccine biosafety , we introduced additional attenuating mutations within the SARS-CoV nsp1 gene . To identify domains within the SARS-CoV nsp1 protein that could contribute to virulence , we compared the sequence to that of the MHV nsp1 ( Fig 8A ) , with the goal of identifying conserved regions that could be functionally important . Based on this information , four mutant viruses ( rSARS-CoV-nsp1* ) were generated by introducing deletions of 8 to 11 amino acids into the nsp1 protein ( rSARS-CoV-nsp1-∆A , -∆B , -∆C and -∆D Fig 8A ) . All the deletion mutants grew to similar titers as rSARS-CoV in Vero E6 cells ( Fig 8B ) . However the ∆A and ∆B mutants grew to lower titers in DBT-mACE2 cells . To analyze the effects of these deletions in vivo , mice were intranasally infected with mutants rSARS-CoV-nsp1-∆A , -∆B , -∆C and -∆D , and daily monitored for 10 days . SARS-CoV-nsp1-∆C and -∆D infected mice transiently lost a small amount of weight and all mice survived . In contrast , mice infected with SARS-CoV lost weight , and all died by day 5 ( Fig 9A ) . Mice infected with rSARS-CoV-nsp1-∆A or rSARS-CoV-nsp1-∆B lost 20 and 15% of their initial weight by day 3 , with survival reduced to 60 and 80% , respectively ( Fig 9A ) . These data indicated that deletion of regions C and D within nsp1 protein led to attenuated mutants , whereas deletion of regions A and B were only partially attenuating . With the exception of rSARS-CoV-nsp1-∆A at day 2 p . i . , virus titers were reduced compared to rSARS-CoV-infected mice ( Fig 9B ) . There was not a strict correlation between virus titers and virulence , possibly because nsp1 is involved in the countering IFN production after infection . No significant changes on gross inspection of lungs or in their weight were observed when the lungs of mock-infected and SARS-CoV-nsp1-∆C and -∆D-infected mice were compared ( Figs 9C and S3 ) . In contrast , lungs from mice infected with the rSARS-CoV and , to a much lower extent SARS-CoV-nsp1-∆A and -∆B-infected mice lungs , showed evidence of hemorrhage ( S3 Fig ) . In addition , rSARS-CoV-infected mice showed lung weight increase , not observed with the lungs of SARS-CoV-nsp1*-infected mice . Compared to mock-infected mice , lungs from rSARS-CoV-infected mice showed clear inflammatory infiltrates and alveolar and bronchiolar edema ( Fig 9C ) . In contrast , mice infected with the viruses rSARS-CoV-nsp1* showed no ( SARS-CoV-nsp1-∆C and -∆D ) , or minimal ( SARS-CoV-nsp1-∆A and -∆B ) lung damage . These data correlated well with the virulence observed for the SARS-CoV-nsp1* mutants , showing that the most attenuated viruses were those that induced less lung pathology in vivo . Since nsp1 has anti-interferon activity , we next measured expression of IFN and IFN-stimulated genes after infection with rSARS-CoV , rSARS-CoV-nsp1-∆C and -∆D . We focused on the ∆C and ∆D viruses , because these viruses were fully attenuated and had an efficient growth in vivo . SARS-CoV-nsp1-∆C and -∆D induced higher levels of IFN-β and ISGs ( IRF1 , DDX58 , and STAT1 ) , compared to mock-infected and rSARS-CoV-infected cells ( Fig 10A and 10B ) . This effect was specific , as the expression of control 18S rRNA was the same in virus-infected cells or mock-infected cells ( Fig 10B ) . These results indicated that deletion of regions C and D of nsp1 restored IFN responses , leading to virus attenuation . To analyze whether SARS-CoV-nsp1-∆C and -∆D induced protective immune responses , mice were intranasally vaccinated with SARS-CoV-nsp1-∆C and -∆D , and challenged 21 days later with rSARS-CoV . After challenge , mock-vaccinated mice rapidly lost weight , and all mice died by day 6 ( Fig 11A and 11B ) . In contrast , mice immunized with SARS-CoV-nsp1-∆C and -∆D viruses , did not significantly lose weight , and 100% survived the challenge ( Fig 11A and 11B ) . In order to develop a safe vaccine candidate , mutant viruses with deletions in both nsp1 and E genes were engineered . A rSARS-CoV deleted in the nsp1 D domain and the E protein ( SARS-CoV-nsp1ΔD-ΔE ) , and a second mutant virus with deletions of the nsp1 D domain coupled with a small deletion ( E∆3 ) ( Fig 4A ) in the E protein ( SARS-CoV-nsp1ΔD-EΔ3 ) were generated . EΔ3 deletion mutant was selected for further studies because this deletion led to a virus that grew to titers similar or higher than the SARS-CoV-∆E , in cell culture or in mice , respectively ( Fig 12A and 12B ) . More importantly , the E∆3 virus was genetically stable after 10 passages in cell culture [53] or in vivo , maintaining its attenuated phenotype ( Fig 12C ) , in contrast to the ∆E virus ( Figs 1 and 5 ) . This deletion was combined with another one in SARS-CoV nsp1 protein ( nsp1ΔD ) , which was fully attenuating . The resulting virus grew to relatively high titers in vivo ( Figs 9 and 13A ) . Viruses were rescued in Vero E6 cells , cloned and sequenced to confirm the presence of the desired mutations . To analyze the stability of the viruses in tissue culture cells , SARS-CoV-nsp1ΔD-ΔE and SARS-CoV-nsp1ΔD-EΔ3 were passaged 10 times in Vero E6 cells , followed by sequencing of the nsp1 and E genes . The deletions introduced in both nsp1 and E genes were conserved , suggesting that these deletions were genetically stable in vitro . SARS-CoV-nsp1ΔD-ΔE and SARS-CoV-nsp1ΔD-EΔ3 at passage 1 reached peak titers at 72 hpi ( 5·105 pfu/ml and 5·104 pfu/ml in Vero E6 and DBT-mACE2 cells , respectively ) ( Fig 13B ) . Decreased virus growth was likely due to deletions in the E gene , as previously described [53] ( Fig 8B ) . After 10 passages , viruses showed a slight increase in titer ( Fig 13B ) , suggesting incorporation of additional mutations but not in E or nsp1 . To analyze the pathogenicity of SARS-CoV-nsp1ΔD-ΔE and SARS-CoV-nsp1ΔD-EΔ3 mutants at passages 1 and 10 ( p1 and p10C , respectively ) , BALB/c mice were intranasally inoculated with recombinant viruses . All mice infected with these viruses maintained their weight and survived ( Fig 14A ) . In contrast , all mice infected with rSARS-CoV died ( Fig 14A ) . These results indicated that viruses including deletions in both nsp1 and E proteins were attenuated in vivo . SARS-CoV-nsp1ΔD-EΔ3 , especially the p10C virus , grew more efficiently than SARS-CoV-nsp1ΔD-ΔE ( Fig 14B ) . Nevertheless , no obvious gross lesions or changes in weight were observed in the lungs of mice infected with any of these doubly mutant rSARS-CoV ( Figs 14C and S4 ) . In contrast , mice infected with rSARS-CoV showed lung injury and a marked increase in the weight of lungs , as described above ( S4 Fig ) . Histological examination of lungs from SARS-CoV-nsp1ΔD-ΔE and SARS-CoV-nsp1ΔD-EΔ3 ( p1 and p10C viruses ) -infected mice showed only minimal evidence of damage or leukocyte infiltration at days 2 and 4 post-infection ( Fig 14C ) while rSARS-CoV-infected mice showed extensive cellular infiltration and edema . The rSARS-CoV-nsp1∆D-E∆3 was selected for further study because this virus showed higher titers in vivo as compared with the rSARS-CoV-nsp1∆D-∆E virus ( Fig 14B ) , therefore it could promote a higher immunization . In addition , ∆E mutation led to unstable viruses that incorporated new chimeric proteins in cell culture , or a novel 8a protein in vivo , causing reversion to a virulent phenotype ( Fig 6 ) . In contrast , viruses containing the E∆3 mutation remained stable after 10 passages and maintained their attenuated phenotype ( Fig 12 ) . To analyze the stability of rSARS-CoV with mutations in nsp1 and E in mice , SARS-CoV-nsp1ΔD-EΔ3 was passaged 10 times ( p10M ) , followed by sequencing from the S gene to the 3´ end of the genome . Only two changes were observed in the viral sequence , one in the E gene ( A26250T N→I ) , and a second one in the M gene ( A26450G Q→R ) . The deletions introduced in the nsp1 and E genes were conserved , suggesting that this virus was essentially genetically stable in vivo . To analyze whether the E and M mutations could be compensatory mutations , virus titers at p1 and p10 were compared in Vero E6 and DBT-mACE2 cells . Titers of SARS-CoV-nsp1ΔD-EΔ3 ( p10M ) at different times post-infection were the same as those observed for rSARS-CoV ( Fig 15A ) , indicating that the mutations increased virus replication . To evaluate whether the compensatory mutations restored the pathogenicity of the virus , BALB/c mice were intranasally inoculated with rSARS-CoV and SARS-CoV-nsp1ΔD-EΔ3 ( p10 ) , and were daily monitored for 10 days . Mice infected with rSARS-CoV started to lose weight by day 2 , and died by day 6 ( Fig 15B ) . In contrast , although mice infected with the SARS-CoV-nsp1ΔD-EΔ3-p10M mutant initially lost 10% of their weight , at day 5 the mice started to regain weight , fully recovered from the disease , and 100% survived ( Fig 15B ) . SARS-CoV-nsp1ΔD-EΔ3-p10M grew to similar titers as rSARS-CoV ( Fig 15C ) . Nevertheless , unlike the parental virus , lungs of SARS-CoV-nsp1ΔD-EΔ3-p10M-infected mice presented no significant increase of inflammatory cytokines . In addition , no obvious lung lesions , weight increases , nor substantial inflammatory cell infiltration , as determined by macroscopic and histological examination , were observed ( S5 Fig ) . To further support the stability and attenuation of the double mutant virus , additional passages ( up to 20 ) were conducted in mice . Evaluation of the passaged virus virulence showed that rSARS-CoV-nsp1∆D-E∆3 remained attenuated ( Fig 15D ) . These results indicated that despite the mutations that the virus incorporated after their passage in mice , the virus maintained the in vivo attenuated phenotype . To determine whether SARS-CoV-nsp1-ΔD-EΔ3 confers protection against challenge with rSARS-CoV , BALB/c mice were either immunized with SARS-CoV-nsp1ΔD-EΔ3-p1 , -p10C and -p10M or non-immunized , as a control . At 21 days postimmunization , mice were challenged with rSARS-CoV administered by the same route . Non-immunized mice lost weight and all died on day 6 after the challenge ( Fig 16A and 16B ) . In contrast , vaccination with the attenuated mutant viruses completely protected mice against challenge with rSARS-CoV ( Fig 16A and 16B ) , indicating that the double mutant virus is a promising vaccine candidate . We have previously shown that deletion of SARS-CoV E gene leads to an attenuated virus that is a promising vaccine candidate [30–34] . However , since safety and stability are main concerns of live attenuated vaccine candidates , we focused on rSARS-CoV-∆E stability in vitro and in vivo and on the generation of a safe vaccine candidate by identifying the mechanisms of reversion to virulence . Unexpectedly , serial passage of rSARS-CoV-∆E in cell culture resulted in the generation of chimeric proteins composed of a partial duplication of the membrane gene fused to a part of the leader sequence . Our results are in agreement with a recombinant MHV lacking the E protein ( rMHV-∆E ) that was viable but its replication was drastically impaired [60] . rMHV-∆E replicated to 10 , 000-fold lower titers than the parental virus and remarkably , evolved similarly to SARS-CoV-∆E after serial passage in tissue culture cells [61] . Despite that the generation of a chimeric protein was previously observed in MHV when E gene was deleted [61] , the presence of a PBM motif within the inserted chimeric sequence and its main role in providing genetic stability to SARS-CoV-ΔE during passage described in this manuscript , were not previously noticed . Alteration of coronavirus genome was not unexpected due to the high frequency of RNA recombination and mutation described for these viruses , both in cell culture and in animals [37 , 62–64] . All chimeric SARS-CoV M proteins generated in cell culture were expressed , enhancing virus growth in cell culture compared to rSARS-CoV-∆E . Similarly , rMHV-∆E that contained chimeric proteins also showed significant increases in viral yields [61 , 65] . In contrast , mice infected with rSARS-CoVs containing chimeric proteins generated in cell culture showed a decrease in viral titers in the lungs of infected mice even when compared with rSARS-CoV-∆E virus . Similarly , extensive passage in cell culture of other CoVs , including porcine epidemic diarrhea virus ( PEDV ) and transmissible gastroenteritis virus ( TGEV ) , led to less pathogenic strains compared to wild-type viruses , possibly due to the emergence of deletion mutants that lost sequence domain not needed for their growth in cell culture , but that influenced their tropism and in vivo replication [66–68] . Chimeric proteins containing a PBM inserted in the viral genome after passage increased viral fitness in cell culture in a tissue specific manner , i . e . , the chimeric protein inserted into viral genome during passage in monkey cells promoted virus growth in cells from this species , whereas the one inserted during passage in murine cells specifically increased virus fitness in cells from mice . Furthermore , these chimeric proteins did not enhance viral growth nor virulence in vivo . These data indicated that the insertion of chimeric proteins specifically adapted the virus for an optimum growth in cell culture but did not enhance in vivo growth nor virulence . In this context , it is also important to note that the activity of PBMs is dependent on their specific sequence , and also on the sequence context in which they are inserted [27 , 28 , 69 , 70] . Serial passage of rSARS-CoV-∆E in mice introduced a partial duplication of 45 nucleotides in the 8a protein , resulting in its reversion to a virulent phenotype . This phenotype was associated to its ability to activate p38 MAPK and to the induction of inflammatory cytokine expression and increased lung damage , as previously described [24 , 71] . 8a protein is a short transmembrane protein composed of 39 amino acids that forms cation-selective ion channels [72] . SARS-CoV variants with deletions in 8a ORF , have been transmitted and maintained in humans in the late phases of SARS-CoV epidemic [73 , 74] . Interestingly , an 8a protein mutant generated during virus passage in vivo contained a new potential PBM ( CTTV ) localized in the internal region of the carboxy-terminal domain of the protein ( Fig 5 ) . Despite the CTVV sequence was already present in the original 8a protein , it most likely does not represent a functional PBM , as it is located within the transmembrane domain of the 8a protein . Active PBMs are in general located in exposed regions of the proteins , usually the end of the carboxy-terminus or , exceptionally , in internal positions within the carboxy-terminal domain , allowing their interaction with PDZ domains , such as it has been observed in the NS5 proteins of tick-borne encephalitis virus ( TBEV ) and Dengue virus [75 , 76] . However , PBMs forming part of a transmembrane domain are not accessible to PDZ-containing proteins and have not been described [29] . Therefore , the new CTVV sequence placed in an exposed environment may constitute a novel and active PBM . The PBM insertion within 8a protein after passaging in vivo could be due to the fact that the ORF8 is one of the regions where most variations were observed between human and animal isolates of SARS-CoV [4] . In fact , a complete genome sequence of SARS-like coronaviruses in bats isolates showed the presence of a PBM within the ORF8 [5] . As mentioned above , species of bats are a natural host of coronaviruses closely related to those responsible for the SARS outbreak . PDZ domains are among the modules most frequently involved in protein-protein interactions found in all metazoans [69] . In the human genome , there are more than 900 PDZ domains in at least 400 different proteins [77] . Many pathogenic viruses produce PDZ ligands that disrupt host protein complexes for their own benefit , such as hepatitis B virus , influenza virus , rabies virus and human immunodeficiency virus , influencing their replication , dissemination in the host , transmission and virulence [29] . Generation of new proteins containing a PBM after passaging in cell culture and in mice may affect their interaction with a wide range of cellular PDZ-containing proteins , affecting diverse biological functions with high relevance in pathogenesis . E protein PBM participates in two different and independent issues , virus stability and virulence . Our data suggest that when the PBM was present in a proper environment at the end of the E protein , either a native or a mutant protein , viruses remained stable . An independent observation is that the presence of a PBM within E protein confers pathogenicity to the virus [24] . This virulence is prevented either by PBM removal [24] or by the introduction of small deletions within the carboxy-terminus of E protein [53] , which by themselves may cause attenuation or , alternatively , by indirectly affecting the PBM . Our results highlighted the critical requirement of viral proteins containing a PBM in the generation of CoVs with virulent phenotypes , and opened up new approaches for the rational design of genetically stable vaccines . Maintaining the attenuated phenotype of the vaccine candidate after passage in vitro was crucial to avoid the reversion to a virulent phenotype during the design and production of a genetically stable vaccine candidate . To this end , the identification of the relevance of the presence of a functional PBM motif at the carboxy-terminus of a transmembrane protein of the virus has been instrumental in the development of a stable SARS-CoV vaccine candidate . To minimize the risk of regain of virulence after passage , we engineered viruses with small deletions in E gene , instead of deletion of the entire E gene . By preserving the PBM , we observed no evidence for the development of chimeric proteins and thus no gain in virulence . As additional measures to ensure safety of this live attenuated vaccine candidate , we incorporated attenuating mutations into nsp1 , in the context of the rSARS-CoV-ΔE or EΔ3 . Nsp1 was chosen as a second attenuation target because this gene is located at a distant site ( >20 kb ) from that of the E gene in the viral genome , making it very unlikely that a single recombination event with a circulating wt coronavirus could result in the restoration of a virulent phenotype . To analyze the role of SARS-CoV nsp1 in the pathogenesis of the virus , recombinant viruses encoding four different small deletions were generated . Deletion of amino acids 121–129 and 154–165 , in the carboxy terminal region of nsp1 led to virus attenuation , indicating that nsp1 enhanced virus pathogenicity , as was previously shown for MHV [45 , 48 , 49] . Interestingly , these attenuated mutants grew in mice to lower titers than rSARS-CoV , probably by inducing higher IFN responses , indicating that these regions of nsp1 are critical for IFN antagonism . The induction of a higher innate immune response by the nsp1 deletion is most probably responsible for the decrease in SARS-CoV-nsp1* virus titers observed in mice and , to a lesser extent , in DBT-mACE–2 cells . In fact , a rSARS-CoV lacking the nsp1 protein grew poorly in IFN competent cells , but replicated as efficiently as the wt virus in IFN deficient cells [46] , consistent with our findings . Similarly , titers of MHV deleted in nsp1 are restored almost to wild type levels in type I IFN receptor-deficient mice [48] . Immunization with singly deleted rSARS-CoV protected mice against challenge with rSARS-CoV , as it was previously shown with MHV nsp1-deletion mutants [48 , 49] . SARS-CoV-nsp1ΔD-EΔ3 , which contained deletions in nsp1 and E protein , maintained its attenuated phenotype after passage in Vero E6 cells and in mice . In addition , immunization with this double mutant fully protected mice from challenge with the parental virulent virus , indicating that it is a promising vaccine candidate in terms of both stability and efficacy . Both humoral and cellular responses are relevant to protect from SARS [18 , 19 , 78 , 79] . The viruses generated in this work express all viral proteins , except for small regions deleted in the E and nsp1 proteins , therefore have the potential of inducing both antibody and T cell responses , making this type of live vaccine more attractive than subunit or non replicating virus vaccines . Understanding of the molecular mechanisms by which an attenuated SARS-CoV reverted to a virulent phenotype could also be applied to the development of other relevant CoVs vaccines , such as MERS-CoV . Animal experimental protocols were approved by the Ethical Committee of The Center for Animal Health Research ( CISA-INIA ) ( permit numbers: 2011–009 and 2011–09 ) in strict accordance with Spanish National Royal Decree ( RD 1201/2005 ) and international EU guidelines 2010/63/UE about protection of animals used for experimentation and other scientific purposes and Spanish national law 32/2007 about animal welfare in their exploitation , transport and sacrifice and also in accordance with the Royal Decree ( RD 1201/2005 ) . Infected mice were housed in a ventilated rack ( Allentown , NJ ) . The mouse-adapted ( MA15 ) [71] parental virus ( wt ) , and recombinant viruses were rescued from infectious cDNA clones generated in a bacterial artificial chromosome ( BAC ) in our laboratory [32 , 33 , 53 , 80] . Vero E6 and BHK cells were kindly provided by E . Snijder ( University of Leiden , The Netherlands ) and H . Laude ( Unité de Virologie et Immunologie Molecularies , INRA , France ) , respectively . The mouse delayed brain tumor ( DBT ) cells expressing the murine receptor ( ACE2 ) for SARS-CoV ( DBT-mACE2 ) were generated in our laboratory [38] . In all cases , cells were grown in Dulbecco's modified Eagle's medium ( DMEM , GIBCO ) supplemented with 25 mM HEPES , 2 mM L-glutamine ( SIGMA ) , 1% non essential amino acids ( SIGMA ) and 10% fetal bovine serum ( FBS , Biowhittaker ) . Virus titrations were performed in Vero E6 cells as previously described [33] . 8 week-old specific-pathogen-free BALB/c Ola Hsd mice females were purchased from Harlan Laboratories . BALB/c mice were maintained for 8 additional weeks in the animal care facility at the National Center of Biotechnology ( Madrid ) . For infection experiments , mice were anesthetized with isoflurane and intranasally inoculated at the age of 16 weeks with 100 , 000 plaque forming units ( pfu ) of the indicated viruses . All work with infected animals was performed in a BSL3 laboratory ( CISA , INIA ) . Mutant viruses ( SARS-CoV-nsp1* ) with small deletions covering different regions of nsp1 protein ( SARS-CoV-nsp1-∆A , -∆B , -∆C and -∆D ) , were constructed using an infectious cDNA clone . cDNA encoding the genome of SARS-CoV-MA15 strain was assembled in a bacterial artificial chromosome ( BAC ) ( plasmid pBAC-SARS-CoV-MA15 ) [32 , 57 , 80] . DNA fragments containing nucleotides 8142 to 9211 , comprising the nsp1 gene of the SARS-CoV genome were generated by overlap extension PCR using as template the plasmid pBAC-SARS-CoV-MA15 and the primers indicated in S4 Table . The final PCR products were digested with the enzymes AvrII and BstBI and cloned into the intermediate plasmid pBAC-SfoI-MluI-SARS-CoV that contains the first 7452 nucleotides of the SARS-CoV infectious cDNA clone [80] , to generate plasmids pBAC-SfoI-MluI SARS-CoV-nsp1* ( pBAC-∆A , -∆B , -∆C , and -∆D ) [80] . The plasmids pBAC-SfoI-MluI-SARS-CoV-nsp1* were digested with the restriction enzymes SfoI and MluI and the fragments were inserted into the plasmid pBAC-SARS-CoV-MA15 , digested with the same restriction enzymes , to generate pBAC-SARS-CoV-MA15-nsp1* plasmids . Mutant viruses SARS-CoV-nsp1ΔD-ΔE and SARS-CoV-nsp1ΔD-EΔ3 were generated using the plasmids pBAC-SARS-CoV-MA15-ΔE and -EΔ3 [33 , 53] . The plasmids were digested with the enzymes BamHI and RsrII and the digested fragments were exchanged with the fragment of plasmid pBAC-SARS-CoV-MA15-nsp1∆D , to generate pBAC-SARS-CoV-MA15-nsp1ΔD-ΔE and pBAC-SARS-CoV-MA15-nsp1ΔD-EΔ3 plasmids . Two fragments representing the nucleotides containing the chimeric proteins MCH-EPBM and 3aCH-3aPBM were chemically synthesized ( BioBasic Inc ) to generate SARS-CoV mutants . The final PCR products and synthesis fragments were digested with enzymes BamHI and MfeI and cloned into the intermediate plasmid psl1190+BamHI/SacII-SARS-CoV to generate the plasmids psl1190-∆E-MCH-EPBM and psl1190-∆E-3aCH-3aPBM . The plasmid psl1190+BamHI/SacII SARS-CoV contains a fragment corresponding to nucleotides 26045 to 30091 of the SARS-CoV infectious cDNA clone engineered into plasmid psl1190 ( Pharmacia ) [80] . These constructs were cloned in the infectious pBAC-SARS-CoV-MA15-∆E with the enzymes BamHI and SacII . Mutant virus rSARS-CoV-∆E-8a-dup with small duplication of 8a protein was constructed using an infectious cDNA clone . cDNA encoding the genome of SARS-CoV-MA15-∆E strain was assembled in a bacterial artificial chromosome ( BAC ) ( plasmid pBAC-SARS-CoV-MA15-∆E ) [32 , 57 , 80] . DNA fragments containing nucleotides 27779 to 27898 , comprising the 8a gene of the SARS-CoV genome were generated by overlap extension PCR using as template the plasmid pBAC-SARS-CoV-MA15 and the primers indicated in S4 Table . The final PCR products were digested with the enzymes XcmI and NheI and cloned into the intermediate plasmid pBAC-BamHI-NheI-SARS-CoV that contains the nucleotides ( nt ) 26044 to 28753 nucleotides of the SARS-CoV infectious cDNA clone [80] , to generate plasmid pBAC-BamHI-NheI-SARS-CoV-∆E-8a-dup [80] . The plasmid pBAC-BamHI-NheI-SARS-CoV-∆E-8a-dup was digested with the restriction enzymes BamHI and NheI and the fragments were inserted into the plasmid pBAC-SARS-CoV-MA15 , digested with the same restriction enzymes , to generate pBAC-SARS-CoV-MA15-∆E-8a-dup plasmid . The viruses were rescued in BHK and Vero E6 cells as previously described [33] . Viruses were cloned by three rounds of plaque purification . Subconfluent monolayers ( 90% confluency ) of Vero E6 and DBT-mACE2 on 12 . 5 cm2 flasks were infected at a multiplicity of infection ( moi ) of 0 . 001 with the indicated viruses . Culture supernatants were collected at 0 , 4 , 24 , 48 and 72 hpi and virus titers were determined as previously described [33] . BALB/c mice were anesthetized with isoflurane and intranasally inoculated with 100 , 000 plaque forming units ( pfu ) of virus in 50 μL of DMEM . Weight loss and mortality were evaluated daily . For protection experiments mice were immunized intranasally with 6000 pfu of the attenuated viruses , and then challenged with an intranasal inoculation of 100 , 000 pfu of SARS-CoV at 21 days post-immunization . Mice were monitored daily for weight loss and mortality . To determine SARS-CoV titers , lungs were homogenized in PBS containing 100 UI/ml penicillin , 0 . 