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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 .

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