1 mg/ml streptomycin , 50 μg/ml gentamicin , and 0 . 5 μg/ml amphotericin B ( Fungizone ) , using a gentleMACS dissociator ( Miltenyi Biotec ) and virus titrations were performed in Vero E6 cells as described above . Viral titers were expressed as pfu/g tissue . Mice were sacrificed at 2 and 4 dpi . Lungs were removed , fixed in 10% zinc formalin for 24 h at 4°C and paraffin embedded . Histological examination was performed using hematoxylin and eosin staining of sections . BALB/c mice were anesthetized with isoflurane and intranasally inoculated with 100 , 000 pfu of the indicated recombinant viruses in 50 μL of DMEM . Two days after inoculation , mice were euthanized , and their lungs were removed and homogenized as previously described . The lung homogenate was clarified by low-speed centrifugation at 3 , 000 rpm for 12 min , and 100 μL of the supernatant was administered intranasally to naive mice . Intranasal inoculation of BALB/c mice with clarified supernatants of lung homogenates collected 2 dpi was repeated 10 times . Cell lysates were resolved by sodium dodecyl sulfate-polyacrylamide gel electrophoresis ( SDS-PAGE ) , transferred to a nitrocellulose membrane by wet immunotransfer and processed for Western blotting . The blots were probed with monoclonal antibodies for p38 MAPK ( dilution 1:500; Cell Signaling ) , phospho-p38 MAPK ( dilution 1:500; Cell Signaling ) and actin ( dilution 1:10 , 000; Abcam ) or polyclonal antibodies specific for M ( dilution 1:1000; Biogenes ) and MCH-DBT ( dilution 1:1000; Biogenes ) proteins . Both polyclonal antibodies recognizing the parental SARS-CoV M protein or the MCH-DBT protein were generated by Biogenes ( Germany ) as previously described [81] using synthetic peptides corresponding to the residues RTRSMWSFNPETNILLNVPLRGTIVTRPLM and PLMNLSLVL , respectively . Bound antibodies were detected with horseradish peroxidase-conjugated goat anti-rabbit ( dilution 1:30 , 000; Cappel ) and the Immobilon Western chemiluminescent substrate ( Millipore ) . DBT-mACE2 cells were infected with SARS-CoV , SARS-CoV-nsp1-ΔC and -ΔD at a moi of 0 . 125 . Total RNAs from DBT-mACE2 infected cells were extracted at 48 hpi using the Qiagen RNeasy kit according to the manufacturer’s instructions . Quantitative reverse transcription-polymerase chain reaction ( qRT-PCR ) reactions were performed at 37°C for 2 h using the High Capacity cDNA transcription kit ( Applied Biosystems ) and 100 ng of total RNA and random hexamer oligonucleotides . Cellular gene expression was analyzed using TaqMan gene expression assays ( Applied Biosystems ) specific for Mus musculus genes ( S3 Table ) . Data were acquired with an ABI PRISM 7000 sequence detection system ( Applied Biosystems ) and analyzed with ABI PRISM 7000 SDS version 1 . 0 software . Gene expression in mock-infected cells and SARS-CoV , SARS-CoV-nsp1-ΔC and -ΔD-infected cells was compared . Quantification was achieved using the 2-ΔΔCt method , which analyzes relative changes in gene expression in qPCR experiments ( Livak and Scmittgen , 2001 ) . The results of three independent experiments were analyzed . All experiments and data analysis were MIQE compliant [82] . Lung sections from infected animals were collected at 2 dpi and homogenized using gentleMACS Dissociator ( Miltenyibiotec ) . Then , total RNA was extracted using the RNeasy purification kit ( Qiagen ) . Reactions were performed at 37°C for 2 h using a High Capacity cDNA transcription kit ( Applied Biosystems ) with 100 ng of total RNA and random hexamer oligonucleotides . Cellular gene expression was analyzed using TaqMan gene expression assays ( Applied Biosystems ) specific for mouse genes ( S4 Table ) . Data representing the average of three independent experiments were acquired and analyzed as previously described [57] . All experiments and data analysis were MIQE compliant [82] . The computer modeling of 8a protein structure was performed with the raptorX server http://raptorx . uchicago . edu [83] . The predicted structures were visualized using Pymol ( http://www . pymol . org/ ) . Student´s t test was used to analyze differences in mean values between groups . All results are expressed as means ± standard errors of the means . P values of <0 . 05 were considered statistically significant . The UniProt ( http://www . uniprot . org/ ) accession numbers for genes and proteins discussed in this paper are: SARS-CoV E protein , P59637; SARS-CoV 8a protein , Q19QW2; SARS 3a protein , P59632; SARS M protein , P59596; mouse IFN-β , P01575; mouse IRF1 , P15314; mouse DDX58 , Q6Q899; mouse STAT1 , P42225; human p38 MAPK , Q16539; human ACE2 , Q9BYF1; mouse ACE2 , Q8R0I0; mouse CXCL10 , P17515; mouse CCL2 , P10148; mouse IL6 , P08505; mouse 18S , O35130; human actin , P60709 .
Zoonotic coronaviruses , including SARS-CoV , Middle East respiratory syndrome ( MERS-CoV ) , porcine epidemic diarrhea virus ( PEDV ) and swine delta coronavirus ( SDCoV ) have recently emerged causing high morbidity and mortality in human or piglets . No fully protective therapy is still available for these CoVs . Therefore , the development of efficient vaccines is a high priority . Live attenuated vaccines are considered most effective compared to other types of vaccines , as they induce a long-lived , balanced immune response . However , safety is the main concern of this type of vaccines because attenuated viruses can eventually revert to a virulent phenotype . Therefore , an essential feature of any live attenuated vaccine candidate is its stability . In addition , introduction of several safety guards is advisable to increase vaccine safety . In this manuscript , we analyzed the mechanisms by which an attenuated SARS-CoV reverted to a virulent phenotype and describe the introduction of attenuating deletions that maintained virus stability . The virus , engineered with two safety guards , provided full protection against challenge with a lethal SARS-CoV . Understanding the molecular mechanisms leading to pathogenicity and the in vivo evaluation of vaccine genetic stability contributed to a rational design of a promising SARS-CoV vaccine .
You are an expert at summarizing long articles. Proceed to summarize the following text: Kaposi's sarcoma-associated herpesvirus ( KSHV ) is a human herpesvirus that causes Kaposi's sarcoma and is associated with the development of lymphoproliferative diseases . KSHV reactivation from latency and virion production is dependent on efficient transcription of over eighty lytic cycle genes and viral DNA replication . CTCF and cohesin , cellular proteins that cooperatively regulate gene expression and mediate long-range DNA interactions , have been shown to bind at specific sites in herpesvirus genomes . CTCF and cohesin regulate KSHV gene expression during latency and may also control lytic reactivation , although their role in lytic gene expression remains incompletely characterized . Here , we analyze the dynamic changes in CTCF and cohesin binding that occur during the process of KSHV viral reactivation and virion production by high resolution chromatin immunoprecipitation and deep sequencing ( ChIP-Seq ) and show that both proteins dissociate from viral genomes in kinetically and spatially distinct patterns . By utilizing siRNAs to specifically deplete CTCF and Rad21 , a cohesin component , we demonstrate that both proteins are potent restriction factors for KSHV replication , with cohesin knockdown leading to hundred-fold increases in viral yield . High-throughput RNA sequencing was used to characterize the transcriptional effects of CTCF and cohesin depletion , and demonstrated that both proteins have complex and global effects on KSHV lytic transcription . Specifically , both proteins act as positive factors for viral transcription initially but subsequently inhibit KSHV lytic transcription , such that their net effect is to limit KSHV RNA accumulation . Cohesin is a more potent inhibitor of KSHV transcription than CTCF but both proteins are also required for efficient transcription of a subset of KSHV genes . These data reveal novel effects of CTCF and cohesin on transcription from a relatively small genome that resemble their effects on the cellular genome by acting as gene-specific activators of some promoters , but differ in acting as global negative regulators of transcription . Infection with Kaposi's sarcoma-associated herpesvirus ( KSHV , HHV8 ) is causally associated with Kaposi's sarcoma ( KS ) , primary effusion lymphoma ( PEL ) and multicentric Castleman's disease ( for a review , see reference [1] ) . KSHV maintains a persistent latent infection as an episome in B lymphocytes , from which it occasionally reactivates , enters a lytic cycle of replication , and produces infectious virions . Released virions infect other lymphocytes to maintain the latent reservoir or are transmitted from person-to-person in saliva . Cell-mediated immunity is essential for limiting KSHV reactivation and pathogenesis , but cellular epigenetic regulatory mechanisms may also play an important role in limiting viral replication . The balance between lytic and latent infection is an important determinant of pathogenicity . Lytic herpesvirus reactivation , while often more common in states of immunosuppression , is nevertheless apparently stochastic , and may occur quite variably among fully immunocompetent individuals [2] . Lytic replication and viral gene expression are important in pathogenesis for several reasons . First , expansion of the reservoir of infected cells is at least partly dependent on recurrent reactivation of human gammaherpesviruses . Thus long-term acyclovir suppression of lytic replication led to a significant decrease over time in the latent Epstein-Barr virus ( EBV ) load in B lymphocytes of immunocompetent patients [3] . Second , lytic replication and gene expression appears to contribute to oncogenesis in several settings where even a minority of infected cells is permissive for lytic replication [4]–[6] . Several lytic KSHV gene products have anti-apoptotic , proliferative or immunosuppressive properties , increasing the likelihood of malignant transformation by paracrine and autocrine mechanisms [7] , [8] . The role of lytic replication in oncogenesis is supported by the decreased incidence of KS in KSHV infected individuals who received long-term antiviral therapy for other infections [9] . Understanding the basic mechanisms by which the host cell maintains control of lytic viral replication and viral strategies to overcome such control is therefore central to devising novel therapies aimed at these control points . Host proteins that play multiple roles in chromatin organization , transcriptional regulation and chromosome segregation have recently been shown to also bind herpesvirus genomes at specific sites and regulate gene expression [10]–[13] . CTCF is an 11 zinc finger sequence-specific DNA binding protein with roles in transcription activation and repression , gene insulation , enhancer blocking and long range chromatin interactions [14] , [15] . CTCF binds to between 14 , 000 to 20 , 000 sites in the human genome and is functionally important in regulation of several hundred genes based on knockdown studies [16]–[18] . Initial studies suggested that CTCF exerted activating or repressing effects on promoters by direct binding in the manner of classic transcription factors [19] . However , its role in global gene regulatory functions was demonstrated by its ability to block enhancer function when interposed between enhancer elements and target promoters [20] . Subsequent studies have shown that CTCF binding mediates insulation throughout the human genome [18] , [21] . In addition , CTCF may act as a barrier element , demarcating regions of heterochromatin and open chromatin , thereby isolating areas of low and high transcriptional activity . Based on binding studies delineating intra-chromosomal interactions , CTCF mediates three dimensional chromatin structure via long-range interactions . Cohesin , a complex of four proteins , SMC1 , SMC3 , SCC1/Rad21 and SA1/2 , essential for chromatid segregation , has also been recognized as a global regulator of transcription ( for a review , see reference [22] ) . The four proteins form a ring-shaped structure that encloses chromatids . Several other proteins are associated with cohesin , and regulate the dynamic association of cohesin with chromatin as it is sequentially loaded and dissociated from chromosomes during mitosis and segregation . Specificity of cohesin localization is complex and likely mediated by multiple proteins including NIPBL , mediator , transcription factors and CTCF . Thus while cohesin binds to many CTCF sites , it also binds to sites on the genome independently of CTCF . Although cohesin may have both positive and negative effects on transcription , many of its effects are thought to be mediated by facilitating and stabilizing long-range interactions between promoters and enhancers to which it binds . The most likely mechanism is that cohesin causes topological linking of DNA sequences in cis similar to its role in chromatid linkage in trans . Cohesin also is involved in regulation of polII pausing at promoters and relieves pausing , promoting RNA elongation [23] . CTCF and cohesin bind at distinct sites on herpesvirus genomes , including herpes simplex virus , EBV and KSHV . CTCF has been implicated in regulating gene expression during latent EBV infection by mechanisms that likely involve both insulator function and modification of genome conformation by causing formation of intragenomic loops [13] . During KSHV infection in primary effusion lymphoma cells , both cohesin and CTCF play a regulatory role in latent and possibly lytic gene expression [12] , [24]–[26] . Chromosome conformation capture assays have demonstrated that a cohesin/CTCF site in the 5′ region of the major latency KSHV transcript forms contacts with a site close to the primary gene necessary for lytic reactivation ( ORF50/RTA ) and with the 3′ region of the latency region . [12] . Mutation of the CTCF site led to increased latency gene expression , suggesting that CTCF and cohesin play a repressive role in latent gene expression . Interestingly however , deletion of this site also led to a loss of stable viral episome maintenance . Although knockdown of cohesin components led to increased transcription of lytic genes in PEL cells , depletion of CTCF had virtually no such effect [24] . Conversely , mutation of the CTCF site , which would be predicted to disrupt both cohesin and CTCF binding , led to decreased lytic gene expression . In this study , we have performed a detailed analysis of the role of cohesin and CTCF in regulating KSHV lytic replication . By employing siRNAs specific for CTCF and cohesin , we have explored their role in regulating KSHV lytic replication . Using ChIP-Seq , we have defined at high resolution the dynamic changes in cohesin and CTCF binding that occur during lytic KSHV replication and reactivation from latency . The distinct regulatory roles of cohesin and CTCF have also been further defined by transcriptional profiling of infected cells undergoing lytic replication under conditions of cohesin and CTCF depletion . These studies reveal novel mechanisms of gene regulation by CTCF and cohesin during KSHV replication and establish their role as host restriction factors for KSHV replication . 293 and 293T cells were grown at 37°C in Dulbecco's modified Eagle's medium ( DMEM ) supplemented with 10% fetal bovine serum ( FBS ) and glutamine . iSLK cells [27] were maintained in DMEM containing 10% charcoal stripped FBS ( Sigma ) and glutamine with 250 µg/ml neomycin and 1 µg/ml puromycin . iSLK cells were infected with WT KSHV derived from bacmid BAC16 , expressing eGFP and hygromycin resistance [28] . KSHV-infected iSLK cells were maintained in 1 . 2 mg/ml hygromycin , 250 µg/ml neomycin and 1 µg/ml puromycin . Protein samples were analyzed by sodium dodecyl sulfate-polyacrylamide gel electrophoresis ( SDS-PAGE ) and immunoblotted with rabbit polyclonal anti-CTCF ( Millipore ) , anti-Rad21 ( Bethyl ) or anti-actin monoclonal antibody ( Sigma ) and horseradish peroxidase-conjugated secondary antibody ( GE Healthcare ) , followed by visualization with a Clarity Western ECL Substrate Kit ( Bio-Rad ) . Image capture was performed with a BioRad GelDoc system . 293T cells were plated at 600 , 000/well in 6-well plates . CTCF or Rad21 were knocked down by transfection with On-target SMARTpool CTCF siRNA or Rad21 siRNA , or mock-depleted with negative control siRNA ( see below ) . 48 h later , 293T cells were transfected with 1 ug/well pDD398 ( ORF57 promoter-luciferase reporter ) plus 1 ug/well pDD267 ( ORF50 expression plasmid in pCDNA3 ) or empty pCDNA3 vector , using Transit-293 ( Mirus ) per the manufacturer's protocol . Each transfection was performed in triplicate . 48 h later , cells were harvested and lysed in reporter lysis buffer ( Promega ) . Luciferase assays were performed in triplicate with 0 . 5 ul of each lysate using Promega's Luciferase Reporter Assay System per the manufacturer's protocol . CTCF ( L-020165-00-0005 ) , Rad21 ( L-006832-00-0005 ) and negative control On-target plus Smart Pool siRNAs ( D-001810-03 ) were purchased from Thermo Scientific . Each siRNA was transfected into SLK KSHV WT cells using Lipofectamine RNAiMAX ( Invitrogen ) according to the manufacturer's protocol and a 10 nM final concentration of siRNA . Similar experiments were also performed with siGENOME Non-Targeting SiRNA #5 , D-001210-05-05 , SiGENOME human CTCF siRNA , M-020165-02-0005 and SiGENOME human Rad21 siRNA , M-006832-01-0005 , purchased from the same manufacturer . Immunoblotting was performed to verify knockdown of the relevant protein . The chromatin immunoprecipitation ( ChIP ) assay was performed as follows . Briefly , 25 million iSLK cells were harvested and washed with cold PBS containing protease inhibitors ( Sigma ) . Protease inhibitors were added to all solutions in this protocol with the exception of low salt wash buffer . Cells were transferred to DNA LoBind tubes ( Eppendorf ) in 1 ml of PBS . Cell fixation was performed by addition of 37% formaldehyde to 1% final concentration and rocking gently for 10 min at room temperature . 2M glycine was added to 0 . 128M final concentration . After centrifugation and washing with cold PBS , cell pellets were resuspended in 2 . 5 ml ice cold swelling buffer ( 5 mM PIPES pH 8 . 0 , 85 mM KCl , 0 . 5% NP-40 ) for 10 min on ice . Cell nuclei were pelleted and resuspended in 2 ml SDS lysis buffer ( 1% SDS , 10 mM EDTA and 50 mM Tris-HCl , pH 8 ) . Lysed nuclei were sonicated on ice to yield approximately 500-bp DNA fragments using a Branson Sonifier 450 . The extent of DNA fragmentation was confirmed by gel electrophoresis of aliquots of the sonicated nuclear preparation . After extract clearing by centrifugation , supernatants were diluted 1∶5 in CHIP dilution buffer ( 0 . 01% SDS , 1% Triton X-100 , 1 . 2 mM EDTA , 16 . 7 mM Tris-HCL , pH 8 and 167 mM NaCl ) . Rabbit polyclonal IgG ( Bethyl ) 1 µg/ml and 50 µl/ml of 50% Protein-A agarose slurry were used to preclear supernatants for 2 hour . Protein-A beads were pelleted , and supernatants were used for immunoprecipitation . 2% of each supernatant was reserved for use as input samples . 8 ug anti-CTCF ( Millipore ) or Rad21 ( Bethyl ) antibody was added and the tubes were rocked at 4°C overnight . 30 µl/2 ml of 50% Protein-A agarose slurry was added and incubated for 2 hours at 4°C with rotation . The tubes were centrifuged rapidly and the beads were washed 3 times with cold low salt wash buffer ( 0 . 1% SDS , 1% Triton X-100 , 2 mM EDTA , 20 mM Tris-HCl , pH 8 . 1 and 150 mM NaCl ) and once with cold TE buffer . Antibody-protein complexes were eluted 6 times with freshly prepared , pre-heated elution buffer ( 1% SDS , 0 . 84% NaHCO3 ) at 65°C . Total elution volume was 1 ml for each immunoprecipitation . Sodium chloride was added to the elutions and input samples to a final concentration of 200 mM NaCl and heated at 65°C for 4 hours . RNase A and proteinase K were added to digest RNA and protein . Finally , DNA was purified from the eluted samples using Qiaquick PCR Purification Kit ( Qiagen ) according to the manufacturer's protocol . After DNA purification , libraries were constructed from the chromatin-immunoprecipitated DNA and input samples using the ChIP-Seq DNA sample prep kit ( Illumina , San Diego , CA ) . Single-end reads of 50 cycles were sequenced on an Illumina HiSeq2000 platform . Sequence reads were mapped to the KSHV genome ( NC_009333 . 1 ) . Library preparations , Illumina sequencing and sequencing data analysis were performed by the University of Utah Huntsman Cancer Institute Microarray facility . Total cellular RNA was isolated from washed cell pellets using Qiazol and Qiagen miRNeasy columns according to the manufacturer's protocols . mRNA was purified from 6 µg total RNA using Qiagen Oligotex mRNA Midikit ( Qiagen ) . cDNA libraries were prepared using the ABI high Capacity cDNA Reverse Transcription Kit with RNase inhibitor ( Applied Biosystems ) . Real-time Quantitative PCR ( qPCR ) was performed with SYBR green PCR Master Mix ( Applied Biosystems ) according to the manufacturer's protocol . Each sample was analyzed in triplicate with gene specific primers and β-actin was used as the endogenous control . The gene-specific primers were as follows: ORF6-2093F: 5′-CTGCCATAGGAGGGATGTTTG-3′; ORF6-2158R: 5′- CCATGAGCATTGCTCTGGCT-3′ ORF25-3733F:5′-CTCGGCGACGTGCTATACAAT-3′; ORF25-3803R: 5′-TGCCGACAAGGACTGTACATG-3′; ORF47 Q1F: 5′-AGCCTCTACCCTGCCGTTGTTCT-3′; ORF47 Q1R 5′-ACGACCGCGACTAAAAATGACCT-3′; ORF57 Q1-5: 5′-GCAGAACAACACGGGGCGGA-3′ ORF57Q2-3′:5′-GTCGTCGAAGCGGGGGCTCT-3′ ORF59 Q1F , 5′-CTCCCTCGGCAGACACAGAT-3′; ORF59 Q1R , 5′-GCGTGGTGCACACCGACGCCC-3′; K2-430F: 5′-ACCCTTGCAGATGCCGG-3′; K2-494R: 5′- GGATGCTATGGGTGATCGATG-3′ K5 Q1F: 5′-TAAGCACTTGGCTAACAGTGT-3′ K5 Q1R: 5′-GGCCACAGGTTAAGGCGACT-3′ vIRF-1_lytF: 5′-CGGCATAGCTGTGCTTACCA-3′; vIRF-1R: 5′- CATTGTCCCGCAACCAGACT-3′; PAN Q1F , 5′-CCGCCGATTGTGGGTTGATT-3′; PAN Q1R , 5′-TTTTGTTCTGCGGGCTTATGGAG-3′; B-actin Q1F: 5′-TCAAGATCATTGCTCCTCCTGAG-3′ B-actin Q1R: 5′-ACATCTGCTGGAAGGTGGACA-3′ RNA samples from iSLK cells were prepared using Qiagen miRNeasy kits . 1 . 5 µg of each RNA were poly ( A ) selected , and libraries were prepared using the Illumina TruSeq RNA sample preparation protocol ( catalog no . RS-930-2001 ) and validated using an Agilent Bioanalyzer . RNA sequencing libraries were sequenced ( 50 cycle single-end reads ) using an Illumina HiSeq2000 instrument . To induce KSHV lytic gene expression or virus replication . iSLK cells were treated with 1 ug/ml doxycycline . Cells were harvested at 24 or 48 hr for RNA preparation . For virus production , supernatants of the cells were harvested 5 days after induction , cleared by centrifugation twice , and filtered through a 0 . 80 µM pore-size cellulose acetate filter . Serial dilutions of supernatants were used to infect 293T cells . 48 hours after infection , flow cytometry was performed on samples in which 20∼40% of the infected cells were GFP positive . Based on the dilution factor , virus titers in the iSLK cell supernatant were calculated . Pellets of the cells from which supernatant was harvested were processed for DNA isolation using Qiagen DNeasy Blood and Tissue kit . 50 ng of each DNA were used for qPCR using primers specific for ORF59 ( see above ) and SYBR green PCR MasterMix ( ABI ) . In order to investigate the potential role of CTCF as a host restriction factor for KSHV lytic replication and reactivation from latency , we specifically depleted KSHV infected cells of CTCF prior to inducing lytic replication . Robust and synchronous reactivation of KSHV from latency was achieved by using SLK cells stably transduced with a doxycycline-inducible viral transactivator , KSHV ORF50/RTA [27] . These RTA-inducible SLK cells ( iSLK ) were infected with the Bac16 KSHV strain that expresses hygromycin resistance and GFP [28] . Infected cells were 100% GFP positive when maintained under hygromycin selection ( data not shown ) . Highly efficient CTCF depletion was achieved by lipid-mediated transfection of iSLK cells with siRNA specific for CTCF ( Figure 1A ) . In order to assess the effect of CTCF depletion on KSHV reactivation and virion production , cells were transfected with either CTCF-specific siRNA or control siRNA and treated with doxycycline 48 hours later . KSHV reactivation was allowed to proceed and virion-containing supernatant was harvested 120 hours after induction of lytic replication with doxycycline . Infectious virus production was measured by infection of 293 cells with serial dilutions of virus supernatant followed by flow cytometry of infected cells . Virus titer in the supernatant can thus be accurately quantitated as GFP-transducing units . As shown in Figure 1B , CTCF depletion prior to induction of lytic replication led to a marked increase in virion production ( 20–25 fold ) , compared to control cells induced to replicate . There was no visible or flow cytometry-detectable release of virus without doxycycline-induced RTA expression from either CTCF depleted cells or in control cells , indicating that RTA is still absolutely required for lytic replication ( data not shown ) . Previous investigations of the role of CTCF in KSHV replication in PEL cells detected no effect of CTCF knockdown on KSHV lytic replication [24] . This is therefore the first demonstration of CTCF acting as a restriction factor for KSHV virus production . CTCF may act as a transcriptional activator or inhibitor by a variety of mechanisms , including alteration of chromosomal conformation by formation of intrachromosomal loops . Previous studies have reported decreased transcription of several lytic KSHV genes upon partial CTCF knockdown , indicating CTCF-mediated transcriptional activation [12] , [24] . Our experiments suggested that CTCF might also repress KSHV lytic genes , leading to increased virus production when CTCF was completely depleted . In order to determine if the increased KSHV replication observed when CTCF was knocked down in SLK cells might be due to transcriptional mechanisms , we assessed changes in mRNA levels of representative KSHV lytic genes by qPCR after CTCF knockdown . Cells were transfected with CTCF or control siRNA and cellular RNA was isolated 48 h after lytic replication was induced as previously described . While ORF57 ( early ) and ORF6 ( early ) lytic mRNA expression were enhanced approximately four-fold by CTCF depletion , there was a less significant increase in other early ( ORF59 ) or late ( ORF25 ) lytic mRNAs ( Figure 2 A–D ) . In addition , expression of PAN RNA , a nuclear non-coding polyadenylated RNA important for lytic reactivation [32] , was not enhanced by CTCF depletion ( Figure 2E ) . It therefore appeared that CTCF knockdown might enhance expression of KSHV genes in a gene-specific manner , consistent with transcriptional repression due to site-specific binding . CTCF binds at specific sites on the KSHV genome during latency and mediates intrachromosomal interactions , primarily between the ORF50 region and the major latency region [12] , [24] , [25] . Since CTCF appeared to play a role in restricting productive KSHV replication , it seemed likely that CTCF dissociation from one or more sites might occur upon lytic reactivation . A comprehensive analysis of dynamic changes in CTCF binding during reactivation from latency has not been previously performed . We therefore performed ChIP-Seq studies on iSLK cells at serial times after induction of lytic replication to characterize CTCF binding to the KSHV genome during the process of reactivation from latency . It should be noted that the system employed in these studies does not require sodium butyrate or other chemical inducers , which have broad effects on gene expression and epigenetic state . Rather , induction of lytic KSHV replication in iSLK cells relies solely on transcriptional activation by KSHV RTA . After treatment with doxycycline to induce RTA expression and lytic replication , we harvested cells at 0 , 3 and 5 days after induction . Cells were treated with formaldehyde to cross-link DNA and protein , followed by DNA fragmentation and immunoprecipitation with anti-CTCF antibody . Immunoprecipitated DNA and input DNA were then analyzed by high-throughput DNA sequencing . The results , shown in Figure 3 , reveal several important aspects of dynamic CTCF changes during KSHV reactivation . First , the high resolution map provided by the deep sequencing identifies at least thirty distinct areas of CTCF localization during latency . All contain high-probability sequence motifs and are consistent with previously published literature [24] , [26] , [33] . Second , there are clearly broad regions in which CTCF binding decreases as lytic replication progresses . Importantly , however , wholesale eviction of CTCF from the genome does not occur . Rather , binding at most sites in the latency gene locus , from approximately nt 117 , 000 to the 3′ end of the genome remains preserved ( blue bar ) . Similarly , binding at the major CTCF site at approximately nt 52 , 000 ( red arrow ) is also maintained . Further evidence of the site-specific nature of these dynamic changes in CTCF binding is evident at another site ( blue arrow ) where CTCF occupancy is maintained despite its loss at neighboring sites . Previous work in lymphoma cells had indicated that cohesin components , including Rad21 , but not CTCF , repress KSHV immediate-early gene expression [24] . Since CTCF depletion alone led to greatly increased KSHV virion production , and cohesin is known to bind to many CTCF sites , it was of interest to determine the effect of cohesin disruption on KSHV replication in our system . Cohesin is a complex of four core proteins , SMC1 , SMC3 , SCC1/Rad21 and SA1/2 that encloses chromatids and may act to facilitate intrachromosomal looping . Depletion of Rad21 effectively disrupts cohesin function in DNA binding and transcriptional regulation [34] . Rad21 knockdown was therefore carried out with Rad21-specific siRNA , and KSHV virion production after induction of replication was measured , as was done in CTCF knockdown experiments ( Figure 4 ) . Virus production after Rad21 depletion was compared to virus production in cells transfected with control siRNA and revealed that Rad21 depletion enhanced KSHV virion yield even more robustly than CTCF depletion ( approximately 90-fold , Figure 4A ) . In subsequent experiments , CTCF depletion was performed in parallel with Rad21 depletion and confirmed that Rad21 represses KSHV virion production more efficiently than does CTCF ( Rad21 depletion enhanced virus production approximately 130-fold versus 20-fold for CTCF depletion , Figure 4B ) . To determine whether the effect of Rad21 or CTCF KD on infectious KSHV virion production was due to increased KSHV replication , we measured KSHV genome copy number by qPCR on DNA samples from cells that were induced to replicate after KD of either CTCF or Rad21 . The results demonstrated that the KSHV copy number in each sample correlated extremely well with the increases in infectious virion titer . KD of CTCF or Rad21 led to approximately 20-fold or 150-fold increases in copy number , respectively ( Figure 4C ) . Completeness of Rad21 and CTCF depletion was verified by Western blotting of lysates from siRNA-transfected cells ( Figure 4D ) . These experiments were repeated with a completely different pool of siRNAs and a different control siRNA . The results were consistent with those shown above ( Figure S1 ) . In order to ensure that the effects of siRNA depletion were not due to adventitious effects of siRNA carryover during infection , we performed infections of 293 cells with virus-containing supernatant and added supernatant from siRNA-transfected but uninduced cells , which had no effect on virus titers measured by flow cytometry ( Figure S2 ) . These data demonstrate that Rad21 , although it is thought to bind primarily at CTCF sites , has effects independent of CTCF binding , and is an even more potent repressor of KSHV replication . Although cohesin is known to bind to many CTCF sites , the binding patterns of cohesin and CTCF to the human genome are not completely concordant [34]–[36] . Since Rad21 depletion appeared to have much more potent effects on KSHV lytic replication than CTCF depletion , it was critical to map the binding of Rad21 during lytic replication and compare its pattern to that of CTCF binding during the same period . We therefore performed a ChIP-seq analysis of Rad21 localization analogous to that conducted for CTCF . KSHV-infected iSLK cells were treated with doxycycline , and DNA was harvested for ChIP at 0 , 3 and 5 days post-induction . Several significant differences between Rad21 and CTCF binding were immediately revealed by the ChIP-Seq analysis ( Figure 5 ) . Comparison with the previously described CTCF experiment demonstrates that there are twelve major peaks of Rad21 binding , significantly fewer than for CTCF . These are consistent with previously identified cohesin-binding sites but include at least one additional novel Rad21 binding locus at nt 28819–29553 ( 24 , 26 ) . In addition , the profile of Rad21 differs from that of CTCF , with most peaks being much narrower , and the relative ratios of the major peaks differing from those of CTCF . Finally , and most interestingly , the eviction of Rad21 was much more rapid and generalized . Thus residual Rad21 binding at 72 h was only detectable at the two loci centered at approximately nucleotides 124 , 000 and 136 , 000 , whereas the loss of CTCF binding was more gradual , and fully evident only by 5 days , in addition to being more site-specific . At 72 h , the input KSHV DNA copy number was only increased by 2 . 5 fold when measured by qPCR or estimated by viral read number in the ChIP-seq input samples , whereas Rad21 binding was absent at most sites . These data suggest that not only does Rad21 not bind to newly replicated genomes but that it is removed from pre-existing latent genomes . To determine whether the changes in CTCF and Rad21 occupancy of the KSHV genome during lytic replication were associated with overall changes in the cellular levels of these proteins during KSHV reactivation , we performed immunoblotting of cell lysates harvested at serial time points when ChIP-Seq was performed . There was no detectable difference in the overall levels of either protein during the time period during which ChIP-seq was performed ( Figure 5B ) . The stimulatory effects of CTCF and Rad21 knockdown on KSHV production suggested that both proteins exert restrictive effects on KSHV lytic replication . Rad21 depletion led to significantly greater increases in KSHV yield , suggesting that Rad21 and CTCF might have unique effects on the transcription of KSHV lytic genes . In order to perform a comprehensive analysis and comparison of the effects of CTCF and Rad21 on the KSHV transcriptional profile , we performed high-throughput deep sequencing of mRNA from KSHV-infected cells in which either CTCF or Rad21 was depleted prior to induction of lytic replication . KSHV-infected iSLK cells were transfected with either control siRNA , CTCF siRNA or Rad21 siRNA as was done in the experiments to examine the effect on virion production . 48 hours after siRNA transfection , cells were treated with doxycycline to induce KSHV lytic replication , and cells were harvested at 24 and 48 hours after induction of replication . RNA was isolated , oligo-dT selected , and processed for deep sequencing . The effects of both CTCF and Rad21 knockdown on lytic cycle transcription were compared to each other and to the transcriptional profile of induced cells transfected with control siRNA . A comparison of the transcriptional profiles over time from each sample ( control , CTCF-depleted and Rad21-depleted ) is presented in Figure 6A , with the read counts normalized against the read counts in the induced control siRNA sample . The first somewhat surprising finding is that transcription of most KSHV genes actually decreases at 24 hours in the CTCF and Rad21 depleted cells compared to control . This is particularly evident in the CTCF-KD case , but is reduced overall by either CTCF-KD or Rad21-KD , suggesting that CTCF and Rad21 initially act as positive factors in lytic gene expression ( Figure 6A and Figure S3 ) . However , by 48 hours , lytic transcription of most genes is increased compared to control when CTCF is depleted . This biphasic effect on KSHV transcription was also evident upon Rad21 KD , with levels of the majority of lytic transcripts being increased by 48 hours . The ultimate enhancing effect of Rad21 on lytic gene transcription was even more pronounced than that of CTCF depletion , demonstrating that the two proteins have similar but distinct effects on lytic gene transcription . In order to allow a more precise comparison , these data were analyzed by comparing read counts for each KSHV gene and the results are presented as binary comparisons between control versus CTCF KD and control versus Rad21 KD in Figures 6B and 6C , respectively . The net effect of CTCF on KSHV lytic gene expression is clearly repressive , as there was increased accumulation of virtually all lytic cycle gene transcripts by 48 hours when CTCF was depleted ( Figure 6B ) . The effect of Rad21 depletion on the transcriptional profile at 48 hours was very similar to that of CTCF , with an increase in expression of most lytic cycle genes ( Figure 6C ) . Consistent with its effect on virus production , the enhancement of gene expression due to Rad21 KD was significantly greater than the effect of CTCF KD for most genes . Whereas most mRNA levels were increased approximately 2–3 fold by CTCF KD , the increase was in the 4–8 fold range when Rad21 was knocked down ( Figure 6B and Figure 6C ) . It should be noted that these increases in lytic mRNA levels due to CTCF KD and Rad21KD are superimposed on those observed as a consequence of induced lytic replication in NC SiRNA cells - which were several orders of magnitude ( 16-fold to 1000-fold ) greater than in uninduced cells ( Figure S4 ) . When the effect of CTCF or Rad21 on early versus late lytic genes was compared , there was no significant difference overall based on the known temporal class of gene expression . The mean fold-change in early gene transcript levels was 3 . 3+/−2 . 1 S . D versus 4 . 5+/−1 . 2 S . D . for late genes . Comparison of CTCF KD and Rad21 KD also demonstrates that these differences in the magnitude of the Rad21 versus CTCF effects were not uniform across the genome , i . e . there were specific individual differences in mRNA abundance due to CTCF KD versus Rad21 KD . This is most clearly evident in two such regions highlighted by black bars in Figure 6A . These regions , which include the vIRF genes and ORF65 , ORF66 , ORF67 and ORF67A , demonstrate decreased expression with CTCF KD and increased expression with Rad21 KD . A third group of genes was also readily evident in the comparison of transcriptomes generated from CTCF-depleted and Rad21-depleted cells . This group consisted of genes whose abundance decreased with CTCF and Rad21 KD at 24 h and remained depressed compared to control at 48 h , suggesting that unlike the majority of genes , they are particularly dependent on CTCF and Rad21 for efficient expression . This group of genes in two clusters ( denoted by blue bars , Figure 6A ) includes K2 , K4 , K5 , K6 , K7 , ORF68 and ORF69 ( Figure 6B and Figure 6C ) . The magnitude of the overall changes in transcription of individual lytic genes due to CTCF or Rad21 knockdown were reproducible but relatively modest ( 2–8 fold over control ) compared with the increases observed in virion production under the same conditions . At least 18 million reads were measured for each sample in the RNA-Seq analyses , which should allow accurate quantification of mRNA levels for all KSHV transcripts , which are abundantly expressed during lytic replication [37] . We therefore performed qPCR for selected mRNA targets to validate and confirm the RNASeq data . ORF57 is representative of the vast majority of genes whose expression was similarly upregulated by both CTCF and Rad21 KD ( Figure 7A ) . The increase in ORF57 expression measured by qPCR upon CTCF or Rad21 KD was approximately 3 . 6 fold over control , which correlates well with the increases measured by RNASeq ( 4 fold ) . A second group of genes that were differentially regulated by CTCF and Rad21 is represented by vIRF1 and ORF47 . Expression of both genes was not significantly changed by CTCF KD but was upregulated 5-fold by Rad21 KD ( Figure 7B and 7C ) . The third group of genes , those whose expression was reduced by both CTCF KD and Rad21 KD , and are thus dependent on CTCF and Rad21 for expression , is represented by K2 and K5 ( Figure 7D and 7E ) . Expression of both genes was confirmed to be reduced to 20% of control by both CTCF and Rad21 KD . Although induction of lytic KSHV replication in this system was dependent on expression of RTA/ORF50 from a transgene under the control of a tetracycline-responsive promoter , it was still possible that significant amounts of RTA/ORF50 protein were produced from endogenous KSHV transcripts . In order to determine whether ORF50 levels were altered by CTCF or Rad21 KD , and thereby responsible for some of the observed transcriptional changes , we directly measured ORF50 protein levels at the same time points at which RNA-Seq was performed . Immunoblotting of protein lysates from cells at 24 hrs and 48 hrs after induction of lytic replication revealed no significant increases in ORF50 protein levels when either CTCF or Rad21 depletion was carried out prior to induction ( Figure 8A ) . In fact , a slight decrease in ORF50 protein was observed at 48 h in all cases . In order to address the possibility that CTCF or Rad21 might inhibit ORF50 function per se , we conducted the following experiment in which we asked whether CTCF or Rad21 KD affects the ability of ORF50 to activate RTA-responsive promoters in KSHV-negative cells . We performed luciferase assays with cells transfected with an RTA-responsive reporter plasmid and an RTA expression plasmid after either CTCF , Rad21 or control KD . The results shown in Figure 8C , demonstrate that CTCF and Rad21 do not inhibit RTA function in the reporter assay . Rather CTCF or Rad21 depletion actually resulted in slightly decreased RTA activation function . These results together demonstrate that the global effects of CTCF and Rad21 on KSHV lytic gene expression are not likely to be mediated via effects on RTA expression or function . In this study we report several novel aspects of the role of CTCF and cohesin as regulators of KSHV virus production . First , both CTCF and Rad21 act as host restriction factors for lytic KSHV replication as depletion of either protein resulted in markedly increased production of infectious virions . Rad21 appears to exert a greater effect , as Rad21 knockdown resulted in nearly 100-fold increases in virus yield , approximately five times more than the increase caused by CTCF knockdown . We also demonstrate that both CTCF and Rad21 dissociate from viral genomes during the process of lytic KSHV replication . Rad21 binding is lost earlier and more completely than CTCF after lytic KSHV replication begins . The almost complete loss of Rad21 from the majority of KSHV genome sites indicates eviction from latent episomes early during lytic replication as well as a lack of binding to newly replicated genomes . Conversely , the persistence of Rad21 at the major latency region and the terminal repeats indicates that Rad21 not only remains bound to template genomes but that it binds to nascently replicated genomes at these two sites . CTCF also exhibited site-specific changes in KSHV genome occupancy during lytic replication . CTCF occupancy was decreased by 3 days , and by 5 days , the relative occupancy at most sites was reduced by over 50% , indicating that CTCF binding also does not occur to newly replicated genomes at these locations . The finding that CTCF depletion results in increased virus production is in contrast to those of Chen et . al . who did not observe any effects of CTCF knockdown on KSHV lytic transcription in PEL cells [24] . These differences may be due to the different cell lines employed , and to the fact that knockdown in the our experiments was essentially complete , with no detectable CTCF remaining at the time of lytic induction . Our findings that cohesin and CTCF may play distinct roles in regulating KSHV reactivation and virion production are mirrored by the differing effects of their knockdown on KSHV lytic gene transcription . Consistent with the more profound effects of Rad21 KD on virion production , Rad 21 depletion consistently led to greater increases in KSHV lytic gene expression than did CTCF KD . Further , depletion of the two proteins had distinguishable effects on the lytic transcriptional profile . Whereas Rad21 KD led to increases in several vIRF gene transcript levels , compared to control , CTCF KD led to decreases or no change in this subset of mRNAs . A similar pattern was observed in several other specific genes , highlighting the complexity of overlapping gene regulation by CTCF and cohesin . Another novel finding in our study is the kinetic profile of the effects of CTCF and Rad21 on lytic gene transcription . Upon induction of KSHV replication , lytic gene transcription increased several orders of magnitude at 24 hours , as expected , and increased further at 48 hours . When CTCF or Rad21 were depleted , the increases in lytic gene transcription were significantly depressed at 24 hours compared to control . This relative decrease in lytic transcription was reversed by 48 hours , when CTCF KD , and particularly Rad21 KD , resulted in greater accumulation of lytic transcripts than in the presence of either protein . These data suggest that at baseline , CTCF and cohesin act as stimulators of transcription of many KSHV lytic genes but that their net effect , exerted subsequently , is negative , resulting in overall restriction of transcription and virus production . What is the likely mechanism of cohesin and CTCF gene activation , followed by inhibition ? It has been suggested that cohesin binding to the promoter region of ORF50/RTA and secondary interactions with cohesin bound at the latency promoter have a repressive effect on ORF50 expression , which is required for lytic reactivation , thus acting as a proximal inhibitor of lytic transcription [24] . Our data suggest that the effects of cohesin on KSHV lytic gene transcription are more complex and global . First , lytic replication was initiated by expression of RTA in trans , essentially removing RTA as a limiting factor for lytic transcription . In addition , total levels of RTA protein were not affected by CTCF or Rad21 depletion . Finally , depletion of cohesin and CTCF actually resulted in less accumulation of KSHV transcripts at early times . These data are consistent with cohesin and CTCF initially acting as general stimulators of KSHV lytic transcription , similar to cohesin's effect on host cell genes [23] , [38] . Cohesin appears to stimulate transcription from promoters of genes to which it is bound by facilitating the transition from paused RNA polII to elongating polII [23] , [39] . In addition , cohesin increases polII occupancy at genes to which it binds , most likely by increasing enhancer-promoter contact via looping . Importantly , however , depletion of cohesin also decreases transcription at most genes which do not bind cohesin and do not contain paused promoters , likely due to cohesin effects on basal and specific transcription factors [23] , [40] . It is this latter mechanism which is most likely responsible for the globally decreased KSHV transcription seen at earlier times after cohesin depletion in our studies . The mechanisms by which CTCF and cohesin regulate herpesvirus transcription are likely to be significantly different from those operative on the human genome as herpesvirus lytic genes are virtually all unspliced and in close proximity to each other . Thus facilitation of enhancer-promoter interaction may be less important in regulation of herpesvirus transcription by cohesin . Combined with the relatively limited number of high density cohesin sites on the KSHV genome , the positive effects of cohesin on early lytic gene transcription are likely due to the indirect effects of cohesin on cellular transcription factors referred to above . What is the likely basis of the subsequent enhancing effects of cohesin depletion on lytic gene expression and RNA accumulation ? A possible mechanism is suggested by the requirement for DNA replication in cis for efficient transcription of late lytic herpesvirus promoters . The positive effects of DNA replication in cis on transcription may derive from topological changes facilitating access to transcription factors as well as relocalization of genomes to intranuclear replication compartments [41]–[43] . It is possible that the physical linkages between cohesin molecules at various sites on circular latent genomes constrain the molecule , limiting maximal transcription . The more robust effect of Rad21 depletion on transcription , and especially virus production , suggest that the linking effects of cohesin may be more important in this regard than CTCF . It also implies that cohesin binding , although coincident with CTCF , may not be completely abrogated by removal of CTCF . The distinct and separable nature of cohesin and CTCF functions is underscored by the subtle but clear differences in the KSHV transcriptional profile exerted by their individual depletion . An additional insight into the potential role of cohesin in regulating KSHV transcription is provided by an examination of the few genes whose transcript levels are depressed by cohesin depletion and remain suppressed at later times . These include several of the K transcripts , suggesting that they are particularly dependent on cohesin for their efficient expression . It is likely relevant that several of these same genes were previously identified as unique among KSHV genes in containing paused RNA polII at their promoters [44] . It has recently been demonstrated that cohesin is particularly important for transcription of eukaryotic promoters that contain paused RNA polII . Thus cohesin may play the same role at these particular KSHV promoters as it does at a subset of cellular promoters that contain paused RNA polII , facilitating transition to elongation [23] . Whether such pausing also occurs at the other KSHV genes whose expression is adversely affected by cohesin depletion ( e . g . ORF 68 , ORF69 ) or if there are other promoter properties that determine cohesin dependence is an interesting avenue for further study . In summary , CTCF and cohesin play distinct roles in regulating KSHV reactivation from latency at the level of mRNA transcription . Cohesin and CTCF appear to initially act as positive factors , facilitating transcription for the majority of KSHV lytic genes , but subsequently their presence limits transcription and virus production , potentially by topological effects on transcription . In contrast to its role in host cell gene regulation , cohesin may primarily play an inhibitory role in transcriptional control of the KSHV lytic cycle . With regards to its baseline stimulatory effects on transcription in KSHV , cohesin effects may primarily derive from global effects on transcription factors such as myc , as has been previously demonstrated with cellular promoters [23] , [40] . It is less likely that cohesin stimulates transcription by facilitating long-range enhancer recruitment to specific promoters as observed in cellular eukaryotic systems [22] , [23] . In addition , cohesin and CTCF appear to be required for activity of certain KSHV promoters that are particularly cohesin and CTCF-dependent , and these effects are possibly due to effects on paused RNA polII . During the process of KSHV virion production , cohesin , and to a lesser degree , CTCF , dissociate from latent KSHV genomes , implying a dynamic role for both in replication control . The importance of both proteins as host restriction factors regulating KSHV reactivation is demonstrated by the dramatic increases in virus yield that result from their depletion .
Kaposi's sarcoma-associated herpesvirus ( KSHV ) is a human virus that causes Kaposi's sarcoma and lymphoma . KSHV establishes a lifelong infection in B lymphocytes , and persists in a latent form as circular DNA molecules . Reactivation and replication yield infectious virions , allowing transmission and maintenance of latent infection . The cellular mechanisms controlling reactivation remain incompletely characterized . Host proteins that regulate RNA transcription play an important role in controlling viral reactivation . In this study , we used high-throughput techniques to analyze the binding of two cellular proteins , CTCF and Rad21 , to the KSHV genome as the virus reactivated to produce infectious virions . We found that these proteins dissociate from the latent genome when reactivation occurs . We also found that depleting cells of these proteins increases virus production as much as a hundredfold . Depleting the cell of CTCF or Rad21 caused complex changes in the synthesis of RNAs by KSHV , with the amounts of most KSHV RNAs increasing greatly . We also showed that Rad21 and CTCF are needed for the virus to synthesize RNAs efficiently . Our study provides new insights into how the cell uses CTCF and Rad21 to limit KSHV's ability to synthesize RNA and reactivate from latency to produce infectious virus .
You are an expert at summarizing long articles. Proceed to summarize the following text: Analyses investigating low frequency variants have the potential for explaining additional genetic heritability of many complex human traits . However , the natural frequencies of rare variation between human populations strongly confound genetic analyses . We have applied a novel collapsing method to identify biological features with low frequency variant burden differences in thirteen populations sequenced by the 1000 Genomes Project . Our flexible collapsing tool utilizes expert biological knowledge from multiple publicly available database sources to direct feature selection . Variants were collapsed according to genetically driven features , such as evolutionary conserved regions , regulatory regions genes , and pathways . We have conducted an extensive comparison of low frequency variant burden differences ( MAF<0 . 03 ) between populations from 1000 Genomes Project Phase I data . We found that on average 26 . 87% of gene bins , 35 . 47% of intergenic bins , 42 . 85% of pathway bins , 14 . 86% of ORegAnno regulatory bins , and 5 . 97% of evolutionary conserved regions show statistically significant differences in low frequency variant burden across populations from the 1000 Genomes Project . The proportion of bins with significant differences in low frequency burden depends on the ancestral similarity of the two populations compared and types of features tested . Even closely related populations had notable differences in low frequency burden , but fewer differences than populations from different continents . Furthermore , conserved or functionally relevant regions had fewer significant differences in low frequency burden than regions under less evolutionary constraint . This degree of low frequency variant differentiation across diverse populations and feature elements highlights the critical importance of considering population stratification in the new era of DNA sequencing and low frequency variant genomic analyses . In the field of human genetics research , there has been increasing interest in the role of low frequency variation in complex human disease ( defined in this text as variants with a minor allele frequency between 0 . 5%–3% ) . This is in many ways a response to changing technology , but more importantly a response to the inability to completely explain heritability in common complex diseases and recognition of the true multifactorial mechanisms of genetic inheritance [1] . Since low frequency variants are likely essential in understanding the etiology of common , complex traits , it is critical to elucidate the genetic architecture and population substructure of low frequency variants for future work in this field . Factors such as rapid population growth and weak purifying selection have allowed ancestral populations to accumulate an excess of low frequency variants across the genome . This affects genomic analyses in two ways: proportion of deleterious versus neutral variation expected in low frequency variants and population stratification . It has been suggested that slightly deleterious single nucleotide variants ( SNVs ) subjected to weak purifying selection are major players in common disease susceptibility [2] , [3] . For example , Nelson et al . found that in 202 drug target genes , 2/3 of the low frequency variants were nonsynonymous mutations . This is a much higher ratio than found for common variants , and reflect the expected proportion given random mutation and degenerate coding . This ratio also suggests low frequency variants are only weakly filtered by selection [2] , [4] . In addition , low frequency variants represent a considerable proportion of the genome due to recent explosive population growth [3] . Gorlov estimates up to 60% of SNVs in the genome are variants with an allele frequency <5% [5] . Since the allele frequency distribution is skewed towards more low frequency variants , a higher number of low frequency deleterious variants are expected . Subsequently , low frequency variants appear to be enriched for functional variation , including protein coding changes and altered function [6] . Further , low frequency variants exhibit extreme population stratification . Demonstrating the magnitude of low frequency population stratification between two populations , Tennessen et al . identified more than 500 , 000 SNVs using 15 , 585 protein-coding genes from 2 , 440 individuals . Of these SNVs , 86% had a MAF<0 . 5% and 82% were population specific between European Americans and African Americans [3] . Low frequency allele sharing between populations on the same continent can be between 70% and 80% . In contrast , low frequency allele sharing between populations on different continents can be lower than 30% and variants are often unique to a single population . This extreme geographic stratification can lead to higher false positives and difficulty in replicating associations across genetic studies when not considered as part of the experimental design for low frequency SNV analyses [6] . To study the “landscape” of low frequency variant stratification across populations , we grouped low frequency variants across pertinent genome-wide biological features in a series of pairwise population comparisons across multiple ancestries . We define the boundaries of grouping by features , which consist of genomic regions ( one or many ) that belong to a genomic category , for example , a gene or a set of genes in a pathway . Methods that aggregate variants have been shown to be much more powerful than single-variant association testing for low frequency variants [7]–[10] , and thus are reliable to detect population stratification . Our collapsing method , BioBin , provides the opportunity to cast a broader net and uncover stratification across meaningful elements such as genes , pathways , and evolutionary conserved regions by aggregating low frequency variants based on expert biological knowledge . Herein we have applied BioBin to individuals from 1000 Genomes Project Phase I data; we defined “cases” and “controls” randomly between exhaustive pairwise population comparisons . Our goal was to identify features across the genome with differences in low frequency burden between populations; specifically , to look for aggregate differences in low frequency variation between populations , not to detect individual population-specific variants . We show that BioBin is effective in identifying differences in low frequency variant burden centered on biological criteria and highlights the considerable differences in low frequency variants across ancestry groups . These results further emphasize the critical importance of considering low frequency population substructure in future rare and low frequency variant analyses . We applied BioBin to whole-genome population data using the 1000 Genomes Project Phase I data . The populations , sample sizes , and total number of loci , variants , low frequency variants , and private variants are listed in Table 1 . Although the Iberian population ( IBS ) is listed in Table 1 , this population was not used in the analyses presented in this paper . There was not a sufficient sample size to meet our low frequency criteria ( N = 14 , MAF cutoff = 0 . 03 ) . In addition to the differences in overall magnitude of variation seen in Table 1 between these population groups , there were also differences in the distribution of this variation . In Figure 1 , we present an allele frequency density distribution plot of all autosomal chromosomes for all 13 populations . African descent populations have the highest density of low frequency variation . Others have found a similar trend genome-wide [11] . In general , the African ancestral populations not only have more variants overall than other ancestral groups ( see Table 1 ) , these populations also have a higher distribution of low frequency variants than other ancestral groups ( see Figure 1 ) . Although low coverage next generation sequence data is prone to errors , we found no evidence that sequence technology led to differential bias in a way that could explain the trends found in this paper ( Text S1 , Table S1 , Figures S1 , S2 ) . We investigated sample-relatedness with respect to common and low frequency variants using both identity-by-descent ( IBD ) and identity-by-state ( IBS ) estimations , and in each analysis , we found evidence of increased relatedness in ASW ( African ancestry , USA ) , CHB ( Han Chinese Beijing , China ) , CHS ( Han Chinese Shanghai , China ) , CLM ( Medellin , Columbia ) , GBR ( England and Scotland ) , JPT ( Japan ) , LWK ( Luhya , Kenya ) , and MXL ( Mexican Ancestry , California ) . We performed iterative IBD calculations in plink to eliminate related individuals from continental groups . Seventy-five individuals of 1080 total individuals were parsimoniously removed to achieve a pi_hat< = 0 . 3 in each continental population . The remaining 1 , 005 individuals were used for the binning analyses presented in this paper . An alternate allele sharing method described by Abecasis et al . uses IBS rather than IBD to review allele sharing [12] , [13] . In the case of low frequency or rare variants , IBS approximates IBD . Figure 2 shows within population IBS for all 13 populations for variants with a MAF<3% , where each point represents a pairwise IBS calculation within the same population ( i . e . YRI-YRI but not YRI-CEU ) . In Figure 2A , the pairs with average IBS calculations that fall outside of the cluster are cryptically related individuals with increased allele sharing . Figure 2B shows the IBS calculations after removing 75 individuals with cryptic relatedness . Complete details of these and additional sample-relatedness analyses are available in Text S2 , Figure S3 , Figure S4 , and Figure S5 . Knowledge of population substructure in low frequency variants is critical for genomic studies . We applied BioBin to test for low frequency ( MAF≤0 . 03 ) variant burden differences between 13 populations from the 1000 Genomes Project across different genomic features: genes ( intronic and exonic variants , filtered nonsynonymous and predicted damaging variants ) , intergenic regions , ORegAnno annotated regulatory regions , pathways , pathway-exons , evolutionary conserved regions , and regions considered to be under natural selection . Results are shown in Figure 3 , Figure 4 , Figure 5 , Figure 6 , Figure S6 , and Figure S7 . In each matrix plot , we have indicated the proportion of significant bins ( after Bonferroni correction ) out of the total number of bins generated between two populations . The color intensity represents the proportion of total bins that were significant [0 , 1] . Overall , there are large differences across populations with regard to low frequency variant burden and the distribution of low frequency variants is not random across the genome . The magnitude of stratification corresponds to the mutational landscape of the region . We chose NCBI Entrez to provide the boundaries for gene regions and created a custom role file of intron and exon boundaries using data provided from UCSC Genome Browser [14] . In Figure 3 , the top matrix corresponds to bins created using gene-exon boundaries , the middle matrix corresponds to bins created using gene-intron boundaries , and the bottom matrix corresponds to bins created using regions between genes ( intergenic ) . The values and color intensity within each block represent the proportion of significant bins after Bonferroni correction out of the total number of bins generated between two populations . The coding regions show a trend of a lower proportion of significant bins with low frequency variant burden differences than either the intron or intergenic bins . For example , in the CEU ( Northern/Western European Ancestry , USA ) −YRI ( Yoruba African ) comparison , approximately 44% of the gene-exon bins had significant differences in low frequency variant burden . In contrast , the noncoding region bins , gene-introns and intergenic bins had 66% and 70% of bins with significant differences in low frequency variant burden . The coding regions appear to be under more constraint across populations than noncoding regions . Comparing only the noncoding regions , introns tend to have slightly fewer variation differences than intergenic bins , most likely because introns are by default nearest neighbors to the selective pressures on coding regions . We filtered the gene-exon bins using annotations from the Variant Effect Predictor Software ( VEP ) [15] . We created gene bins with only nonsynonymous variants and a second analysis using only predicted damaging variants annotated by SIFT or PolyPhen2 [15]–[17] . The results in Figure 4 indicate that these potentially functional and significant changes are even more conserved between populations than coding regions ( Figure 3A ) . We used ORegAnno ( Open Regulatory Annotation database ) to define regulatory region boundaries for the bin analysis . The top matrix of Figure 5 shows the 78 population comparisons for the ORegAnno regulatory feature analysis . In comparison to Figure 3 , the annotated regulatory regions have fewer significant bins . For example , in gene-exon analysis shown in Figure 3 , approximately 44% of the ASW-CHB gene-exon bins contained significant differences in low frequency burden . However , in Figure 5 , only 28% of the ASW-CHB annotated regulatory bins contained significant differences in low frequency burden . This trend is consistent across the matrix of population comparisons; regulatory regions have fewer significant bins than the coding or noncoding features of the same population comparison . Several biological pathway and group sources from LOKI ( the Library of Knowledge Integration , which is described in detail in the methods ) were used to generate low frequency variant bins; including , PFAM , KEGG , NetPath , PharmGKB , MINT , GO , dbSNP , Entrez , and Reactome . The Figure 5B shows the 78 population comparisons for the pathway group feature analysis . Of all of the feature analyses , pathway bins consistently show the highest proportion of significant differences in low frequency variant burden between populations . There are several potential explanations . First , since pathway bins are generally much larger than the other feature types , it is possible that large bins increase the false positive rate . Second , the same genes and regions can recur in multiple pathways . If the region has significant differences in low frequency variant burden , then each pathway or group containing that region will have a higher chance of having significant differences in low frequency variant burden . Following this logic , a pathway containing many genes has a higher chance of having at least one gene with extreme low frequency variant stratification . To compare only coding regions within a pathway , we filtered the pathway analysis to include only variants within exons . The proportions are reduced ( shown in Figure S6 ) but still higher than the gene-exon proportions shown in Figure 3A . PhastCons output downloaded from UCSC Genome Browser was used to derive evolutionary conserved feature boundaries for primates , mammals , and more than 40 species of vertebrates . Figure S7 shows the 78 population comparisons for the ECR feature analysis . Of all of the feature analyses , ECR bins had the smallest proportion of significant bins . More ancestrally similar populations tended to have negligible low frequency burden differences in these conserved segments . For example , approximately 7% of the ECR region bins ( vertebrate alignment ) were significantly different between FIN ( Finnish ) and JPT ( Japanese ) individuals . However , the significant number of bins between the two ancestrally similar GBR ( British ) and CEU individuals was less than 1% . To investigate regions of natural selection , we created a feature list using regions recently identified/confirmed by Grossman et al . with the Composite Multiple Signals algorithm on the 1000 Genomes Project data [18] . In addition , a publication by Barreiro et al . provided a list of specific genes with the strongest signatures of positive selection; i . e . genes that contained at least one nonsynonymous or 5′ UTR mutation with an FST value greater than 0 . 65 [19] . After lifting positions to build 37 , there were only 368 regions from the regions identified by Grossman et al . The results are shown in Figure 6 . The top plot corresponds to regions identified in African ancestry , the middle plot corresponds to regions identified in populations of Asian ancestry , and the bottom plot corresponds to regions identified specifically in populations of European ancestry . The trends in these three matrix plots are distinctly different from the trends shown in Figures 3–5 . The blocks of comparisons within a continental group ( shown in black boxes on each matrix plot ) still have very little color , which means that the low frequency variant burden between populations within a continental group is very similar . The main difference is the gain of intensity outside of the continental groups . For example , in Figure 6B ( regions identified in Asian populations ) , the European continental group and Spanish continental group mostly have proportions over 60% when compared to populations of Asian descent . In the same plot , the populations in the African group have proportions over 85% when compared to populations in the Asian group . In general , we found regions considered to be under natural selection unlikely to have significant differences in low frequency burden between ancestrally similar populations , and very likely to have significant differences in regions considered to be under natural selection between ancestrally distant populations ( see Figure 6 ) . Additional analyses were performed using regions identified in other publications and can be found in Text S3 , Table S2 , and Table S3 . Although low frequency variants are commonly assumed as independent ( in low linkage disequilibrium ( LD ) with other variants ) , there are rare haplotypes within related individuals and populations [20] . In Figure 7 , three pairwise population comparisons are shown . We investigated the top 10 ranked bins from the CEU-CHB ( A ) , CHB-YRI ( B ) , and CEU-YRI ( C ) coding and noncoding analyses for presence of LD ( r2>0 . 3 ) between two variants in the same bin . Figure 7 shows bins predominately filled with white-space indicating low to no pairwise LD between variants in those bins . In the top ten bins from these three analyses , rare haplotypes do not appear to be driving the significant differences seen in low frequency variant burden . Since the proportion of significant bins in the feature analyses is considerably higher for pathway bins than any other feature , we wanted to investigate the correlation between pathway p-value and bin size . We chose to assess the correlation between significance and several characteristics of the pathways using the pathway feature CEU-YRI population comparison . Figure S8 and Figure S9 show the correlations between six untransformed and transformed variables ( with outliers removed ) , where each pairwise correlation is significant ( p-value<0 . 05 ) . A bin was considered an outlier if the number of loci in the bin was more than 2 . 5 standard deviations from the mean transformed loci value . The most interesting correlations were the nonlinear correlations between the loci/variants/genomic coverage and p-values . Figure S9B is a higher magnification of the highlighted correlation in Figure S9A , specifically; we plotted the correlation between −log10 p-values and log10 variants . The lowess smoothing function is shown in red , and the function appears to change slope twice . From x = 1 to x = 3 , the slope is increasing with increasing number of variants . From x = 3 to x = 4 , the slope is near 0 . From x = 4 to x = 5 , the slope is increasing with increasing numbers of variants . When the log10-transformed value of the number of variants is less than 3 or greater than 4 , there appears to be a positive correlation between the number of variants in a bin and increasing significance of that bin . However , the data is not uniformly distributed and is sparse in those same areas . Therefore , the trends in the tails are most likely unreliable . We created boxplots describing certain characteristics from each data source . Figure S10 shows that specific sources ( i . e . KEGG ) consistently have larger bin characteristics ( number of loci , number of genes , coverage ( kb ) , etc . ) and also have much more significant bin p-values ( Figure S10B ) . It appears that certain sources might inherently have more significant groups by nature of the information that source provides , or because of the size of groups found in the source . From the matrix plots shown above , there is undoubtedly a functional component that influences the evolution of low frequency variants . However , from the correlations in the pathway analyses , it is also clear that larger bins can contain more stratification and thus more likely to have significance differences in low frequency burden . In more traditional case/control analyses , large bins are less likely to be significant because increasing binsize generally means more noise to mitigate the signal . However , in this study , when diverse populations are compared , larger bin sizes have more opportunity to capture population stratification . Figure S11 shows the relative number of loci across tested features and varying interregion parameters . Boxplots in Figure S11A represent each feature tested in the population comparison . The small inset figure shows the magnitude of difference between the numbers of loci in pathways ( peak ) versus other feature types . The main plot in Figure S11A shows the same information , but is limited to 2000 loci . In general , ECRs/exonic regions/nonsynonymous gene variants/ORegAnno annotated regions/predicted deleterious gene variants/UTRs are very small bins . Pathway bins have a broader distribution , but in general are much larger . For comparison , we varied the size of intergenic regions ( only noncoding regions ) between 10 kb and 200 kb , those results are shown in Figure S11B . We also split the entire genome ( including coding and noncoding regions ) by various windows between 10 kb–200 kb . Figure S11C represents a genome “average , ” and both Figure S11B and S11C can be used as comparison for feature tests . Figure S11B and S11C show increasing bin size as windows increase , the proportion of significant bins increase as window size increases as well ( see Figure S12 and S13 ) . For example , Figure S13 shows matrix plots from whole-genome “average” analyses ( A–G correspond to 10 kb , 25 kb , 50 kb , 75 kb , 100 kb , 150 kb , and 200 kb respectively ) . According to Figure S11A and S11C , exon bins from the original feature analysis are roughly comparable in size to 10 kb bins from the whole-genome “average” analysis . In the gene analysis results , approximately 43 . 9% of bins are significant after Bonferroni correction between CEU-YRI . Comparatively , the genome average between CEU-YRI for 10 kb bins is 57 . 64% . This supports the idea that coding regions are presumably more functional and perhaps more conserved than other regions in the genome of comparative size . According to Figure S11A and S11C , pathway bins from the original feature analysis are roughly comparable in size to 150 kb bins from the whole-genome “average” analysis . In the pathway analysis results , approximately 81 . 28% of bins are significant after Bonferroni correction between CEU-YRI . Comparatively , the genome average between CEU-YRI for 150 kb bins is 86 . 09% . The gap between pathway bins and “average” genome stratification given similar size is much smaller for pathways than it is for exons . This particular pathway analysis includes introns ( which typically have more variation than coding regions and larger bins are expected to collect more stratification . However , there are still fewer significant bins than expected on average . Since the reference genome is predominantly of European ancestry [21]–[23] , populations with non-European ancestry generally have more variation with respect to the reference genome than those of European ancestry ( see Table 1 ) . Therefore , to interpret the results of this study , one might conclude that non-European populations have higher rates of sequencing error than European descent populations . However , in the most recent 1000 Genomes Project publication , the authors report an accuracy of individual genotype calls at heterozygous sites more than 99% for common SNVs and 95% for SNVs at a frequency of 0 . 5% . Furthermore , the authors found that variation in genotype accuracy was most related to sequencing depth and technical issues than population-level characteristics [11] . Therefore , neither the sequencing error nor the predominantly European reference genome adequately explains the trends seen in the genomic feature exploration ( see Text S1 , Table S1 , Figures S1 , S2 ) . Both sequence generation ( technology and/or site ) and population identity strongly contributes to underlying stratification in next-generation sequence data . After removing individuals with cryptic relatedness , 4 out of 13 Phase I populations were sequenced entirely using a single sequence technology ( CHB , CHS , JPT , and TSI ) . The other 9 populations had between 3–18 individuals or ∼5%–57% of the population sequenced on technologies other than Illumina ( ABI SOLID or LS454 ) . Note: all three of the Asian populations ( after removing individuals with cryptic relatedness ) were sequenced only with Illumina technologies . In our IBD analysis using variants with a minor allele frequency of 5% or greater and linkage disequilibrium r2< = 0 . 2 , we identified and eliminated 75 individuals of various population backgrounds . In addition to the previously documented relatedness in 1000 Genomes Project [http://www . 1000genomes . org/phase1-analysis-results-directory] , we also found additional cryptic relatedness seen in other work [24] , [25] . The differences are likely because we used continental groups ( not a single population or the entire 1080 individuals ) to identify cryptically related individuals and in our analysis that could include variants with fixed opposite frequencies and are overall common . This is infrequent in populations of the same continental group , but could be stratification introduced by different sequencing technologies . The major goal of this study was to investigate population stratification across multiple biological features . We created matrix plots to illustrate the proportion of significant bins in each comparison ( shown in Figure 3 , Figure 4 , Figure 5 , Figure 6 , Figure S6 , and Figure S7 ) . Our results show an interesting trend between functional regions of the genome and variant tolerance . Mutations appear to be less tolerated in functional regions . Similarly , ECRs , which are known to be conserved among species , are also the features least likely to have variation burden differences between two populations . There is some debate about selection and functional significance in these conserved regions , it is unknown what factors have the largest effect on mutation rates [26] , but it is possible that consistently low mutation rates in these features have generated conserved regions throughout evolution [27] . There are two potential explanations: 1 ) additional level of repair of DNA damage in transcriptional active regions by transcription coupled repair ( TCR ) , 2 ) approximately 3% of the genome is subject to negative selection , however it is estimated that functionally dense regions contain up to 20% of the sites under selection [26] , [27] . A number of the top results in each comparison have an interesting context , particularly in light of natural selection . Perhaps one of the most notable is SLC24A5 ( Ensembl ID:ENSG00000188467 ) , which is one of the top ten results in 19 out of 78 populations comparisons in the gene feature analysis . European specific selective sweeps estimated in the last 20 , 000 years suggest that SLC24A5 is key in skin pigmentation and Zebrafish with “golden” mutations exhibit melanosomal changes [28]–[30] . The presence of selection in particular populations due to environmental factors such as distance to the equator has led to the evolution and expansion of low frequency variants in some populations but not others . A second notable top result is DARC ( Ensembl ID:ENSG00000213088 ) , which encodes the Duffy antigen . The DARC gene bin was in the top ten results in 14 out of 78 population comparisons in the gene feature analysis . It has long been known that populations of African descent have increased diversity due to natural selection at this location , which prevents Plasmodium vivax infection . The top result from the regulatory region analysis was a region on chromosome 20 ( chr20:45395536–45396346 ) which was in the top ten bins in 24 out of 78 populations comparisons in the ORegAnno feature analysis . This region also overlaps ENCODE transcription factor binding sites in multiple cell lines , including: CTCF , POLR2A , NFYA , E2F1 , FOS , and more . It was also annotated as an insulator in multiple cell lines in ENCODE Chromatin State Segmentation analyses using Hidden Markov Models [14] , [31] . One last example , chr15 . 968 , contains variants in the genome location chr15:48400199–48412256 . This bin is one of the top ten bins in 17 out of 78 population comparisons in the intergenic analysis . The region covered by the chr15 . 968 bin is less than 1 kb upstream of SCL24A5 on chromosome 15 and overlaps with several transcription factor-binding sites ( including CTCF ) , regions thought to be weak enhancers , and regions thought to be insulators . According to Grossman et al . , there are defined regions under natural selection before and after this region ( chr15:45145764–45258860 and chr15:48539026–48633153 ) , and all are very likely to participate in the transcriptional regulation of SLC24A5 [18] . The natural selection features require knowledge of three things for interpretation: 1 ) population A , 2 ) population B , and 3 ) the population where this signature was identified . When all three of these are within the same ancestral or continental group , we expect very few differences in low frequency burden . However , if population A is the same or similar to the population possessing the selection signature and population B is different , we expect significant differences in low frequency burden between population A and population B . In our results , we found that the vast majority of regions considered to be under natural selection had significant differences in low frequency burden between disparate ancestral populations , which support the theory of selection in these regions . In general , size of bins can influence the number of stratified variants contained and thus the significance of that bin . It is important to prove that this is because larger bins have a greater opportunity to “collect” variants that are stratified and not because of inflated type I error . We have tested type I error rates in bins between approximately 40 variants to over 100 , 000 variants , which covers all analyses presented in this paper , and found no correlation between bin size and Type I error rate ( unpublished data ) . However , it should also be noted that while larger bins have more chances to collect stratified variants , there is also a larger capacity to collect neutral variants that contribute noise and decrease the signal . Using CEU-YRI pathway burden analysis , we reviewed the correlation between pathway size and significance . The number of genes in pathways ranged from 1 to over 700 genes , with the average around 5 genes per group . Correlations for this data are shown in Figure S9 . Not surprisingly , there were very linear and positive correlations between number of loci , number of variants , and genomic coverage . However , each of these had a nonlinear and somewhat complex relationship with the log-transformed p-value . This is highlighted in Figure S9B , which shows the relationship between the −log10 transformed p-value and the log10 transformed number of variants in the bin . The trend indicates that p-values are positively correlated ( become more significant ) with numbers of variants in a bin when the numbers of variants are relatively small or very large . Two reasons could explain this correlation: 1 ) the false-positive rate is influenced by bin size ( number of variants per bin ) , and 2 ) true signals from gene bins with burden differences perpetuate higher numbers of significant pathway bins . After extensive simulation testing ( unpublished data ) and recent publications in the literature , we believe the later is true [32] , [33] . A single or small number of child bins ( gene bins in this example ) , can drive parent bins ( pathways in this example ) to be significant even if no other child bin contains stratification . The comparison in Figure S10 between group sources available in LOKI suggests KEGG , NetPATH , PharmGKB , and Reactome have consistently larger bins ( higher number of loci , variants , and coverage ) . On average , these same four sources also tend to have bins with smaller p-values . Therefore , larger pathways are more likely to contain a gene with extreme low frequency variant stratification . Population stratification is incredibly important in genomic analyses , particularly when low frequency variants are being studied . Expected stratification and potential bias is related to bin size and functional significance of region studied . Regions with more selective pressure often have fewer differences between populations than one would expect by chance . However , it is also important to consider the size of the region since population stratification tends to become more of a problem in large bins . The x-axis of each matrix plot ( i . e . Figure 3 ) are oriented with African continental populations on the far right and the continental group with the highest proportion of significantly different low frequency variant bins on the far left . According to these matrix plots , Asian populations have more bins that are significantly different when compared to African populations than European/African population comparisons . Popular evolutionary theories suggest that the population that left Africa split before travelling East and West . One would expect low frequency burden differences ( compared to African populations ) to be very similar . However , populations from the Asian continental group tend to have more low frequency burden differences with African groups than European descent populations differences with African groups . There are at least three possible explanations; first , the Asian populations were the only continental group to be sequenced on the same technology , which could introduce a different bias when testing any of these populations with populations outside of Asian ancestry . While this is true of the 1 , 005 unrelated individuals , there were cryptically related individuals sequenced using SOLID technologies in all three of the Asian populations . The only population ( including cryptically related individuals ) to be sequenced exclusively on Illumina was TSI . When we examined the Asian populations and included the cryptically related individuals ( and thus individuals sequenced with different technologies ) , the trend was the same . Asian populations are the most different from African populations with regard to low frequency variant burden . The second potential explanation is that Asian populations had considerable proportions of cryptic relatedness that had to be removed for our analysis , 49 of the 75 individuals removed were from Asian populations . Perhaps there was something unique about how those samples were collected . The third and most interesting explanation is a speculation that involves the journey for early Asian populations after leaving Africa . Travelling east was much different geographically than travelling west . For example , early Asian migrants would have traversed the Himalayan Mountains . The harsh travel could have induced bottlenecks and other evolutionary mechanisms that would uniquely change the genetic architecture , specifically the architecture of low frequency variation . The course of travel for European descent populations was very different; they were exposed to unique challenges and climates . As each continental group diverged from Africa , their separate paths could explain why the difference in burden exists ( EAS/AFR and EUR/AFR ) . As we continue in pursuit of genetic etiology explaining heritability in common , complex disease , it is important to consider multiple types of genomic data , specifically variation beyond common variants . Low frequency variants are more frequent in the genome than common variants and are likely to have significant functional impact on human health . However , as we look forward to many successes in next-generation data analysis , it has become increasingly clear that we can't apply the same methods and corrections to low-frequency variants as we did in GWAS . Since low frequency variants are often recent mutations , they are specific to continental ancestry groups . This provides two important conclusions . First , potentially functional low-frequency variants are likely not the same across distantly related individuals . Second , low frequency population substructure leads to substantial differentiation and cannot be ignored [11] . Until relatively recently , we have not focused on the challenges presented by low frequency population stratification . Current methods used for GWAS to correct for ancestry are not likely adequate for low frequency stratification [34] , [35] . Therefore , it is imperative that researchers are aware of potential pitfalls stratification can introduce to low frequency genomic analyses . In summary , we were able to expose the magnitude of low frequency population stratification between all populations available in 1000 Genomes Project Phase I release across multiple interesting biological features . The magnitude of low frequency stratification appeared to be dependent on the functional location of the variation and the genomic size of the pertinent features . For example , there were fewer differences in low frequency burden in coding regions than intergenic regions . We found features with less variant tolerance and possibly more evolutionary constraint to have fewer differences in low frequency variant burden between different populations , i . e . significant low frequency bins seemed to be consistent with mutation theory . In addition , larger features were more likely to contain stratified variants and thus be significantly different between two populations . Low levels of stratification existed even between populations of the same continental group . The results of this study serve as a warning to researchers whom wish to use population control groups such as 1000 Genomes Project or shared control sets , unmatched case and control groups can contribute significantly to type I error rates . Future studies should focus on methods to accurately control for low frequency population stratification . BioBin is a standalone command line application written in C++ that uses a prebuilt LOKI database described below ( software paper in preparation ) . Source distributions are available for Mac and Linux operating systems and require minimal prerequisites to compile . Included in the distribution are tools that allow the user to create and update the LOKI database by downloading information directly from source websites . The computational requirements for BioBin are quite modest; for example , during testing , a whole-genome analysis including 185 individuals took just over two hours using a single core on a cluster ( Intel Xeon X5675 3 . 06 GHz processor ) . However , because the vast amount of data included in the analysis must be stored in memory , the requirements for memory usage can be high; the aforementioned whole-genome analysis required approximately 13 GB of memory to complete . Even with large datasets , BioBin can be run quickly without access to specialized computer hardware or a computing cluster . The number of low frequency variants is the primary driver of memory usage [36] . BioBin is open-source and publicly available on the Ritchie lab website ( http://ritchielab . psu . edu/ritchielab/software/ ) . Harnessing prior biological knowledge is a powerful way to inform collapsing feature boundaries . BioBin relies on the Library of Knowledge Integration ( LOKI ) for database integration and boundary definitions . LOKI contains resources such as: the National Center for Biotechnology ( NCBI ) dbSNP and gene Entrez database information ( downloaded dbSNP b137: Dec 21 2012 , Entrez: Feb 1 2013 ) [37] , Kyoto Encyclopedia of Genes and Genomes ( KEGG , downloaded Dec 21 2012 , Release 64 ) [38] , Reactome ( downloaded Dec 12 2012 ) [39] , Gene Ontology ( GO , downloaded Feb 1 2013 ) [40] , Protein families database ( Pfam , downloaded Dec 1 2011 ) [41] , NetPath - signal transduction pathways ( downloaded Sept 3 2011 ) [42] , Molecular INTeraction database ( MINT , downloaded Oct 29 2012 ) [43] , Biological General Repository for Interaction Datasets ( BioGrid , downloaded Feb 1 2013 , version 3 . 2 . 97 ) [44] , Pharmacogenomics Knowledge Base ( PharmGKB , downloaded Jan 6 2013 ) [45] , Open Regulatory Annotation Database ( ORegAnno , downloaded Jan 10 2011 ) [46] , and evolutionary conserved regions from UCSC Genome Browser ( downloaded Nov 10 2009 ) [14] . LOKI provides a standardized interface and terminology to disparate sources each containing individual means of representing data . The three main concepts used in LOKI are positions , regions and groups . The term position refers to single nucleotide polymorphisms ( SNPs ) , single nucleotide variants ( SNVs ) or low frequency variants . The definition of region has a broader scope . Any genomic segment with a start and stop position can be defined as a region , including genes , copy number variants ( CNVs ) , insertions and deletions , and evolutionary conserved regions ( ECRs ) . Sources are databases ( such as those listed above ) that contain groups of interconnected information , thus organizing the data in a standardized manner . LOKI is implemented in SQLite , a relational database management system , which does not require a dedicated database server . The user must download and run installer scripts ( python ) and allow for 10–12 GB of data to be downloaded directly from the various sources . The updater script will automatically process and combine this information into a single database file ( ∼6 . 7 GB range ) . A system running LOKI should have at least 50 GB of disk storage available . The script to build LOKI is open source and publicly available on the Ritchie lab website ( http://ritchielab . psu . edu/ritchielab/software/ ) . Users can customize their LOKI database by including or excluding sources , including additional sources , and updating source information as frequently as they like [36] . We chose NCBI dbSNP and NCBI Entrez Gene as our primary sources of position and regional information due the quality and reliability of the data , clearly defined database schema , and because they contain gene IDs that map to the majority of group sources in LOKI . Gene boundary definitions were derived from NCBI Entrez . Pathway/group bins , regulatory regions , and evolutionary conserved regions were created using sources available in LOKI ( sources detailed in Software section ) . Some sources explicitly provide lists of genes in pathways; others provide groups of genes , which share a biological connection ( i . e . protein-protein interactions ) . For the purposes of this study , any bin created by multiple regions/genes will be analyzed in the “Pathway-Groups” feature analysis . External custom input files were generated using boundaries of annotated exon regions from UCSC to bin exon and intron specific variants . For example , if Gene A has three exons and two introns , only two bins would be created: GeneA-exons and GeneA-introns . GeneA-exons would contain all variants that fell within any of the three Gene A exon boundaries . External custom feature files were also generated for regions under natural selection by combining regions provided by previously published work [18] , [19] . Example binning strategies can be seen in Figure 8 . Using hierarchical biological relationships and optional functional or role information , BioBin can create many combinations of variants to bin . Custom feature files and additional binning details are explained in Text S4 , Table S4 , and Table S5 . BioBin is a bioinformatics tool used to create new feature sets that can then be analyzed in subsequent statistical analyses . Statistical tests used with BioBin can be chosen according to the hypothesis being tested , the question of interest , or the type of data being tested [36] . Unless otherwise noted , the results presented herein were calculated using a Wilcoxon 2-sample rank sum test implemented and graphed in the R statistical package [47] , [48] . P-values presented have been corrected using a standard Bonferroni correction , adjusting for the number of bins created and tested in a given analysis . Simulations confirming the power and validity of using the Wilcoxon 2-sample rank sum test are described in Text S5 and Table S6 . To investigate population stratification using BioBin , we analyzed the 1000 Genomes Project Phase I data . The 1000 Genomes Project was started in 2008 with the mission to provide deep characterization of variation in the human genome . As of October 2011 , the sequencing project included whole-genome sequence data for 1080 individuals , and aimed to sequence 2 , 500 individuals by its completion [49] . We removed 75 cryptically related individuals and conducted a pairwise comparison of low frequency variant burden differences between all 13 ancestry groups included in the phase I release of the 1000 Genomes Project ( October 2011 release ) . Table 1 provides the total number of variants ( common and low frequency ) and individuals included in Phase I VCF files of 1000 Genomes Project data for 1080 individuals in all 13 populations . In any genetic study , and especially in consideration of low frequency variants , it is important to evaluate sample relatedness . We combined populations by continental ancestry ( i . e . AFR continental group includes ASW , LWK , YRI ) and evaluated sample relatedness between and within the general ancestry groups using identity-by-state ( IBS ) and identity-by-descent ( IBD ) . Pairwise IBS represents the number of shared alleles at a specific locus between two individuals . IBS can be observed as 0 , 1 , or 2 depending on how many alleles are in common between the pair . If the shared alleles are inherited from a recent common ancestor , they are also considered IBD . Pairwise IBS calculations for low-frequency variants approximate IBD since the variants are likely to be recent and the chance of being identical because of recurrence is rare [50] . We used plink and plink-seq to estimate pairwise IBS and IBD for individuals of the same general ancestry group ( http://atgu . mgh . harvard . edu/plinkseq/ , http://pngu . mgh . harvard . edu/~purcell/plink/ ) [51] . For common variants , we created an independent subset of SNVs with a minor allele frequency greater than 5% and r2 values less than 0 . 2 to calculate pairwise IBD between individuals . For example , for the populations of African descent ( LWK , ASW , and YRI ) we grouped all of the individuals from these three populations and calculated the IBD . We removed maximally connected or related individuals in a parsimonious and iterative manner and repeated the IBD analysis until the maximum pairwise pi_hat score was less than or equal to 0 . 3 . After repeating this analysis in each continental group , 75 individuals were dropped from BioBin analyses based on our threshold for cryptic relatedness . We also evaluated allele sharing within and between major ancestral groups using plink-seq to calculate IBS for low frequency variants and common variants ( threshold 0 . 03 MAF and 0 . 25 MAF , respectively ) . Even though we estimated IBD in common variants ( described above ) , we calculated the IBS in low frequency and common variants separately to ensure the results were consistent . Using the ratio of shared alleles divided by the total number of genotyped alleles between two individuals , we evaluated excess sharing of low frequency variants ( MAF<0 . 03 ) compared to excess sharing of common variants ( MAF>0 . 25 ) . Feature selection in BioBin is a clear innovation over other available collapsing methods . Knowledge of biological features , such as genes and pathways , are available through LOKI for binning . In this analysis , we used the feature options of BioBin to investigate a variety of biologically relevant bins for differences in low frequency variant burden across 13 populations . We implemented a minimum bin size of two variants , inter-region bin size of 50 kb , and set the MAF binning threshold to 0 . 03 . We chose a 3% MAF binning threshold to focus our analysis on rare and near rare variation that differs between population groups . Additional details concerning binning parameters can be found in the Text S4 . We binned genes ( introns , exons , nonsynonymous variants , and predicted deleterious variants ) , intergenic regions , pathways , pathway-exons , regulatory regions , evolutionary conserved regions , and regions thought to be under natural selection . Natural selection can alter genomic variation in features , particularly in regions with some impact on protein function ( regulatory regions , coding regions ) . Positive selection on a specific variant allows the advantageous variant to sweep through a population , which can lead to an excess of common variants . Alternatively , weak negative selection or purifying selection can result in selective removal of deleterious alleles . This can decrease variation around the locus under selection and lead to an excess of rare or low frequency variation [52] . Commonly , evidence of natural selection is found only in one ancestral group , which is consistent with the idea that these selection events postdate population separation [53] . Because of this differentiation among populations , we were interested in using regions identified as being under selective pressures as features in a BioBin analysis . Table 2 shows the analysis plan , features tested , sources used , and the mean number of bins generated across all pairwise comparisons . After evaluating the population comparisons for the features described in Table 2 , we investigated the linkage disequilibrium ( LD ) in 10 top-ranked bins for three population comparisons , CEU-CHB , CHB-YRI , CEU-YRI . We calculated the LD between binned variants and determined the number of variants inside of a bin in LD with an r2> = 0 . 3 . We also evaluated the correlation between pathway significance and bin size . We took all of the pathways in the YRI/CEU analysis and compiled the following information for each pathway bin; total genomic coverage , number of genes , number of independent genes , number of loci , number of variants , and BioBin p-value . Because the majority of pathways or groups are not very large , the data was heavily skewed ( see Figure S8 ) . We performed a log10 transformation on all six variables: number of genes in the pathway or group , number of unique genes ( not present in any other pathway or group ) , number of loci in the pathway bin , number of variants in the pathway bin , genomic coverage of the pathway bin , and the BioBin reported Bonferroni adjusted p-value . Because of the skewness , we removed any pathway bins that had transformed loci values outside of 2 . 5 standard deviations of the log-transformed loci mean .
Low frequency variants are likely to play an important role in uncovering complex trait heritability; however , they are often continent or population specific . This specificity complicates genetic analyses investigating low frequency variants for two reasons: low frequency variant signals in an association test are often difficult to generalize beyond a single population or continental group , and there is an increase in false positive results in association analyses due to underlying population stratification . In order to reveal the magnitude of low frequency population stratification , we performed pairwise population comparisons using the 1000 Genomes Project Phase I data to investigate differences in low frequency variant burden across multiple biological features . We found that low frequency variant confounding is much more prevalent than one might expect , even within continental groups . The proportion of significant differences in low frequency variant burden was also dependent on the region of interest; for example , annotated regulatory regions showed fewer low frequency burden differences between populations than intergenic regions . Knowledge of population structure and the genomic landscape in a region of interest are important factors in determining the extent of confounding due to population stratification in a low frequency genomic analysis .
You are an expert at summarizing long articles. Proceed to summarize the following text: Vertebrate development requires communication among cells of the embryo in order to define the body axis , and the Wnt-signaling network plays a key role in axis formation as well as in a vast array of other cellular processes . One arm of the Wnt-signaling network , the non-canonical Wnt pathway , mediates intracellular calcium release via activation of heterotrimeric G proteins . Regulator of G protein Signaling ( RGS ) proteins can accelerate inactivation of G proteins by acting as G protein GTPase-activating proteins ( GAPs ) , however , the possible role of RGS proteins in non-canonical Wnt signaling and development is not known . Here , we identify rgs3 as having an overlapping expression pattern with wnt5b in zebrafish and reveal that individual knockdown of either rgs3 or wnt5b gene function produces similar somite patterning defects . Additionally , we describe endogenous calcium release dynamics in developing zebrafish somites and determine that both rgs3 and wnt5b function are required for appropriate frequency and amplitude of calcium release activity . Using rescue of gene knockdown and in vivo calcium imaging assays , we demonstrate that the activity of Rgs3 requires its ability to interact with Gα subunits and function as a G protein GAP . Thus , Rgs3 function is necessary for appropriate frequency and amplitude of calcium release during somitogenesis and is downstream of Wnt5 activity . These results provide the first evidence for an essential developmental role of RGS proteins in modulating the duration of non-canonical Wnt signaling . The Wnt signaling network is classified into β-catenin-dependent and β-catenin-independent pathways [1]–[3] . β-catenin-dependent Wnt signaling acts through the stabilization of β-catenin and subsequent transcriptional activation of β-catenin targets [4] , whereas β-catenin-independent Wnt signaling influences cell polarity ( known as Planar Cell Polarity or PCP in Drosophila ) . β-catenin-independent Wnt signaling has also been shown to lead to calcium ( Ca2+ ) release as well as activation of Rac , Rho and other cytoskeletal components in vertebrates [5] , [6] . In zebrafish , Wnt-5 and -11 function in Wnt/Ca2+ signaling [7] , [8] . Wnt11 is enriched in the anterior and mutants show anterior extension and eye fusion defects , while Wnt5b is enriched in the posterior and mutants show altered cell movements during gastrulation , often resulting in convergence extension and somite defects [9]–[11] . Zebrafish embryos demonstrate Ca2+ release dynamics during epiboly , gastrulation , convergent extension and organogenesis [12]–[21] . Two distinct types of Ca2+ release events , aperiodic transient fluxes found mainly in the enveloping layer and dorsal forerunner cells [17] , [18] , [22] , [23] and sustained increases in Ca2+ levels in the deep cell layer and yolk syncytial layer [24] , [25] , have been described . We have shown that early Ca2+ transients are , in part , modulated by Wnt5 [15] , [26] . The zebrafish wnt5b genetic mutant ( pipetail ) shows reduced Ca2+ release [24] and the ventralized maternal effect mutant hecate shows ectopic Ca2+ release [18] . Moreover , inhibition of Ca2+ release results in alteration of dorsal ventral patterning , cell movement and left-right patterning [17] , [26] . These observations suggest that the kinetics of Ca2+ release , both transient and sustained , translate into distinct developmental outputs [16] . Wnts interact with receptors of the Frizzled ( Fz ) family [27] and due to structural similarities to G protein coupled receptors ( GPCR ) , Fz receptors are hypothesized to stimulate heterotrimeric G protein activation [28]–[30] . We have shown previously that Wnt proteins work though specific Fz homologues to activate G proteins and to modulate Ca2+ release in zebrafish embryos [15] , [22] , [26] , [31] . Moreover , in Drosophila , Wnt target genes have been shown to be upregulated when Gαo is over-expressed and constitutively active Gαo is sufficient to restore Wnt signaling in the absence of Fz activity [32] . In addition , epistasis experiments support that G proteins function downstream of Fz and upstream of Disheveled ( Dvl ) [32] . G protein signaling is regulated by the lifetime of active Gα and βγ subunits . Activated Gα subunits have an intrinsic GTPase activity that converts the GTP-bound conformation to the Gα-GDP bound conformation allowing reassembly with Gβγ subunits to form the inactive Gαβγ heterotrimer [33] . Regulator of G protein Signaling ( RGS ) proteins have been shown to influence the duration of G protein signaling [34]–[37] . RGS proteins share a conserved RGS domain of 130 amino acids that binds to activated Gα subunits and accelerates their rates of GTP hydrolysis by up to 1000-fold [38]–[40] . By functioning as GTPase-activating proteins ( GAPs ) for G proteins , RGS proteins are uniquely situated to modulate the intensity and duration of Wnt signaling . However , no studies have ascertained the possible importance of RGS proteins in non-canonical Wnt signaling or whether changes in frequency and or amplitude of signaling result in biological defects . To investigate potential roles of RGS proteins in vertebrate development , we utilize gene knockdown in zebrafish . We focus on rgs3 , which was identified in an expression screen in zebrafish [41] . We find that rgs3 is expressed in overlapping and adjacent domains with wnt5b at multiple stages of zebrafish development . Morpholino knockdown of rgs3 in zebrafish embryos causes convergence and extension ( CE ) defects that resemble phenotypes observed in the wnt5b genetic mutant , pipetail [42] . To this end , we have identified a genetic interaction between rgs3 and wnt5b . Additionally , we describe endogenous Ca2+ release dynamics during somite stages and show that Rgs3 and Wnt5b impact the frequency of Ca2+ release . Moreover , we show that Rgs3 modulates the extent and duration of Wnt5b induced Ca2+ activity . Functional analyses show that both the rescue of the rgs3 knockdown defect and impact on Wnt5-induced Ca2+ release requires a key asparagine in the RGS domain of Rgs3 necessary for Gα binding and acceleration of its GTPase activity . This research identifies a link between Wnt5b signaling and Rgs3 activity relative to the frequency of Ca2+ release , thus revealing obligatory roles for RGS proteins in vertebrate development in the context of the whole animal . Our results also demonstrate that the biological outcome of Wnt signaling depends greatly upon regulating the duration of its signal , as shown here with Rgs3 . Zebrafish rgs3 was identified in an expression screen during early somitogenesis stages [41] and is poised to interact with the Wnt signaling network . Utilizing Reverse Transcriptase Polymerase Chain Reaction ( RT-PCR ) , we determined that rgs3 expression begins during the blastula period shortly after zygotic transcription initiates ( 2 . 5–5 hours post fertilization , hpf ) , and persists through the segmentation period ( 10–24hpf ) ( Figure 1A ) . Whole Mount In Situ Hybridization ( WMISH ) demonstrated ubiquitous rgs3 expression during epiboly and gastrulation stages . During somite stages ( 10–20 hpf ) , rgs3 expression resolves in the somites , tailbud , and brain ( Figure 1B–1G ) , with discrete rgs3 expression in the midbrain/hindbrain boundary as demonstrated by overlap with the molecular marker engrailed 1 ( eng1 ) at 28 hpf ( Figure S1F ) , and enriched rgs3 expression in the posterior ( caudal ) portion of developing somites ( Figure 1D ) . rgs3 and wnt5b show both overlapping and adjacent expression domains in the somites and in the posterior tailbud ( Figure 1E–1G and Figure S1A , S1B , S1C , S1D ) . rgs3 expression is enriched around the Kupffer's vesicle ( Figure S1C ) , a ciliated organ in the zebrafish embryo that has been shown to influence left-right patterning , yet rgs3 does not appear to be required for organ laterality ( data not shown ) . As Wnt5b is a secreted ligand , the proximity of rgs3 to wnt5b producing cells suggests that Rgs3 may function in modulating Wnt5b signaling . In zebrafish , wnt5b induces increased Ca2+ release during the blastula stage in a G protein dependent manner [15] , [22] , [26] . To determine if rgs3 over-expression is sufficient to negatively regulate Wnt5b signaling ( Figure 2A ) , we tested the impact of rgs3 on wnt5b induced Ca2+ release . In vivo imaging in blastula stage embryos is accomplished with the Ca2+ sensor Fura-2 . Upon binding Ca2+ , Fura-2 exhibits an absorption shift that can be determined by collection at two wavelengths ( 340nm , Ca2+-saturated and 380nm , Ca2+-free ) . A ratio image is derived as the quotient of the 340-nm image divided by the 380-nm image on a pixel-by-pixel basis , and provides spatial and temporal measurement of Ca2+ dynamics . Ca2+ release activity was monitored over a 75 minute time course during the blastula stage . Sequential ratiometric images were analyzed by a subtractive algorithm to identify changes in Ca2+ release activity ( transients ) over time as well as the location of the activity as described previously [13] , [43]–[45] . Transients identified during the time course are presented as a composite image with location of Ca2+ release mapped on the embryo . The number of Ca2+ transients during the cellular blastoderm stage is represented by height of the peaks and color coded where purple is low and yellow/red is high number of events . The composite image of a wild-type ( wt ) embryo during the blastula stage indicates endogenous Ca2+ levels throughout the embryo ( Figure 2C ) compared to those observed during increased Ca2+ release in an embryo injected with wnt5b ( Figure 2B ) . Co-injection of rgs3 with dextran-conjugated Texas Red ( TxR ) lineage tracer into a subset of cells in embryos uniformly expressing wnt5b co-mixed with Fura-2 supports that rgs3 is sufficient to suppress wnt5b induced Ca2+ release as demonstrated by the reduction of Ca2+ levels ( Figure 2D ) in the rgs3/TxR positive region ( Figure 2F ) . We next investigated if Rgs3 suppression of wnt5b induced Ca2+ release requires GAP activity . A conserved asparagine within the RGS domain of RGS proteins is necessary for GAP activity for Gα subunits [46]–[48] . Substitution of this key asparagine ( N ) with Alanine ( A ) results in a loss of the GAP activity of RGS proteins towards Gα subunits in culture cells [46] , [48] . To elucidate the role of the GAP function of Rgs3 , we created an N to A mutation in zebrafish rgs3 ( rgs3N109A ) ( Figure 3A ) . We evaluated the impact of rgs3N109A expression on Wnt5b induced Ca2+ release . Unlike rgs3 , the rgs3N109A is unable to suppress wnt5b induced Ca2+ release ( Figure 2E ) as demonstrated by no change in the Ca2+ activity in the rgs3N109A /TxR positive region of embryos ( Figure 2G ) . To rule out the possibility that lack of suppression by Rgs3N109A was due to differences in its expression or localization compared to Rgs3 , we generated and expressed N-terminal myc-tagged rgs3 and rgs3N109A constructs in embryos . Western analysis reveals robust and comparable expression of Rgs3 and Rgs3N109A at the time of Ca2+ imaging as well as through 24hpf ( Figure 3B ) . Immunostaining for anti-myc in epiboly stage embryos also indicates that both proteins localize to the membrane and cytoplasm ( Data not shown ) . Together these data strongly indicate that rgs3 is sufficient to inhibit wnt5b-induced Ca2+ signaling and that this action requires the GAP activity of Rgs3 . Since Rgs3 is sufficient to modulate Wnt5 activity in an over-expression assay , we next evaluated the necessary role of rgs3 during development . To knockdown Rgs3 , we utilized antisense morpholino oligonucleotides ( MO ) [49] . Three separate MOs were designed to bind rgs3 5′UTR ( MO and MOb ) or splice junction ( SA ) ( Figure 3A ) . All MOs designed to knockdown Rgs3 produced similar defects . Control-injected embryos at 28 hpf are fully extended with a characteristic anterior-posterior ( A-P ) length ( Figure 3C ) . In contrast , rgs3 MO-injected embryos have shorter A-P extension and a kinked tail ( Figure 3D ) reminiscent of defects observed in the wnt5b ( pipetail ) genetic mutant [42] . Zebrafish somites develop sequentially anterior to posterior and form a distinct chevron shape [50] ( Figure 3E ) . rgs3 morphants display tighter packed and rounded somites ( Figure 3F ) . To evaluate anterior-posterior extension alterations at an earlier developmental stage ( 15 hpf ) , molecular markers were used . Control-injected embryos have a characteristic spacing of krox20 expression in the hindbrain rhombomeres 3 and 5 , as well as regular spaced blocks of myoD expression in the developing somites flanking the midline ( Figure 3G–3H and 3K–3L ) . In contrast , krox20 and myoD expression in rgs3 morphants reveal a failure of cells to converge on the midline resulting in a lateral expansion of the rhombomeres and somites ( Figure 3I and 3M ) . Additionally , rgs3 morphants fail to extend along the anterior-posterior ( A-P ) axis leading to closer spaced myoD ( Figure 3M , asterisks ) . The A-P extension defects were further confirmed with pax2 , a marker expressed in the anterior retina , midbrain/hindbrain , and otic vesicle of 18 hpf embryos ( Figure 3O ) . rgs3 morphants display compression of these regions along the A-P axis ( Figure 3P ) . Together these data strongly indicate that rgs3 is required for normal anterior-posterior axis extension . The specificity of the rgs3 knockdown as well as structural functional analyses were determined by RNA co-injection experiments . Injection of control 5bp mismatch MO resulted in negligible defects compared to rgs3 MO which induced morphological somite defects ( Figure 3R ) . Co-injection of rgs3 MO with rgs3 RNA suppressed the MO-induced defects evaluated by molecular markers krox20 ( Figure 3J ) , myoD ( Figure 3N , asterisks ) and pax2 ( Figure 3Q ) . Moreover , wild-type rgs3 RNA leads to significant suppression of MO-induced defects ( Figure 3R and Table S1 ) . In contrast , rgs3N109A mutant RNA does not suppress the MO-induced defect ( Figure 3R and Table S1 ) . These results demonstrate that Rgs3 GAP activity is required for its developmental functions . The functional requirement of rgs3 during anterior-posterior axis extension and the finding that over-expression of rgs3 is sufficient to inhibit wnt5b-induced Ca2+ signaling , raised the possibility that rgs3 may negatively modulate Ca2+ release dynamics during somitogenesis . In fact , Ca2+ signals along the trunk of zebrafish embryos during somitogenesis have been described using the bioluminescent Ca2+ reporter R-aequorin [12] , [51] , [52] . In order to compare changes in Ca2+ release dynamics upon rgs3 manipulation , we performed a detailed analysis of endogenous Ca2+ release in tissues that express both wnt5b and rgs3 . To this end , we utilized Fura-2 imaging to monitor Ca2+ activity with a focus on the developing somites and tailbud in either a dorsal ( Figure 4A ) or a lateral ( Figure S2A ) orientation . The pseudocolored ratio image provides a spatial and temporal measurement of Ca2+ dynamics with low Ca2+ represented by blue and high Ca2+ represented by yellow/red . Representative pseudocolored ratio images from a time-lapse series of Ca2+ measurements ( Video S1 ) , spanning the 3–13 somite stages are shown ( Figure 4B–4E ) . The notochord and forming somites can be identified in the grayscale fluorescence images ( Figure 4B′–4E′ ) . Overlay of grayscale and ratio images illustrate the regions of increased Ca2+ levels relative to morphology ( Figure 4B″–4E″ ) . Ca2+ release activity during somitogenesis is dynamic with sustained Ca2+ levels in the presomitic mesoderm , lateral to the somite forming region and flanking the midline/notochord ( Figure 4B″–4E″ ) . As somitogenesis proceeds , sustained Ca2+ levels appear distinctly between the somites ( Figure 4C″–4E″ , arrowheads ) . In addition , we observe localized short-lived increases in Ca2+ release ( referred to as transients ) . To demonstrate a transient , a region of interest ( ROI ) is noted by dashed circle ( Figure 5A–5C ) . In the ROI , an increase in Ca2+ is observed from time 0s to time 15s and the local increase subsides by time 30s . Since rgs3 may function to influence the frequency of Ca2+ release , we determined the number of transients as a function of developmental age ( Figure 5D ) . In wt embryos , we observe an average of 5 . 3 Ca2+ transients per hour ( n = 3 ) ( Figure 5E ) . A similar frequency is found when analyzing the data from a lateral view ( Figure S2B , S2C , S2D , and S2K ) . Having defined endogenous Ca2+ release dynamics during somitogenesis , we next determined the impact of rgs3 knockdown . From the development of somite 6 to somite 12 , rgs3 morphants have statistically more Ca2+ transients , with an average of 21 . 7 per hour ( n = 3 ) , when compared to wt embryos ( Figure 5D and 5E ) . rgs3 morphants have sustained Ca2+ levels in the lateral regions similar to wt . However the dynamics within the somite region frequently show initiating transients leading to responses in neighboring cells , resulting in larger areas of increased Ca2+ release ( Figure 5I–5K , Video S2 ) . These large and robust transients are not observed in wt embryos ( Figure 5F–5H , Video S1 ) or in morphant embryos co-injected with rgs3 RNA ( Video S3 ) . The same dramatic increase in both the frequency of release and amplitude is observed in lateral views as well ( Figure S2E , S2F , S2G , and S2K ) . The change in Ca2+ release dynamics in rgs3 morphants is consistent with a delayed shut-off of G protein signaling , i . e . normally mediated by the GAP activity of Rgs3 . These data indicate that during the segmentation period Rgs3 functions to limit the extent and duration of endogenous Ca2+ release activity . Previously , we reported reduced Ca2+ release in blastula stage Wnt5b ( pipetail ) genetic mutants [24] . When compared to wild-type embryos , wnt5b morphant embryos show a statistically reduced number of Ca2+ transients , averaging 1 . 3 per hour ( n = 2 ) during the segmentation period ( Figure 5D–5E , 5L , and 5M; Video S4 ) . A similar decrease in frequency is also observed in a lateral view ( Figure S2H , S2I , S2J , S2K ) . The size and duration of Ca2+ transients observed in wnt5b morphants are comparable to wt embryos ( Video S4 ) . In order to determine if the increased frequency of Ca2+ transients associated with rgs3 knockdown is dependent upon wnt5b signaling , we simultaneously knocked down wnt5b and rgs3 . Embryos co-injected with wnt5b MO and rgs3 MO and imaged during the segmentation period show a statistically reduced number of Ca2+ transients , 1 . 8 per hour ( n = 5 ) ( Figure 5D–5E ) . The reduced Ca2+ release in the double knockdown is not significantly different than wnt5b single knockdown , demonstrating that the rgs3 morphant phenotype is dependent upon Wnt signaling . Studies have shown that increased Wnt/Fz signaling leads to degradation of Dvl [53]–[55] . In addition Drosophila genetics places active G protein signaling upstream of Dvl [32] . Therefore , it seemed essential to determine whether Rgs3 plays a role in modulation of Dvl levels . In the absence of an antibody to evaluate Dvl levels , we generated a myc-tagged ( MT ) form of zebrafish Dvl2 that is readily detected by western blot after injection into embryos ( Figure 6A ) . We find that wnt5b co-expression reduced Dvl-MT levels ( Figure 6A ) . Reduction of Rgs3 function , via MO knockdown , also leads to decreased Dvl-MT levels . These data demonstrate that endogenous Rgs3 functions in the non-canonical Wnt pathway upstream of Dvl , thereby functioning to modulate the duration and robustness of Wnt5 signaling . To further explore interaction between Rgs3 and Wnt5b , we defined a low dose for wnt5b MO which results in a mild A-P extension phenotype and determined whether rgs3 enhances or suppresses the wnt5b gene knockdown defects . Phenotypes were evaluated by morphology ( Figure 6B , 6E , 6H , and 6K ) and molecular markers , krox20 and myoD ( Figure 6C–6D , 6F , 6G , 6I , 6J , 6L , and 6M ) . Compared to wt ( Figure 6B–6D ) , low dose wnt5b MO ( 2 ng ) results in a mild phenotype ( Figure 6E–6G ) . We next defined a sub-phenotypic dose for rgs3 MOsa ( 0 . 8 ng ) , which produced a phenotype ( Figure 6H–6J ) indistinguishable from wt ( Figure 6B–6D ) . Individual injection of low dose rgs3 MOsa or wnt5b MO did not induce any severe defects ( Figure 6N ) . However , wnt5b MO ( 2 ng ) combined with rgs3MOsa ( 0 . 8 ng ) resulted in a 92% penetrance of severe defects ( Figure 6K–6N ) . Our Ca2+ imaging implicated Rgs3 function in limiting the extent and duration of endogenous Ca2+ release activity and that this was dependent upon Wnt5 . However , in the presence of low level Wnt5 activity ( low-dose MO ) , partial knockdown of rgs3 could lead to discordant changes in the frequency and amplitude of Ca2+ release result in the dramatic phenotypic penetrance and severity . These results provide new evidence for an essential role of Rgs3 in modulating the duration of Wnt5b signaling . We show that Rgs3 is necessary for proper gastrulation and somite patterning during zebrafish development . These actions of Rgs3 require its ability to interact with and accelerate the rate of GTP hydrolysis by G proteins , as revealed by studies employing an Rgs3 mutant defective in these activities . We describe endogenous Ca2+ release dynamics during somitogenesis . The frequency of Ca2+ transients as well as the observation of sustained Ca2+activity in the trunk and tail region are consistent with previous reports of Ca2+ activity during zebrafish somitogenesis [12] , [51] , [52] , [56] . Of particular significance is the dramatic change in frequency of endogenous Ca2+ release upon rgs3 knockdown . RGS proteins were identified as negative regulators of G protein signaling in the mid 1990s [57] , [58] and the role of G proteins in Wnt/Ca2+ signaling was first demonstrated in 1997 [22] . Subsequent reports further implicated G proteins in canonical Wnt signaling [31] , [59] , [60] . Heterotrimeric G protein activation and inactivation are tightly regulated to provide precise control of the amplitude and duration of G protein signaling . Receptor-stimulated GTP binding activates G proteins , while their intrinsic GTPase activity functions to terminate signaling . RGS proteins by definition accelerate this GTPase activity . Over-expression studies in cell culture [61] and in Xenopus embryos [62] have indicated that specific RGS proteins are sufficient to regulate canonical Wnt signaling . Although G protein signaling is required for normal cell movement during zebrafish gastrulation [11] , the role of RGS proteins in noncanonical Wnt signaling has not been investigated . Our current study identifies a member of the RGS protein family that has a direct impact on frequency and amplitude of Wnt5b signaling . We find that Rgs3 activity is sufficient to modulate wnt5b induced Ca2+ release and this ability requires GAP activity consistent with the known role of G proteins in the activation of Wnt signal transduction pathways [5] , [63] , [64] . We report the key novel finding that knockdown of Rgs3 results in increased frequency and amplitude of Ca2+ release that this dramatic impact on Ca2+ dynamics in the somites is dependent upon Wnt5 supporting that Wnt/Ca2+ signaling activity is an in vivo target of RGS proteins . Moreover , rgs3 demonstrates a complex genetic interaction with wnt5b . rgs3 is expressed in and near wnt5b expressing tissues and we find that combined low doses of wnt5b MO and rgs3 MOsa result in a large penetrance of severe somite defects which is not observed during their individual knockdown . Our data suggest that both the frequency and amplitude of wnt5b signaling must be tightly regulated to result in correct cell movement and somite patterning . Wnt5b modulates both transient Ca2+ release activity in small populations of cells , as well as , sustained activity in a large region of cells [16] . While the transient release correlates with limiting β-catenin activity [17] , [26] , we hypothesize that the sustained activity correlates with polarized cell movement , for example in convergence-extension movements during gastrulation or neural and vascular outgrowth [16] . This concept is supported by vascular outgrowth defects in pipetail genetic mutants [65] as well as the observation of a reduction in sustained Ca2+ activity at the somite boundaries ( data not shown ) . It is of interest to determine if interactions between rgs3 and wnt5b influence directed vascular outgrowth . Modulation of G protein signaling ( impacting frequency as well as duration ) is likely to influence directed cell migration , vascular development as well as numerous other developmental processes [66]–[68] . Our findings clearly justify the need for further investigations into the role of RGS proteins in this process and other interactions between Rgs3 and Wnt signaling to provide new insights into their mechanistic role in directed cell movement and disease . Our loss of function analysis coupled with rescue and in vivo physiological analysis in whole embryos has provided compelling functional insight into the developmental role of RGS proteins in the Wnt signaling network . Adults were maintained in a 14-hour light / 10-hour dark cycle at 28°C . Embryos were collected from natural pairwise matings and staged by hours post fertilization ( hpf ) at 28 . 5°C and morphological criteria described in Kimmel et al . [50] , [69] . rgs3 ( clone IBD5096 ) was isolated in an expression screen in zebrafish [41] and generously provided by Dr . I . Dawid . MO-resistant rgs3 was generated by RT-PCR and directionally cloned ( 5′-3′ ) into the pCS2+ , pCS2+ myc or pCS2+ Flag expression vector . The Quick Change II site-directed mutagenesis kit ( Stratagene ) was used to generate an Asparagine ( N ) to Alanine ( A ) substitution at amino acid 109 which is located in the RGS domain of Rgs3 . Synthetic RNA was then in vitro transcribed using SP6 RNA polymerase and the mMessage mMachine system ( Ambion ) . Antisense morpholino oligonucleotides ( MO ) were designed to target the 5′-UTR/ATG ( rgs3 MO and rgs3 MOb ) to inhibit translation and an intron-exon junction in the RGS domain ( rgs3 MOsa ) to alter splicing . As a control rgs3 MOmm ( 5 bp mismatch in lowercase letters ) was designed ( Gene-Tools ) : rgs3 MO 5′-AGTCGGTTCTTCATGTCTTTGGCCC-3′ , rgs3 MOb 5′-TCTCCGAGAAATCCACCATAGTGTG-3′ , rgs3 MOsa 5′-CCAGTCCATCTGATGAGGGAGAGAG-3′ . rgs3 MOmm 5′-TCaCCcAGAAATCCtCCATtGTcTG-3′ . MOs ( 5–20ng ) were pressure-injected into one cell-stage embryos . For low-dose knockdown , 0 . 8ng rgs3 MOsa and/or 2 ng wnt5b MO [65] were injected into one cell zebrafish embryos . Control rgs3 MOmm did not produce any phenotype at 25 ng . For rescue , in vitro-transcribed MO-resistant rgs3 ( 500 pg ) was co-injected with 20 ng rgs3 MO . Injected embryos were characterized by morphological and molecular analysis . Embryos were fixed overnight in 4% paraformaldehyde and dechorionated . Single label WMISH was performed as previously described [24] , [70] , using digoxigenen ( Dig ) -labeled or dinitrophenyl ( DNP ) -labeled antisense and sense RNA probes . Detection was carried out using BM purple ( Roche Applied Science ) . Double label WMISH was performed as previously described [71] , using both Dig and DNP-labeled antisense probes . Purple color was developed with AP-conjugated anti-Dig and BM purple ( Roche Applied Science ) , and red color was developed with AP-conjugated anti-DNP and INT RED ( Roche Applied Science ) . Reactions were stopped in phosphate-buffered saline ( PBS ) . Embryos were mounted on bridged coverslips and photographed using a Zeiss Stemi M13 Stereoscope and an Axiocam digital camera . To compare levels of MT-Rgs3 and mutant MT-Rgs3 , embryos were injected with either myc-rgs3 or myc-rgs3 ( N109A ) ( 750 pg ) . To investigate Rgs3's impact on Dvl , C-terminal myc tagged zebrafish dvl2 ( 300 pg ) was injected alone , with rgs3 MOsa ( 5ng ) , with wnt5b ( 250pg ) , and with both rgs3 MOsa ( 5ng ) and wnt5b ( 250pg ) . Injected Embryos were allowed to develop to the appropriate stage ( 5 hpf and 24 hpf ) before incubating in lysis buffer ( 20 µM Tris , 100µM NaCl , 1µM EDTA , 5% Triton , . 5%SDS , 10% Leupeptin and 0 . 1µM PMSF ) at room temperature for 3 minutes . Embryos were then disrupted using a pestle , centrifuged at 13 , 000 rpm for 10 minutes at 4°C and the clear supernatant containing whole zebrafish protein was collected . Approximately 10µg of protein was loaded in each well and separated with SDS-PAGE gel electrophoresis . Proteins were transferred onto nitrocellulose membrane ( Li-Cor ) and incubated with the following primary antibodies: mouse anti-myc ( 1∶2 , 000; Cell Signaling Technology ) and rabbit anti-β actin ( 1∶2 , 000; Sigma ) , and then incubated with the following secondary antibodies: IRDye800 anti-mouse ( 1∶20 , 000; Li-Cor ) and IRDye680 anti-rabbit ( 1∶20 , 000; Li-Cor ) . The signal was visualized using the Odyssey Infrared Imaging System ( Li-Cor ) . Embryos injected with either myc-rgs3 or myc-rgs3 ( N109A ) ( 200 pg ) were fixed 1–3 hrs in 4% PFA/1× PBS at sphere/dome stage . Mouse anti–myc antibody ( 1∶1 , 500; Cell Signaling Technology ) , followed by goat-anti-mouse Alexa488 conjugated secondary antibody ( 1∶400; Molecular Probes ) was used to detect the rgs3 products . Nuclei were identified with 5 µM TO-PRO-3 ( Molecular Probes ) . Embryos were mounted in an animal pole orientation in bridged coverslips and optically sectioned using two-channel imaging on a scanning laser confocal microscope , Leica TCS SP2 . Wide-field fluorescence and bright–field images from a Zeiss Stemi M13 Bio Stereoscope were photographed using Axiovision ( Zeiss ) software and an Axiocam 5000 camera . Images were merged using Adobe Photoshop CS . The ratiometric Ca2+-sensing dye Fura-2 or Bis-Fura-2 ( Molecular Probes ) was injected into 1-cell zebrafish embryos . The excitation spectra are different between Ca2+ bound Fura-2 ( 340-nm ) and Ca2+ free ( 380-nm ) forms . By taking the ratio of the fluorescence intensity at these wavelengths an estimate of intracellular-free Ca2+ can be derived . To stimulate Wnt signaling , in vitro transcribed wnt5b RNA ( 400 pg ) was co-injected with Fura-2 at the one cell stage . rgs3 or rgs3N109A RNA ( 400 pg ) was unilaterally injected at the 16-cell stage mixed with dextran-conjugated Texas Red ( TxR ) lineage tracer . TxR distribution was determined by collecting a reference exposure at 540-nm excitation . For cellular blastoderm stage imaging , embryos were oriented in a lateral position in a glass-bottomed dish on a Zeiss axiovert epifluorescence microscope . Image pairs at 340 and 380-nm excitation wavelengths ( 510-nm emission ) were collected at 15-second intervals . Each imaging session collected 300 image pairs . The ratio image , a pixel by pixel match of both excitation wavelengths , is calculated by computer software ( RatioTool , Inovision ) . The sequence of ratio images was processed and the Ca2+ fluxes ( transients ) were determined by a subtractive analog patterned after [72] , [73] and described in [13] , [43] . The ratio image ( 340nm , Ca2+-saturated and 380nm , Ca2+-free ) imported for publication is encoded in 8 bits and converted to pseudocolor with low ratio ( low Ca2+ ) represented by blue and high ratio ( high Ca2+ ) represented by yellow/red . For somite imaging , 2–6 somite stage embryos were oriented in low melt agarose ( 0 . 4% ) in a dorsal or lateral orientation . Time courses collected images pairs until 12–15 somite stage at 15-second intervals ( Approximately 1000 images pairs ) . Image pairs were converted to ratio images as described above . Sequential ratio images were manually analyzed for changes in Ca2+ transients . Somite stage Ca2+ transients were defined as a localized increase in Ca2+ which persists no longer than thirty seconds . Calculations for MO rescue experiments were made using the Fisher's exact test and the two-tailed p-value was reported . Calculations for analysis of somite stage Ca2+ transients in morphant embryos were made using One-Way Analysis of Variance ( one-way ANOVA ) with Tukey HSD test p-values reported .
Vertebrate development requires communication among cells in order to define the body axis ( front/back , head/tail , or left/right ) . Secreted factors such as Wnts play key roles in a vast array of cellular processes , including patterning of the body axis . One arm of the Wnt-signaling network , the non-canonical pathway , mediates intracellular calcium release via activation of heterotrimeric G proteins . Regulator of G protein Signaling ( RGS ) proteins can accelerate inactivation of G proteins by acting as G protein GAPs and are uniquely situated to control the amplitude of a Wnt signal . Here , we combine cellular , molecular , and genetic analyses with high resolution calcium imaging to identify a role for RGS modulation of Wnt-mediated calcium release dynamics and developmental patterning events . We find that loss of rgs3 gene function produced body patterning defects like those observed with loss of wnt5b gene function . Analysis of endogenous calcium release dynamics in developing zebrafish revealed that both rgs3 and wnt5b are required for appropriate frequency and amplitude of calcium release . Our results provide new evidence that a member of the RGS protein family is essential for modulating the non-canonical Wnt network to assure normal tissue patterning during development .
You are an expert at summarizing long articles. Proceed to summarize the following text: Botulinum neurotoxins ( BoNTs ) include seven bacterial toxins ( BoNT/A-G ) that target presynaptic terminals and act as proteases cleaving proteins required for synaptic vesicle exocytosis . Here we identified synaptic vesicle protein SV2 as the protein receptor for BoNT/D . BoNT/D enters cultured hippocampal neurons via synaptic vesicle recycling and can bind SV2 in brain detergent extracts . BoNT/D failed to bind and enter neurons lacking SV2 , which can be rescued by expressing one of the three SV2 isoforms ( SV2A/B/C ) . Localization of SV2 on plasma membranes mediated BoNT/D binding in both neurons and HEK293 cells . Furthermore , chimeric receptors containing the binding sites for BoNT/A and E , two other BoNTs that use SV2 as receptors , failed to mediate the entry of BoNT/D suggesting that BoNT/D binds SV2 via a mechanism distinct from BoNT/A and E . Finally , we demonstrated that gangliosides are essential for the binding and entry of BoNT/D into neurons and for its toxicity in vivo , supporting a double-receptor model for this toxin . Botulinum neurotoxins ( BoNTs ) , one of the six Category A potential bioterrorism agents , are a family of bacterial toxins that cause the fatal disease botulism in humans and animals [1] , [2] , [3] . These toxins target and enter presynaptic nerve terminals by receptor-mediated endocytosis . Once inside neurons , they act as proteases to cleave host proteins essential for synaptic vesicle exocytosis . Blocking vesicle exocytosis abolishes the release of neurotransmitters from nerve terminals , thus paralyzing muscles , and may cause death due to respiratory failure [1] , [4] . The ability of BoNTs to block synaptic vesicle release also provides the basis for their medical applications: local injections of minute amounts of toxin can attenuate neuronal activity in the targeted region which can be beneficial in many medical conditions such as dystonia [5] , [6] , [7] , [8] . BoNTs are classified into seven serotypes ( BoNT/A-G ) based on their antigenic properties [1] , [9] . They share a similar overall domain structure composed of a heavy chain ( ∼100 kDa ) and a light chain ( ∼50 kDa ) connected via a disulfide bond [1] , [4] , [10] . The heavy chain contains two functional domains: the C-terminal receptor binding domain ( HCR , ∼50 kDa ) and the N-terminal domain ( HN ) that mediates the translocation of the toxin light chain ( LC ) across endosomal membranes . The LCs act as zinc-dependent proteases . The specificity of each toxin LC for host proteins has been well-established: BoNT/B , D , F , and G cleave synaptic vesicle protein synaptobrevin II ( Syb , also known as VAMP II ) [11] , [12] , [13] , [14] , [15] , [16] . BoNT/A , E , and C cleave peripheral membrane protein synaptosomal-associated protein of 25 kDa ( SNAP-25 ) [12] , [17] , [18] , [19] , [20] , [21] . In addition , BoNT/C can also cleave the plasma membrane protein syntaxin ( Syx ) [22] , [23] . These three proteins are known as soluble N-ethylmaleimide sensitive factor attachment protein receptors ( SNARE ) that form the basic machinery mediating the fusion of synaptic vesicle membranes to plasma membranes [24] , [25] , [26] , [27] , [28] . There are two key functional variations among BoNTs: the particular SNARE proteins that their LCs cleave , and the cellular receptors that they use to enter cells . Understanding these key determinants for each BoNT is critical for developing effective strategies to counteract their toxicity and for utilizing them for scientific and therapeutic applications . Thus , it has been a major focus to identify the receptors for each BoNT [29] , [30] . The first binding components identified for BoNTs are widely expressed complex forms of gangliosides ( polysialiogangliosides , PSG ) , a family of glycosphingolipids [31] , [32] , [33] , [34] , [35] , [36] . PSG are abundantly expressed in neurons and their direct interactions with all seven BoNTs have been characterized . Ganglioside-binding sites within each toxin have been proposed and further supported by mutagenesis and crystal structural studies [32] , [33] , [34] , [35] , [36] , [37] , [38] , [39] , [40] , [41] , [42] , [43] , [44] , [45] , [46] . On the functional level , it has been shown that blocking ganglioside synthesis using chemical inhibitors reduced the binding and entry of BoNT/A and B in cells [47] , [48] , [49] . Recently , knockout ( KO ) mice lacking the ability to synthesize PSG have been created . It has been shown that lacking PSG in these KO mice reduced the toxicity of all seven BoNTs at motor nerve terminals using an ex vivo phrenic nerve hemi-diaphragm preparation [42] , [43] , [46] , [50] . Furthermore , BoNT/A , B , E , and G failed to bind and enter hippocampal neurons cultured from PSG deficient mice and this defect can be restored using exogenous gangliosides [49] , [51] . Finally , mice lacking PSG showed decreased sensitivities to BoNT/A , B , C , and G in vivo [49] , [52] , [53] . In addition to gangliosides , accumulating evidence suggests that there are specific protein receptors for BoNTs and a double-receptor model has been proposed [29] , [54] . Previous studies have established two isoforms of synaptic vesicle membrane protein synaptotagmin ( Syt ) I and II , in conjunction with PSG , as the receptors for BoNT/B and G [35] , [46] , [49] , [55] , [56] , [57] , [58] . Co-crystal structure of BoNT/B bound to Syt II revealed that the toxin binds the membrane adjacent region of Syt [48] , [59] . This binding mechanism is shared by BoNT/G , which has the highest sequence similarity to BoNT/B among the seven BoNTs [40] , [41] , [46] , [58] . The protein receptor for BoNT/A and E was subsequently identified as the synaptic vesicle protein SV2 [51] , [60] , [61] . SV2 contains twelve transmembrane domains and one major luminal domain ( the fourth luminal domain , L4 ) [62] , [63] , [64] , [65] . In contrast to our detailed understanding of BoNT/B-Syt interactions , how BoNT/A and E recognize SV2 at the molecular level remains to be characterized . What have been shown are: 1 ) Binding of BoNT/A and E are mediated by SV2-L4; 2 ) BoNT/A can bind SV2-L4 , while there is no detectable binding of BoNT/E to recombinant SV2-L4; 3 ) all three mammalian isoforms of SV2 ( SV2A , B , and C ) can function as the receptor for BoNT/A , while BoNT/E likely only utilizes SV2A and SV2B; 4 ) Mutating a conserved N-linked glycosylation site within SV2-L4 ( N573Q in SV2A ) blocked the entry of BoNT/E and also reduced the entry of BoNT/A into neurons . In addition , it was suggested that BoNT/F , which has the highest sequence similarity to BoNT/E within the seven BoNT-HCRs , also uses SV2 as its receptor [43] , [45] . However , functional evidence is still lacking for the role of SV2 in mediating the binding and entry of BoNT/F into neurons . The remaining serotypes , BoNT/C and BoNT/D , share the highest sequence similarity to each other among the seven BoNTs [9] , [66] . Whether these two toxins share the same mode of receptor-recognition with the other five BoNTs remains unsolved . It has been suggested that BoNT/C and BoNT/D do not need protein receptors since treating rat brain synaptosomes with proteases and heating did not diminish toxin binding [53] . It was further suggested that BoNT/D binds phosphatidylethanolamine but not gangliosides , and lacking PSG did not reduce the toxicity of BoNT/D in mice [53] . On the other hand , recent studies have demonstrated that BoNT/D can bind gangliosides and the toxicity of BoNT/D is reduced at phrenic nerve hemi-diaphragm preparations from PSG deficient mice [42] . Here we established that BoNT/D uses SV2 as its protein receptor via a binding-mechanism distinct from BoNT/A and E . We further determined that gangliosides are essential for the binding and entry of BoNT/D into neurons and for its toxicity in vivo , thus extending the “double-receptor” model to this toxin and revealing how members of BoNTs converge onto a central theme yet also have their own individual receptor recognition strategies . Exocytosis of synaptic vesicles and subsequent endocytosis of vesicle components is a major membrane recycling event at presynaptic terminals – the target site for BoNTs [67] , [68] . Using the cleavage of Syb by BoNT/D as a functional readout for toxin entry , we found that stimulating vesicle exocytosis in cultured rat hippocampal neurons with high levels of potassium solution ( high K+ buffer ) increased Syb cleavage as compared to resting conditions ( Figure 1A ) . We next constructed the receptor binding domain of BoNT/D ( BoNT/D-HCR ) fused with a HA tag in order to directly assay the binding of toxins to cell surfaces , since there were no suitable antibodies available for BoNT/D detection . This recombinant BoNT/D-HCR is capable of competing with BoNT/D for binding receptors as it reduced the cleavage of Syb by BoNT/D ( Figure 1B ) . We found that high K+ buffer increased the binding of BoNT/D-HCR to neurons ( Figure 1C ) . Binding occurs mainly at presynaptic terminals as shown by high degrees of co-localization between BoNT/D-HCR and the presynaptic marker synapsin ( Figure 1C , overlay ) . Moreover , treating neurons with tetanus neurotoxin ( TeNT ) , which cleaves Syb and blocks synaptic vesicle exocytosis [11] , blocked the binding of BoNT/D-HCR to neurons ( Figure 1D ) . Together , these data suggest that BoNT/D enters neurons through recycling of synaptic vesicles . We next purified BoNT/D-HCR as a glutathione S-transferase ( GST ) fusion protein and used it to pull-down interacting proteins from rat brain detergent ( Triton X-100 ) extracts . Bound materials were subjected to immunoblot analysis using antibodies for major synaptic vesicle membrane proteins [68] , [69] . The HCRs of BoNT/A , E , B , as well as GST protein alone , were assayed in parallel as controls . BoNT/D-HCR pulled-down significant amounts of vesicle protein SV2 and low levels of Syt I , but not other vesicle proteins such as synaptophysin ( Syp ) or synaptogyrin I ( Syg ) , in a similar manner to the HCRs of BoNT/A and E ( Figure 2A ) . BoNT/A and E are known to use SV2 as their receptor and the low levels of bound Syt I might be due to the association of Syt I with SV2 as previously characterized [70] , [71] , [72] , [73] , [74] . Consistently , the HCR of BoNT/B , which uses Syt I/II as its receptors , pulled-down Syt I but not SV2 ( Figure 2A ) . These data suggest that BoNT/D-HCR can bind SV2 . SV2 shown in Figure 2A was detected using an antibody that recognizes all three mammalian SV2 isoforms . We noticed that the molecular weight of SV2 pulled-down by BoNT/D-HCR and BoNT/E-HCR appears to be different ( Figure 2A ) . Therefore , we further analyzed the bound materials using antibodies specific for each SV2 isoform ( Figure 2B ) . While BoNT/A-HCR pulled-down all three SV2 isoforms , BoNT/D-HCR and BoNT/E-HCR showed clear preferences: BoNT/D-HCR for SV2B ( which is of lower molecular weight than SV2A and C ) , and BoNT/E-HCR for SV2A , respectively . We note that although BoNT/E-HCR did not pull-down detectable levels of SV2B , we have previously demonstrated that SV2B can function as the receptor to mediate the binding and entry of BoNT/E in neurons [51] . The likely explanation for these apparently contradictory results is that the pull-down assay using detergent-solubilized materials may only preserve the strongest binding interactions , while the neuronal surface may provide an optimal environment for SV2-BoNT/E interactions . Similarly , the preference of BoNT/D-HCR for SV2B does not exclude other SV2 isoforms as its receptors on neuronal surfaces , but suggests that BoNT/D-HCR may have the highest binding affinity to SV2B under our assay conditions . In addition , we also carried out co-immunoprecipitation assays using a HA antibody to immunoprecipitate soluble BoNT/D-HCR incubated with brain detergent extracts ( Figure 2C ) . Immunoprecipitated materials were analyzed using antibodies against different synaptic vesicle proteins . SV2B is the only one co-immunoprecipitated with BoNT/D-HCR at significant levels ( Figure 2C ) ; further confirming BoNT/D-SV2 interactions . To determine whether SV2 plays a role for the binding and entry of BoNT/D , we used hippocampal neurons cultured from SV2A/B double KO mice as a cell model . We have previously found that hippocampal neurons mainly express two of the three SV2 isoforms – SV2A and SV2B , thus SV2A/B KO neurons can serve as a loss-of-function model [51] , [60] . We found that BoNT/D failed to enter SV2A/B KO neurons , as demonstrated by the lack of Syb cleavage ( Figure 3A ) . Furthermore , expressing SV2A , B or C in SV2A/B KO neurons via lentiviral infection restored the entry of BoNT/D and resulted in the cleavage of Syb ( Figure 3A ) . These results demonstrated that SV2 is essential for the functional entry of BoNT/D into neurons , and all three isoforms of SV2 can mediate the entry of BoNT/D . We next examined whether SV2 is required for the binding of BoNT/D to neurons . As shown in Figure 3B , BoNT/D-HCR failed to bind SV2A/B KO neurons . BoNT/B served as an internal control , which bound to both control and SV2A/B KO neurons , demonstrating that loss of BoNT/D-HCR binding is not due to any potential defects in the vesicle recycling process in SV2A/B KO neurons . Furthermore , binding of BoNT/D-HCR was also observed for a subpopulation of SV2A/B KO neurons that still express SV2C and bound BoNT/D-HCR largely co-localizes with endogenous SV2C ( Figure S1 ) . Together , these results indicate that SV2 likely mediates the binding of BoNT/D to neurons . SV2 has only one major extracellular domain ( luminal domain ) with significant length ( SV2-L4 , ∼130 amino acids , Figure 4A ) . We previously demonstrated that BoNT/A can bind recombinant SV2-L4 fragments directly [60] , while BoNT/E requires glycosylation at a particular site within the SV2-L4 domain [51] . We next carried out a series of studies to determine whether BoNT/D shares SV2-binding mechanisms with either BoNT/A or BoNT/E . In previous studies , we have constructed chimeric receptors containing SV2-L4 plus the transmembrane and cytoplasmic domains of low density lipoprotein receptor ( LDLR ) [51] . Once expressed in SV2A/B KO neurons , these chimeric receptors ( SV2-L4-LDLR ) were able to mediate the binding of BoNT/A ( Figure 4B , Figure S2 , [51] ) and BoNT/E [51] , but failed to mediate the binding of BoNT/D-HCR ( Figure 4B , Figure S2 ) . To address the concern that SV2-L4-LDLR does not localize to synaptic vesicles , we next inserted SV2A-L4 into the luminal domain of another multiple membrane spanning synaptic vesicle protein Syg . Once expressed in SV2A/B KO neurons , this chimeric protein ( Syg-SV2A-L4 ) mediated the entry of BoNT/A ( Figure 4C ) and BoNT/E ( Figure 4D ) into neurons at a comparable efficiency to endogenous SV2A expressed in control neurons , as indicated by the similar levels of SNAP-25 cleavage . In contrast , Syg-SV2A-L4 failed to mediate the entry of BoNT/D as shown by the lack of Syb cleavage ( Figures 4C–D ) . These data suggest that the SV2-L4 domain , expressed in chimeric receptors , can provide a binding site for BoNT/A and E , but it is not sufficient for BoNT/D . Because glycosylation at the third site within the SV2-L4 ( N573 in SV2A ) has been shown to be essential for the entry of BoNT/E and also can enhance the entry of BoNT/A at low toxin concentrations [51] , we next examined whether BoNT/D shares this requirement . Three mutant forms of SV2A that harbor point mutations at each N-linked glycosylation consensus sequence ( N498Q , N548Q , N573Q ) , respectively , were expressed in SV2A/B KO neurons . As we previous reported , N573Q mutation completely blocked the entry of BoNT/E and protected SNAP-25 ( Figure 4E ) . In contrast , none of the mutants blocked the entry of BoNT/D ( Figure 4E ) . Furthermore , N573Q did not affect the entry of BoNT/D when we reduce BoNT/D concentrations from 100 pM ( Figure 4E ) to 30 and 10 pM ( Figure 4F ) . As a control , we confirmed our previous finding that N573Q mutation reduced BoNT/A entry at low toxin concentrations ( 3 and 1 nM , Figure 4F ) . These data again suggest that BoNT/D has a SV2-binding mechanism distinct from BoNT/A and BoNT/E . In addition , these studies also demonstrated that the SV2A ( N573Q ) mutant is able to mediate the entry of toxins , providing strong evidence that mutating the third glycosylation site in SV2A does not affect the function and localization of SV2 , but rather specifically abolishes the receptor function for BoNT/E and reduces the binding affinity for BoNT/A . Besides L4 , SV2 has two other short luminal domains ( L1: ∼15 amino acids; L3: ∼20 amino acids , Figure 4A ) . To test whether these two minor domains play any roles for BoNT/D , we constructed two mutant forms of SV2A by deleting the middle portions of L1 and L3 ( residue 196-200 of L1 and 321-331 of L3 ) , respectively . Both mutants , when expressed in SV2 A/B KO neurons , were able to restore the entry of BoNT/D and BoNT/E ( Figure 4G ) , indicating these two short luminal domains are unlikely participants in providing the binding site for BoNT/D or BoNT/E . Whether SV2 can provide the binding site for BoNT/D on cell surfaces is a key question in establishing it as a receptor for BoNT/D . SV2 luminal domains are the only regions that can be transiently exposed to the outside of cells during vesicle recycling . The finding that LDLR- or Syg-based chimeric receptors failed to mediate the entry of BoNT/D did not exclude the L4 domain as the toxin binding site , especially considering that the L4 domain in SV2 is anchored to membranes through both N- and C-terminal transmembrane domains ( Figure 4A ) , yet these membrane adjacent regions are disrupted in chimeric receptors . Unfortunately , our attempts to include the transmembrane domains of SV2 in different chimeric receptors , as well as various mutations within the L4 domains and the L4 domain deletion all resulted in mis-folded proteins that are not expressed/trafficked in cells , suggesting a rigid requirement for a specific conformation within the L4 domain . In order to examine whether SV2 provided the binding site for BoNT/D , we have to find a way to present SV2 luminal domains onto cell surfaces in their native conformation . The solution comes from a new observation we made when examining the binding of BoNT/D-HCR to SV2A/B KO neurons transfected with different SV2 isoforms ( Figure 5A ) . SV2 normally resides on synaptic vesicles . As expected , transfecting SV2A or SV2B restored the binding of BoNT/D-HCR to SV2A/B KO neurons in a puncta pattern ( Figure 5A , upper and middle panels ) , suggesting that binding occurs at presynaptic terminals . To our surprise , binding of BoNT/D-HCR to SV2C-transfected neurons showed a continuous binding pattern along neuronal processes ( Figure 5A , lower panel ) . The likely explanation is that a significant portion of SV2C localizes onto plasma membranes and mediates the binding of BoNT/D-HCR to regions outside of synapses . To confirm the localization of SV2C to plasma membranes , we tested the binding of BoNT/A to SV2C-transfected SV2A/B KO neurons under a low temperature condition , which stops membrane trafficking and only allows the binding to occur at cell surfaces . Furthermore , surface-bound toxins were detected via immunostaining without permeabilizing cells . Under these assay conditions , we detected the binding of BoNT/A to SV2C-transfected neurons in a continuous pattern along neuronal processes ( Figure 5B , upper panel ) . BoNT/A is known to bind SV2C-L4 [60] , [61] , thus this result demonstrates that SV2C is located on cell surfaces with its luminal domains exposed to the outside of cells . In addition , we subsequently permeabilized these cells and detected synaptic marker Syb ( Figure 5B , overlay Syb/BoNT/A ) . We found that Syb distributed along BoNT/A-bound neuronal processes in a puncta pattern , indicating that these processes are axons harboring presynaptic terminals and also demonstrating that BoNT/A binding occurs at both presynaptic terminals and regions outside of synapses . Under the same assay conditions , we observed robust binding of BoNT/D-HCR to SV2C-transfected neurons ( Figure 5B , lower panel ) at both presynaptic terminals ( labeled by synapsin ) and regions outside of synapses , demonstrating that SV2C mediates the binding of BoNT/D-HCR to cell surfaces . This low temperature surface-binding assay allows us to examine toxin binding even in WT neurons . Using this assay , we found that the receptor for BoNT/B , Syt I , also has a significant portion localized on plasma membranes when over-expressed in rat neurons , as demonstrated by the surface binding of BoNT/B and the binding of Syt IN Ab that recognizes the N-terminus of Syt I luminal domain ( Figure 5C , upper panel ) . Under the same assay conditions , we did not detect the binding of BoNT/B or Syt IN Ab to SV2C-transfected neurons ( Figure 5C , lower panel ) . Consistently , BoNT/D-HCR binds to SV2C-transfected rat neurons ( Figure 5D , upper panel ) , but not to Syt I-transfected neurons ( Figure 5D , lower panel ) , demonstrating the specificity of BoNT/B and BoNT/D-HCR in recognizing their respective receptors under our assay conditions . Finally , we expressed SV2C in non-neuronal HEK293FT cells . Transfected cells were exposed to BoNT/D-HCR and immunostaining was first carried out without permeabilizing cells to detect the surface binding of BoNT/D-HCR . Cells were subsequently permeabilized to confirm the expression of SV2C using a polyclonal SV2C antibody . As shown in Figure 5E , expression of SV2C mediated the binding of BoNT/D-HCR to the surfaces of HEK293FT cells . Furthermore , the polyclonal SV2C antibody , which recognizes the N-terminal cytoplasmic domain of SV2C , failed to stain SV2C in transfected cells without permeabilizing cells ( Figure S3 ) , suggesting that SV2C maintains the correct membrane topology on the surface of HEK293FT cells . Together , the experiments described in this section demonstrate that SV2 functions as a receptor providing the binding site for BoNT/D on cell surfaces . We next determined whether BoNT/D requires gangliosides as co-receptors . We note that a previous study concluded that PSG are not required for BoNT/D binding and entry based on a KO mouse line lacking GM3 synthase , which only depletes a- and b- series , but not o-series PSG . O-series PSG are not abundant in WT neurons; however , it has been shown that their levels are significantly elevated in GM3 KO neurons [75] . Thus , we assessed the role of PSG using a different KO mouse line lacking the gene encoding GM2/GD2 synthase ( GS KO ) , an enzyme required for synthesis of all major PSG [76] . Using a well-established rapid-time-to-death assay , we examined the sensitivity of KO mice versus their wild type ( WT ) littermates to BoNT/D . The assay was conducted by injecting a large amount of toxins ( 105–106 mean lethal doses , LD50 ) into mice that resulted in death within 30 min to 1 hour . Within this range of toxin concentrations , the effective toxicity in vivo can be estimated based on how long the mice survive using a standard curve [57] , [77] . When injected with the same amount of BoNT/D , the KO mice survived significantly longer than WT mice ( Figure 6A ) . The effective toxicity in KO mice was reduced to only 10% of the level in WT mice ( Figure 6A ) , demonstrating that PSG are essential for the toxicity of BoNT/D in vivo . We next assayed whether reduced toxicity of BoNT/D in GS KO is due to the decrease of BoNT/D entry into neurons . Using hippocampal neurons cultured from GS KO mice as a cell model , we found that lacking gangliosides reduced the entry of BoNT/D into neurons as evidenced by the reduction of Syb cleavage ( Figure 6B ) . Furthermore , the functional entry of BoNT/D was restored by loading exogenous gangliosides into the cell membrane ( Figure 6B ) . The next question is whether PSG are required for the neuronal binding step of the toxin action . To directly examine this , we tested the binding of BoNT/D-HCR to hippocampal neurons cultured from GS KO mice and their WT littermates . Binding of BoNT/D-HCR was abolished in GS KO neurons ( Figure 6C , middle panel ) , and it was restored by loading exogenous gangliosides onto the cell membrane ( Figure 6C , right panel ) , demonstrating that gangliosides are required for the binding of BoNT/D to neurons . Together , these studies provided critical evidence at both animal and cellular levels for the conclusion that PSG are essential co-receptors for BoNT/D . Among the seven BoNTs , BoNT/C has the highest sequence similarity to BoNT/D , while BoNT/F has the highest sequence similarity to BoNT/E within the seven BoNT-HCRs [9] . We next assessed whether BoNT/C and BoNT/F share the same requirement for receptor-recognition with BoNT/D or BoNT/E , taking advantage of available PSG and SV2 KO mouse lines . We found that lacking PSG abolished the functional entry of BoNT/C , as shown by the lack of cleavage of SNAP-25 ( Figure 7A ) . Entry was restored by adding exogenous gangliosides to cell membranes ( Figure 7A ) . Similarly , lacking PSG reduced the entry of BoNT/F , as evidenced by the decreased cleavage of Syb ( Figure 7B ) . Loading gangliosides into cell membranes restored the entry of BoNT/F ( Figure 7B ) . These results are consistent with previous studies demonstrating that gangliosides can bind BoNT/C and F , and are essential for their toxicity in mice and in phrenic nerve hemi-diaphragm preparations [43] , [45] , [53] . Our studies provided further cellular evidence to establish gangliosides as a shared co-receptor for all seven BoNTs . Lacking SV2 , on the other hand , did not significantly reduce the cleavage of SNAP-25 and Syx by BoNT/C in cultured hippocampal neurons as compared to the control neurons that still express SV2A ( Figure 7C ) , suggesting that BoNT/C does not share the same protein receptor requirement as BoNT/D in hippocampal neurons . We also found that the absence of SV2 did not reduce the sensitivity of hippocampal neurons to BoNT/F compared to control neurons that still express SV2A , as evidenced by the similar levels of Syb cleavage at all toxin concentrations examined ( Figure 7D , E ) . Although many questions remain to be determined such as whether other SV2 isoforms play a role in BoNT/F entry , whether SV2 mediates BoNT/F entry into other types of neurons and whether other proteins can compensate the loss of SV2 for BoNT/F , it is clear that BoNT/F does not share the same receptor-binding mechanism with BoNT/E in hippocampal neurons . To identify the receptor for BoNT/D , we first determined that BoNT/D uses synaptic vesicle recycling to enter neurons . Using BoNT/D-HCR as bait , we identified synaptic vesicle protein SV2 as the toxin binding protein . Utilizing hippocampal neurons cultured from SV2A/B KO mice , we demonstrated that SV2 is essential for the binding and entry of BoNT/D into neurons and all three isoforms of SV2 can mediate the binding and entry of BoNT/D . Our key finding is that localization of over-expressed SV2C onto plasma membranes mediated the binding of BoNT/D to cell surfaces in both neurons and HEK293 cells , suggesting that the luminal domains of SV2 provide the binding site for BoNT/D and demonstrating that other synaptic vesicle proteins are not required . Together , these data establish SV2 as the protein receptor for BoNT/D . Interestingly , BoNT/D appears to have a SV2-recognition strategy distinct from BoNT/A and BoNT/E . First , SV2-L4 domain expressed in LDLR-based or Syg-based chimeric proteins can function as the receptor for BoNT/A and E , but failed to mediate the entry of BoNT/D . Second , N573Q mutation that abolished a glycosylation site within the SV2A-L4 domain blocked the entry of BoNT/E and also reduced the entry of BoNT/A , but has no significant effect on the entry of BoNT/D . These data suggest that BoNT/D has a distinct SV2-binding mechanism that has yet to be understood . In fact , we still do not understand whether BoNT/A and BoNT/E share similar mechanisms recognizing SV2 at the molecular level . The finding that BoNT/D also uses SV2 as a receptor , together with recent progress solving the crystal structures of BoNTs and BoNT-HCRs [39] , [42] , [44] , [78] , [79] , provided an opportunity for comparative studies in order to understand the molecular and structural basis for seemingly diverse SV2-binding mechanisms utilized by different BoNTs . Among the seven BoNTs , BoNT/B and G display the highest similarity and they share Syt I/II as their receptors [9] , [58] . BoNT/A and E are fairly close to each other and they share SV2 as the receptor [9] . The finding that BoNT/D also uses SV2 as the receptor , on the other hand , may not be explained by sequence similarity especially considering that the binding mechanism appears to be different from BoNT/A and E . This surprising convergence may suggest that SV2 possesses certain characteristics/functions that make it an attractive receptor candidate . One possibility is that the complex glycan structure in SV2 may facilitate the binding of toxins . It is also interesting to note that Syt is known to function as the Ca2+ sensor for triggering vesicle release and also plays an important role for maintaining the rate of synaptic vesicle endocytosis [80] , [81] , [82] , [83] , [84] . Although the function of SV2 has not been established , recent studies showed that lacking SV2 results in elevated Ca2+ levels in the presynaptic terminals and also reduces the rate of compensatory membrane retrieval after synaptic vesicle release [85] . Moreover , it has been shown that SV2 associates with Syt and may regulate the endocytosis of Syt [70] , [74] , [82] . The roles of Syt and SV2 in Ca2+ signaling and compensatory endocytosis , two critical functions in a vesicle cycle , may provide strategic reasons for toxins to exploit them as receptors to target recycling synaptic vesicles . Using BoNT/A as a specific SV2 luminal domain probe , we showed that significant portions of SV2C localize onto plasma membranes when over-expressed in neurons . This is likely due to over-expression of exogenous proteins since it was not observed for endogenous SV2C ( Figure S1 ) . This phenomenon was also seen for Syt I . It has been proposed that the plasma membrane is a default destination for Syt I and its sorting to synaptic vesicles requires endocytotic sorting adaptors that could be overwhelmed by over-expressed Syt I [86] . It remains to be seen whether a similar mechanism causes the localization of over-expressed SV2C on plasma membranes . In addition to protein receptors , we also examined the role of PSG for the binding and entry of BoNT/D in neurons utilizing GS KO mice . We showed that these ganglioside deficient mice are less sensitive to BoNT/D in vivo . We further showed that BoNT/D cannot bind and enter neurons cultured from GS KO mice; binding and entry can be restored by loading exogenous gangliosides to cell membranes . We extended these studies to BoNT/C and BoNT/F , and demonstrated that PSG are required for the functional entry of both toxins in cultured neurons . Together , these studies contribute to the growing body of evidence that PSG are a shared binding platform for all seven BoNTs . Finally , we note that a natural chimeric toxin composed of the light chain of BoNT/C and the receptor binding domain of BoNT/D has been tested in patients [87] , [88] . This toxin is designated as BoNT/C-D , but has also been marketed as a subtype of BoNT/C [9] , [89] . Its receptor binding domain is identical to BoNT/D . Our studies indicate that this toxin targets neurons by recognizing SV2 and gangliosides . This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . The protocol was approved by the Standing Committee on Animals of Harvard Medical School ( Permit Number: 04619 ) . All efforts were made to minimize suffering of animals . Mouse monoclonal antibodies for Syb ( Cl 69 . 1 ) , Syt I ( Syt IN Ab: Cl 604 . 4; Syt I cytoplasmic domain Cl 41 . 1 ) , SV2 ( pan-SV2 ) , synaptophysin ( Cl 7 . 2 ) , syntaxin ( HPC-1 ) and SNAP-25 ( Cl 71 . 2 ) were generously provided by E . Chapman ( Madison , WI ) . Rabbit polyclonal antibodies for BoNT/A and BoNT/B were described previously [57] , [60] . Rabbit polyclonal antibodies for SV2A , B , C , and Syg were generously provided by R . Janz ( Houston , TX ) and were described previously [65] , [90] , [91] . The following antibodies were purchased from indicated vendors: mouse monoclonal anti-HA ( 16B12 , Covance ) ; rabbit polyclonal anti-synapsin and guinea pig anti-vesicular glutamate transporter I ( vGlut-I , Millipore ) ; chicken polyclonal anti-GFP and mouse monoclonal anti-actin ( Abcam ) . GM2/GD2 synthase KO mice have been previously described [76] and were obtained from the Consortium for Functional Glycomics ( Grant number: GM62116 ) . The SV2A/B knockout mice were described previously [90] and were generously provided by R . Janz . Bovine brain gangliosides were purchased from Matreya LLC ( PA ) . TeNT was purchased from List Biological Lab ( CA ) . BoNT/A ( Hall-A ) , BoNT/B ( Okra ) , BoNT/C ( Brazil ) , BoNT/D ( D1873 ) , BoNT/E ( Alaska ) and BoNT/F ( Langeland ) were purified in E . Johnson's lab from indicated strains . Rat SV2A/B/C and Syg cDNAs were described previously [62] , [63] , [64] , [65] and were generously provided by R . Janz . Rat Syt I cDNA were generously provided by T . C . Sudhof ( Palo Alto , CA ) . Lox-Syn-Syn lentivirus vector [92] was used for all constructs expressing exogenous proteins in neurons . This vector contains two separate neuronal-specific promoters ( synapsin promoter ) . One promoter controls expression of indicated proteins and the other controls expression of EGFP . LDLR-based chimeric receptors were generated by fusing the L4 domains of each SV2 isoform ( residues 468–595 in SV2A , 410–539 in SV2B , 453–580 in SV2C ) to the N-terminus of a fragment encoding the transmembrane and cytosolic domain of human LDLR-2 ( residues 788-860 ) as described previously [51] . Syg-based chimeric receptor was constructed by inserting the SV2A-L4 domain between residue 140 ( L ) and 141 ( N ) within the second luminal domain of Syg . Deletion mutants SV2A-ΔL1 and SV2A-ΔL3 were generated by replacing residues 196-200 of L1 and 321–331 of L3 with a peptide sequence derived from the first eleven amino acids of rat Syt I [93] . This sequence can be recognized by Syt IN-Ab , which we found only recognizes rat but not mouse Syt I [51] , thus serving as a tag . Point mutations at N-glycosylation sites of SV2A have been described previously [51] . The cDNA encoding the HCRs of BoNT/A and BoNT/B were generously provided by J . Barbieri ( Milwaukee , WI ) and were previously described [73] . The cDNA encoding the HCRs of BoNT/D ( residues 859–1276 , GenBank: CAA38175 . 1 ) and BoNT/E ( residues 820–1252 GenBank: X62683 . 1 ) were synthesized by Geneart Inc . ( Germany ) with codon optimized for E . Coli expression . They were subcloned into pGEX4T vector for expression as GST fusion proteins . In addition , BoNT/D-HCR was also subcloned into pET-28 vector , with a HA-tag ( YPYDVPDYA ) fused to its N-terminus . This HA-tagged BoNT/D-HCR was purified as N-terminal tagged His6-fusion proteins . Both GST-fusion and His6-fusion proteins were purified as previously described [94] , [95] , except that the induction conditions were changed to 16°C overnight with 0 . 25 mM IPTG . Rat brain detergent extracts were prepared by homogenizing one fresh dissected adult rat brain in 15 ml 320 mM sucrose buffer , followed by a centrifugation at 5000 rpm for 2 min at 4°C in a Sorvall SS-34 rotor . Supernatants were collected and centrifuged at 11 , 000 rpm for 12 min using the same rotor . The pellet was collected and solubilized for 30 min in 15 ml Tris-buffered saline ( TBS: 20 mM Tris , 150 mM NaCl ) plus 2% of Triton X-100 and a cocktail of protease inhibitors ( Roche , CA ) . Samples were subsequently centrifuged at 17 , 000 rpm for 20 min in a Sorvall SS-34 rotor to remove the insoluble materials . The final brain detergent extracts yielded ∼2 mg/ml proteins . GST pull-down assays were carried out using 500 µg GST fusion proteins or GST protein immobilized on glutathione-Sepharose beads , mixed with 1 . 5 ml rat brain detergent extracts for 1 hr at 4°C . Beads were washed three times with the washing buffer ( TBS plus 0 . 5% Triton X-100 ) . Ten percent of bound proteins were subjected to SDS-PAGE and immunoblot analysis following standard western blot procedures using the enhanced chemiluminescence ( ECL ) method ( Pierce ) . ‘Input’ corresponds to 0 . 5% of total brain extracts incubated with each HCR protein . Co-immunoprecipitation experiments were carried out by first incubating 100 nM HA-tagged BoNT/D-HCR with 0 . 5 ml rat brain detergent extracts for 1 hr at 4°C , and then after the addition of monoclonal anti-HA antibody ( 4 µl ) incubating for a further 1 hr . Protein G Fast Flow beads ( 50 µl , GE Bioscience ) were then added and incubated for additional 1 hr . The beads were washed three times in the washing buffer ( TBS plus 0 . 5% Triton X-100 ) . Bound proteins were analyzed by immunoblot analysis . Rat hippocampal neurons were prepared from E18-19 embryos . Mouse hippocampal neurons were prepared from P1 mice . Dissected hippocampi were dissociated with papain following manufacture instructions ( Worthington Biochemical , NJ ) . Cells were plated on poly-D-lysine coated glass coverslips and cultured in Neurobasal medium supplemented with B-27 ( 2% ) and Glutamax ( Invitrogen ) . Experiments were carried out generally using DIV ( days in vitro ) 12–18 neurons . Transfection of neurons was carried out using Lipofectamine 2000 ( Invitrogen ) at DIV5 . Lentiviral particles were produced by HEK293FT ( Invitrogen ) cells co-transfected with the virus packaging vectors ( VSV-G and Δ8 . 9 ) as described previously [92] . Viruses were added to neurons at DIV5 . The control buffer ( PBS ) used in Figure 1A contains ( mM: NaCl 140 , KCl 3 , KH2PO4 1 . 5 , Na2HPO4 8 , MgCl2 0 . 5 ) . High K+ buffer is the same as the control buffer but adjusted to 56 mM KCl and 87 mM NaCl plus 1 mM CaCl2 . In general , the binding of BoNT/D-HCR to neurons was assayed by incubating neurons with 80 nM BoNT/D-HCR for 5 min in high K+ buffers at 37°C . Cells were washed three times . Immunostaining was carried out by fixing cells with 4% paraformaldehyde , permeabilized with 0 . 3% Triton in PBS solution , and incubated with indicated primary antibodies for 1 hr at room temperature , followed by the incubation with secondary antibodies conjugated with Alexa dyes ( Invitrogen , CA ) for 1 hr at room temperature . Images were collected using a Leica TCS SP confocal microscope using a 40x oil objective . Surface binding assays described in Figure 5B–D were carried out by first incubating neurons in cold media ( 4°C , 5 min ) , and then exposing them to indicated reagents in cold media on ice for 10 min . Cells were washed and fixed . Immunostaining was first carried out without the permeabilization step to detect the surface binding of BoNT/D-HCR , BoNT/A , BoNT/B , or Syt IN-Ab . Cells were subsequently permeabilized and immunostaining was carried out for the additional indicated intracellular proteins . In Figure 5E , HEK293FT cells were growing on poly-D-lysine coated coverslips and were transfected with full-length SV2C in pCMV5 vector using Lipofectamine 2000 when cells reached 70–80% confluence . Cells were exposed to BoNT/D-HCR ( 80 nM ) for 30 min at 37°C 48 hrs after the transfection . Cells were washed and fixed . Immunostaining was first carried out without permeabilization to detect BoNT/D-HCR . Subsequently , cells were permeabilized to detect SV2C using a polyclonal SV2C antibody . We note that the binding of BoNT/D-HCR was observed mostly in cells with round cell shapes – a morphology that becomes more prominent when cells reach complete confluence . Functional entry of BoNTs into neurons was assayed by examining the cleavage of their substrate proteins in neurons . In general , neurons were exposed to indicated toxins for 5 min in high K+ buffers at 37°C . Neurons were washed , further incubated in toxin-free media for an additional 6 hrs , and lysed in the cell lysate buffer ( PBS with 1% Triton X-100 , 0 . 05% SDS and protease inhibitor cocktail ( Roche , CA ) , 100 µl per one well of 24-well plates ) . Lysates were centrifuged for 10 min using a microcentrifuge at 4°C , and the supernatants were assayed by immunoblot analysis . A rapid-time-to-death assay was utilized to assess the toxicity of BoNTs in GS KO mice , following a well-established protocol as previously described [57] , [77] . Briefly , the WT and KO littermates were injected with the same amount of toxins intravenously ( lateral tail vein ) , and their time-to-death was recorded . The apparent intraperitoneal LD50/ml of toxins in each mouse ( effective toxicity ) was determined using a standard curve as previously described [57] , [77] . The stock of gangliosides was prepared by dissolving mixed bovine brain gangliosides in Chloroform:Methanol ( 2:1 ) solution . They were dried in glass tubes using nitrogen gas , resuspended in Neurobasal media at 1 mg/ml , and added to culture media at 250 µg/ml concentrations for 12 hrs to load into cell membranes .
BoNTs are a family of seven bacterial toxins ( BoNT/A-G ) . Among the seven BoNTs , whether BoNT/D uses the same entry pathways and similar receptor-binding strategies as other BoNTs is not known . Previous studies have suggested that BoNT/D does not need a protein receptor nor ganglioside co-receptor , in contrast to all other BoNTs . Here we demonstrate that BoNT/D uses synaptic vesicle protein SV2 as its protein receptor and gangliosides as co-receptor , thus supporting the “double-receptor” model as a central theme for this class of toxins . Furthermore , we found that BoNT/D utilizes a SV2 binding mechanism distinct from BoNT/A and BoNT/E , two other BoNTs that use SV2 as receptors . This indicates that different BoNTs can develop their distinct mechanisms to target a common receptor protein .